Incrementality

<p>Here's a major challenge for modern marketing teams: how do you measure the impact of an advertising campaign beyond the digital ad platform or social media channels where the campaign is taking place?</p>
<p>The problem is, "<em>As digital ad platforms and social media channels do not have access to data outside their environment, they all use the last-touch attribution method to show the effectiveness of an advertising campaign."</em></p>
<p>The <a href="https://www.lifesight.io/blog/prepare-for-cookieless-world">cookieless</a> and privacy-driven digital space makes it even worse. With the need for a digital audience footprint, tracking the real impact of marketing efforts requires going beyond the vanity metrics.</p>
<p>That's where incrementality testing in marketing shows the true impact of marketing campaigns, more so when they are multi-channel.</p>
<p>Incrementality measurement identifies the channels that lead to leaky revenue and the ROI-generating ones that need to be optimized.</p>
<p>Let's see how incrementality tests quantify the impact of various marketing activities without being weighed down by continuous changes in the analytics field.</p>
<h2>What is incrementality testing?</h2>
<p>Incrementality testing is the statistical approach to assessing the impact of a marketing activity or campaign. It shows whether introducing a new campaign had a positive, negative, or null effect and by how much. This quantification helps shape marketing efforts by bringing together profitable campaigns and discarding the ones with negligible results.</p>
<p>Suppose your marketing team is launching a new product and deciding whether to invest in LinkedIn ads and other marketing channels. The incremental testing can help decide if introducing this campaign impacts the desired outcome.</p>
<p>Here are five approaches to marketing incremental testing that would help evaluate the impact.&nbsp;</p>
<h3>Types of incrementality testing</h3>
<br/>
<h4><strong>Holdout Testing</strong></h4>
<p>Holdout testing involves splitting the target audience into two groups: one exposed to the campaign and the other not. You determine the campaign's impact by comparing the outcomes between the two groups.</p>
<p>To build from the previous example, the team randomly selects a portion of their target audience to see the LinkedIn Ads and leaves another portion. Then, they compare the conversion rates between these groups. If the audience exposed shows better impact, the campaign is retained and optimized further.</p>
<h4><strong>Matched Market Testing</strong></h4>
<p>Matched market testing involves audience groups having the same external environment. This is the same as holdout testing but with similar market or audience conditions. This enables the marketing teams to account for external factors that could influence the results.</p>
<p>For instance, you target two departments within the same organization &ndash; marketing and finance. This way, you can account for department-specific variables, such as the role and job function of the employees. So, while they have a similar environment with comparable demographics, different job roles will impact the response to LinkedIn Ads. This allows users to compare results and assess the campaign's impact while controlling for variations.</p>
<h4><strong>Multi-Variate Testing</strong></h4>
<p>Multi-variate testing is a type of incrementality testing that involves testing multiple variables simultaneously to assess their combined impact. It's useful for evaluating how different campaign elements, like ad copy and visuals, affect the desired outcome.</p>
<p>For instance, the team runs LinkedIn Ads with variations in ad copy, visuals, and targeting options and analyzes the combined effect.</p>
<h4><strong>Geo-Testing</strong></h4>
<p>Geo-testing focuses on specific locations. You can evaluate the regional impact by exposing some geographic areas to the campaign and leaving others out. For instance, the team selects certain regions to run LinkedIn Ads and leaves others. This allows them to gauge whether the location has any considerable effect.</p>
<h4><strong>Time-Based Testing</strong></h4>
<p>Time-based testing assesses the impact of the campaign over different time periods. It helps understand how seasonality, day of the week, or time of day affect the campaign's performance.</p>
<p>For instance, the team runs LinkedIn Ads at different times, such as weekdays vs. weekends and mornings vs. evenings. Then, they analyze which timeframes give better results.</p>
<p><a href="https://www.linkedin.com/posts/zachary-cascalho-cox-249713b4_when-we-increase-media-investment-on-activity-7124541667193356288-EvZ-?utm_source=share&amp;utm_medium=member_desktop">Zachary Cascalho Cox</a>, a growth expert at Google, suggests three ways brands can think about proving out their media channels:</p>
<ol>
<li>Geo Testing</li>
<li>Account for confounding variables</li>
<li>Testing for a long enough Time</li>
</ol>

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<p><strong><em>Read more: </em></strong><a href="https://www.lifesight.io/blog/incrementality-testing-pros-and-cons"><em>The pros and cons of incrementality testing</em></a></p>
<h2>How do you calculate incrementality?</h2>
<p>While you can deploy any of the marketing incrementality measurement approaches discussed above, here are some core concepts you must understand before starting:</p>
<ul>
<li>Test group: The group of the audience exposed to the campaign.</li>
<li>Control group: The group of the audience set aside and not exposed to the campaign.&nbsp;</li>
<li>Incremental lift: The percentage difference between the test and control groups.</li>
<li>Key performance indicators (KPIs): These metrics assess the campaign's impact. These are the outcomes that the team is aiming for.</li>
</ul>
<h3>How to measure incrementality: The formula</h3>
<p>For incremental testing, you split the audience into two groups: control and test.</p>
<p>As per the thumb rule, the control group is more than 10% of the test group.</p>
<p>Then, you expose the campaign to the test group and isolate the control group.</p>
<p>You measure the KPIs and analyze their results.</p>
<p>The formula to calculate incrementality is:</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/incrementality_formula_bd78e77f12.webp) 

<p>For instance, you want to assess the incremental lift of a LinkedIn Ads campaign targeting two departments: marketing and finance. You run the campaign and measure the click-through rate (CTR) as the KPI. After the campaign, the results are as follows:</p>
<ul>
<li>Test group (Marketing): CTR = 8%</li>
<li>Control group (Finance): CTR = 3%</li>
</ul>
<p>This shows the positive results of the campaign. Now, using the formula:</p>
<p>Incrementality = ((8 - 3) / 8) * 100 = (5 / 8) * 100 = 62.5%</p>
<p>This shows that over 60% of the audience clicked because they saw the Ad.</p>
<h2>How do you measure the impact of incrementality testing?</h2>
<p>Once you calculate the incrementality of marketing, the next step is to measure its impact. The three metrics to evaluate incrementality testing impact are:</p>
<h3>Lift</h3>
<p>Lift measures the cumulative effect of the campaign on the desired KPI. It shows how much the campaign performs better by quantifying the results.</p>
<p>Case in point, in the previous example, with the CTR of the test group at 8% and of the control group at 3%, the lift would be calculated as:</p>
<p>Lift = (CTR in Test Group - CTR in Control Group) / CTR in Control Group Lift = (8% - 3%) / 3% = 166.67%</p>
<p>The 166.67% lift shows that the campaign had a 166.67% better CTR in the test group over the control group.</p>
<h3>ROI</h3>
<p>ROI depicts the campaign's profitability by comparing the campaigns' costs and profits. It calculates the net difference and assesses whether the campaign generated a positive return.</p>
<p>Suppose the LinkedIn Ads campaign for your new product launch costs $10,000. It resulted in 50 new customers with an average customer value of $200 each.</p>
<p>The total revenue generated is $10,000 (50 customers * $200/customer).</p>
<p>The ROI would be:</p>
<p>ROI = (Revenue - Cost) / Cost ROI = ($10,000 - $10,000) / $10,000 = 0</p>
<p>Zero ROI shows that the campaign broke even. It generated revenue equal to its cost, which is why the campaign was not profitable.</p>
<h3>Net Incremental Revenue (NIR)</h3>
<p>Net incremental revenue is the additional revenue generated by the test group compared to the control group. It accounts for the revenue increase attributable to the campaign.</p>
<p>For instance, if the total revenue generated by the test group was $20,000 and the control group was $10,000, then NIR would be:</p>
<p>NIR = Revenue in Test Group - Revenue in Control Group = $20,000 - $10,000 = $10,000</p>
<p>This $10,000 represents the total actual incremental revenue generated by the campaign. It is the revenue directly attributable to the campaign.</p>
<p>So your next question may be: what's the difference between an incrementality test and attribution? Are they the same or different?</p>
<h2>Incrementality testing vs. Multi-touch attribution</h2>
<p>Incrementality Testing focuses on understanding the direct impact of a specific marketing activity by isolating it and measuring the changes in desired outcomes. It involves comparing a test group exposed to the campaign to a control group that is not. This approach answers the fundamental question: Did the marketing campaign cause an increase in sales or conversions? Incrementality testing is particularly valuable in a privacy-first world, as it does not rely on individual user-level data but assesses group-level data to understand the impact of marketing activities.</p>
<p>On the other hand, MTA is designed to understand the contribution of each marketing touchpoint throughout a customer's journey. It considers multiple consumer interactions with a brand across various channels before making a purchase. MTA models, such as linear, time-decay, or position-based, aim to assign proportional credit to these different channels and touchpoints, providing a more comprehensive view of what drives sales and conversions.</p>
<p><a href="https://www.linkedin.com/posts/nbran_talked-to-the-cmo-of-a-company-with-over-activity-7122580919151902723-IyfY?utm_source=share&amp;utm_medium=member_desktop">Nate Branscome</a>, a GTM and attribution expert, recommends three strategies for CMOs:</p>
<ol>
<li>Implementing a multi-touch or machine learning attribution model to identify which touchpoints are contributing most.</li>
<li>Using incrementality testing to confirm what you see in the attribution model.&nbsp;</li>
<li>Aligning sales and marketing by notifying sales of the warmest accounts</li>
</ol>

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<p>While the core focus of incrementality testing is evaluating campaign performance, it can be applied in different scenarios to assess the effectiveness of marketing efforts. So let's look at some of the most common scenarios for applying incrementality testing.</p>
<h2>Common scenarios for applying incrementality testing</h2>
<br/>
<h3>1. Paid advertising campaigns</h3>
<p>Marketing incrementality evaluates the effectiveness of paid ads by assessing their performance in test and control groups.</p>
<p>For instance, an e-commerce retailer runs two identical campaigns in different regions. In one region, they increase their ad spending, while they keep it the same in the other. By comparing the sales growth between the two regions, they understand if the increased ad spending is driving additional sales.</p>
<h3>2. Email marketing initiatives</h3>
<p>Businesses can assess if investing in an additional email marketing campaign is worth it or not as incrementality tests segregate the audience into test and control groups and compare the impact.</p>
<p>Suppose an e-commerce website sends out promotional emails with discounts to only one segment of its customer base while excluding another segment. Then, by analyzing the sales data from both groups, they can extract insights to determine if the emails helped in sales.</p>
<h3>3. Promotions and discounts</h3>
<p>Incrementality testing helps retailers and e-commerce platforms assess if promotions and discounts add actual value for their customers or just act as a nudge to boost sales.</p>
<p>The same store runs a "Buy One Get One Free" promotion offer for a week and then stops it for another week. By comparing the sales during these two weeks, they can determine if the promotion resulted in any significant increase in sales.</p>
<h3>4. Loyalty programs</h3>
<p>Incrementality testing helps businesses assess the prospect of loyalty programs by helping them segregate between the core reasons for recurring orders.</p>
<p>If your brand has a loyalty program where members earn reward points for every purchase, you can use incrementality testing marketing to compare the spending of loyalty program members to non-members to see if the program is encouraging customers to purchase more often.</p>
<h3>5. New product launches</h3>
<p>Incrementality testing is useful for brands launching new products to reveal if the reason for brand awareness is the new marketing campaigns or organic interest.</p>
<p>Say a phone company introduces a new model and runs marketing campaigns. They compare the sales of the new model with a similar older model (historical data) to find out if the marketing efforts for the new product launch are driving incremental sales.</p>
<h2>Why should brands measure incrementality?</h2>
<p>Now that we better understand how incrementality helps to overcome the challenges of attribution, here are three core reasons why your next campaign must invest in incrementality testing.</p>
<h3>Prove sales impact</h3>
<p>Incrementality testing allows you to prove the direct impact of a campaign on desired KPIs. For instance, if an e-commerce company launches a campaign, it can compare clicks to attribute the results. As a result, they can tie back these results to prove the campaign's impact on sales.</p>
<h3>Measure ads accurately</h3>
<p>Brands run multiple advertising campaigns at the same time across various platforms. Incrementality testing helps distinguish between the campaigns that bring desired outcomes and those that don't.&nbsp;</p>
<p>For example, if a retail brand runs both Google Ads, TikTok Ads, and Facebook Ads, incrementality testing helps to identify the better platform.</p>
<h2>Incrementality measurement: A data-driven approach to justify advertising investment&nbsp;</h2>
<p>In this economy of "do more with less," marketing teams must explain how the money they spend on ads is being used.</p>
<p><a href="https://www.lifesight.io/incrementality-testing">Incrementality testing</a> becomes their advocate and helps prove ROI with specific data. For instance, an e-commerce retailer can use marketing incrementality testing to determine the effect of a paid social media advertising campaign on online sales vs organic search.</p>
<p>Overall, incrementality helps you understand which campaigns give momentum to the core success metrics.</p>
<h2>What channels make sense for measuring incrementality testing?</h2>
<p>You must consider the following factors to choose channels for testing:</p>
<ul>
<li><strong>Segmentation</strong> - Since increment testing relies on creating test and control groups, any chosen channel must allow you to segment your audience.</li>
<li><strong>Measurable</strong> - As incrementality relies on assessing the impact on KPIs, the chosen platform must provide measurable metrics. For instance, billboards don't provide any significant metrics to track.</li>
</ul>
<p>Based on these two factors, some of the channels that are relevant for incrementality testing are:</p>
<ul>
<li>Email Marketing,</li>
<li>Paid Media, and</li>
<li>Mobile Push Notifications.</li>
</ul>
<p>These channels become a good starting point for incrementality testing.</p>
<h2>The common challenges of incrementality testing</h2>
<p>While incrementality offers insights to optimize marketing spend, the top 3 common challenges that can give misleading results are:</p>
<p><strong>Seasonality</strong></p>
<p>Seasonality refers to fluctuations in consumer behavior and data caused due to changes in external factors, such as holidays or weather changes. To address seasonality, one approach is to compare the test results with historical data from the same period in previous years. Alternatively, it is advisable to avoid testing during holidays or other seasonal impacts.</p>
<p><strong>Outliers and overlapping audiences</strong></p>
<p>Outliers are data points that deviate from the average.</p>
<p><strong>A case in point:</strong> Where an average order value is $200, getting a $4000 order will be an outliner.</p>
<p>On the other hand, overlapping audiences refer to individuals who belong to multiple test and control groups which leads to confusion in data interpretation.</p>
<p>To overcome these, use statistical methods like trimming or robust regression to reduce the impact of outliers. For overlapping audiences, carefully segment and target groups or use exclusion lists.</p>
<p><strong>Testing duration and segment size</strong></p>
<p>An inadequate testing duration or small segment size can give inconclusive or unreliable results. Relying on shorter tests and small segments will not provide significant data or long-term insights.</p>
<p>To get reliable findings, ensure the testing duration aligns with the expected customer behavior cycle, business cycle, or industry norms.</p>
<p>Additionally, aim for a significant sample size where the control group is at least 10-20% of the test group.</p>
<h2>Overcome privacy-first regulatory challenges with incrementality testing</h2>
<p>Incrementality testing doesn't rely on third-party cookies or demand individual user data. As a result, it becomes a valuable strategy to address the complexities of privacy-first regulations while assessing the performance and impact of your marketing campaigns. Now that data privacy is at its peak, it rises as an ethical solution to track and analyze user behavior while adhering to regulations.</p>
<h2>FAQs</h2>
<h3><strong>1. What's the difference between incrementality testing and A/B testing?</strong></h3>
<p>In A/B testing, the sample audience is split into two or more groups to test different campaign variations. It is used to understand which variation performs better based on predefined metrics like click-through rate.</p>
<p>An incremental test measures the incremental value of a marketing campaign. For example, conversions that resulted from a specific media strategy being tested.</p>
<h3><strong>2. Incrementality testing vs. marketing mix modeling (MMM) vs multi-touch attribution: What is better?</strong></h3>
<p>When choosing between incrementality testing, marketing mix modeling (MMM), and multi-touch attribution (MTA), the decision depends on your specific marketing goals and the insights you seek.</p>
<p>Each method has its strengths and is suited to different aspects of marketing analysis. Incrementality testing is vital for causal insights into the effectiveness of specific campaigns, MMM is best for strategic planning and budget allocation, and MTA excels in providing detailed insights into the customer journey and touchpoint effectiveness.</p>

A Beginner’s Guide to Incrementality Testing.webp

Measure the true impact of marketing campaigns with incrementality testing. Learn types, calculation methods, and apply it to scenarios for optimized results. Discover the channels and metrics that matter, overcoming challenges, and navigating privacy-first regulations. Elevate your advertising investment with a data-driven approach.

A Beginner’s Guide to Incrementality Testing

Privacy

<p>Marketing mix modeling or MMM has been around for more than 50 years now. In a world where marketing and advertising were solely offline based, and there were no tools to tell marketers which of their campaigns were working on which channels, MMM came as a savior.</p>
<p>However, it couldn't rise to popularity because of its complexity then. Adding to that, the advent of digital marketing introduced a whole new world where third-party cookies ruled the boardroom conversations between marketing leaders.</p>
<p>But now, MMM is supposedly in its 2.0 version with customers shifting to an omnichannel behavior, cookies on the verge of extinction, and the immense potential unlocked by AI and machine learning.</p>
<p>Rohit Maheswaran, the co-founder and CPO of Lifesight, recently sat down for a podcast to discuss how MMM unlocks scale and incrementality for brands and how can they optimize media buying in a privacy-first era. This blog explores the ideas discussed on the <a href="https://martechpod.com/episode/how-marketing-mix-modeling-unlocks-scale-incrementality-rohit-maheswaran-lifesight/">MarTech Podcast</a>.</p>
<h2>The Resurgence of MMM in Today's Data Landscape</h2>
<p>Over the past few years, marketing has seen significant growth, pivoting from third-party marketing measurement to a focus on first-party data. This transition is crucial in the context of GDPR, iOS 14 changes, and browser-based limitations. These changes are not just technical shifts; they represent a fundamental change in how we approach marketing data.</p>
<p><blockquote><em>"Marketing mix modeling supplements decision-making by incorporating various data inputs, including spending, conversions, competitor activities, events, and organic data, to optimize budget allocation."</em></blockquote></p>
<p>These changes have been a major reason for the resurgence of MMM. With businesses collecting unfathomable data and having access to store and put this data to use has become the core of modern MMM.</p>
<p>The crux of modern MMM lies in its use of historical spend and sales data, and external factors like competitor promotions. This model enables marketers to optimize budget allocation more effectively than ever before. As Rohit notes, "<em>Marketing mix modeling measures the true influence of media channels, online and offline, beyond click-based or view-based attribution</em>".</p>
<h2>Adapting to a Privacy-First World</h2>
<p>In this new era of advertising, where advanced tracking methods are under scrutiny, the fundamental principles of advertising still hold strong. The challenge is no longer about collecting more data but about utilizing first-party data effectively while respecting user privacy.</p>
<p>The future, as Rohit sees it, involves adapting to new data-driven approaches. This means building robust first-party data strategies and re-educating ourselves to leverage a range of marketing tools that go beyond traditional attribution methods.</p>
<p><blockquote><em>"Building out a first-party identity and first-party data strategy is where it';s all at. Brands today need to put 100% of their focus on ensuring their data is owned by them and not sitting in some other platform."</blockquote></em></p>
<h2>The Role of MMM in Today's Marketing Tech Stack</h2>
<p>MMM plays a vital role in the martech analytics stack. It is used for budget reallocation, followed by campaign execution, data activation, attribution, and daily optimization. This process creates a feedback loop, enabling more accurate budget allocation and measurement.</p>
<p>However, embracing MMM is not without its challenges. Convincing marketers, accustomed to attribution models, about the value of probabilistic models like MMM can be daunting. Data accessibility and quality are additional hurdles. But as Rohit rightly points out,"<em>Traditional methodologies have evolved, with cloud-based AI capabilities enabling faster marketing mix model outputs for even non-technical or non-marketing analytics users</em>."</p>
<h2>Leveraging Lifesight for Effective Media Buying</h2>
<p>Lifesight's solutions have been instrumental for brands in achieving remarkable results. For instance, a leading retail group that adopted Lifesight's solution end-to-end witnessed a 50% improvement in reach accuracy and a 30% reduction in cost per acquisition. This not only increases their Return on Ad Spend (ROAS) but also enables them to trim marketing budgets while sustaining sales growth.</p>
<p><blockquote><em>"When it comes to match rates using our tool, marketers are seeing upwards of 50% improvement in the accuracy of the reach of the customer base. Their cost per acquisition also drops by a significant amount."</em></blockquote></p>
<h2>Embracing the New MMM</h2>
<p>In conclusion, MMM's evolution represents a significant opportunity for marketing leaders. It's a tool that aligns perfectly with the current trends of data privacy and the increasing need for more sophisticated, AI-driven marketing strategies.</p>
<p>Rohit Maheswaran's insights underscore the potential of MMM in transforming media buying strategies in a privacy-first era. As the landscape of digital marketing continues to evolve, embracing MMM could be the key to unlocking new levels of scale and incrementality for brands.</p>
<p>For those keen to explore how Lifesight can revolutionize their marketing strategies, signing up for a demo could be the first step towards harnessing the full potential of MMM. Lifesight's innovative approach to data and its practical application in today's complex marketing environment make it an invaluable partner for brands looking to stay ahead in the game.</p>
<p>Whether you're navigating the intricacies of retail, ecommerce, finance, or media, understanding and implementing MMM with Lifesight's expertise could be your strategic advantage in a competitive market. It's not just about adapting to change; it's about leading it.</p>
<p><a href="https://www.lifesight.io/demo">Sign up for a Lifesight demo</a> and embark on your journey towards marketing excellence.</p>

Insights On Marketing Mix Modeling And Media Buying In A Privacy First Era ft. Rohit Maheswaran.webp

Hear from Rohit Maheswaran, Co-Founder and CPO at Lifesight on how MMM can help you unlock scale and incrementality and optimize your media buying experience

Insights On Marketing Mix Modeling And Media Buying In A Privacy First Era

Data

<p>According to a survey conducted by Statista in June 2023, 53% of advertisers mentioned that they face issues with reaching their target audience at scale. Most of these marketers have been relying on third-party cookies for their marketing attribution. But lately, these cookies have not really been a good friend to marketers.</p>
<p>We hosted a panel of three experts in the marketing and advertising space who have a cumulative experience of 25+ years helping both B2B and B2C brands with data-driven marketing to answer the question: &ldquo;<em>How can advertisers overcome signal loss and identity issues?</em></p>
<p>In this blog post, we will expand on some key takeaways from this conversation.</p>
<p>Alternatively, if you want to get the video recording of the panel talk, head to the following link - <a href="https://www.lifesight.io/events/how-advertisers-overcome-signal-loss-and-identity-issues"><em>How advertisers overcome signal loss &amp; identity issues?</em></a></p>
<h2>Takeaway 1: Signal loss is a major concern in advertising but there&rsquo;s a solution that is growing in popularity</h2>
<p>With Google planning to slowly kill off third-party cookies, marketers are already seeing the repercussions in the form of broken and inaccurate customer journeys. To make things worse, advertising platforms like Google, Facebook, and the like are trying to gain more control over the walled gardens created by them.</p>
<p>With data being held by these platforms marketers are finding it hard to devise campaigns and prove ROI. Also, GDPR laws are increasingly becoming more stringent across the globe and the message is very clear for marketers: <strong>User data privacy is paramount going forward</strong>.</p>
<p>Companies like Apple too are forcing marketers to make data privacy the centerpiece of their overall marketing strategy with their ATT framework.</p>
<p>But there&rsquo;s a solution and it&rsquo;s called <strong><em>first-party data</em></strong>.</p>
<p>Here&rsquo;s what our panelist <a href="https://www.linkedin.com/in/frank-brandstetter/">Frank Brandstetter</a>, Director of Programmatic at Safe Exchange had to say about <a href="https://youtube.com/shorts/WlmYFXMQRH0?feature=share">signal loss</a>.</p>
<h2>Takeaway 2: First-party data to the rescue of complex customer journeys</h2>
<p>As data becomes more scarce in the third-party world, companies started shifting towards owning their data. In the last five years, the term "first-party data" has seen up to a 4x increase in interest across the globe.</p>

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<p>The first-party data movement, if we may call it, is helping businesses get rid of the walls of these advertising platforms and structure their data around user consent and privacy.</p>
<p>This approach is a win-win for both businesses and customers and frankly a more reliable way to get a holistic view of the entire customer journey.</p>
<p>Take Starbucks for example. Starbucks has been collecting relevant user data with consent and is using it not just to drive better customer experiences but also to make major decisions on expansions and product launches.</p>
<h2>Takeaway 3: Collecting and implementing first-party data has its own challenges</h2>
<p>On the face of it, first-party data collection and implementation look easy, but in reality, it is a complex puzzle with different stakeholders.</p>
<p>First and foremost, it needs investment from companies to set up the infrastructure that can help you with first-party data collection and implementation. And where there is investment there is also buy-in.</p>
<p>With multiple teams interacting with your marketing data, you need to convince not just the C-suite, but also other stakeholders to realign their data strategy.</p>
<p>And before we address the <em>how </em>it needs a thorough audit to understand the current data sources and what role they play in marketing data collection.</p>
<p>Finally, before you jump into any investment, be crystal clear on what are your expectations. Adopting a new strategy or a platform without this can lead to more problems.</p>
<p><a href="https://www.linkedin.com/in/ikembhavi/">Isha Kembhavi</a>, Principal Consultant at Evalueserve, shared <a href="https://youtube.com/shorts/KiyahacbLik?feature=share">more insights on these challenges</a>.</p>
<h2>Takeaway 4: The missing piece in the build vs buy conversation of first-party data</h2>
<p>This is an ongoing question with every technology - Should we build it in-house or should we take the vendor route?</p>
<p>According to <a href="https://www.linkedin.com/in/anantdamle/">Anant Damle</a>, Head of Solutions Architect at Google, there are some<a href="https://youtube.com/shorts/8l2CmdwXFxQ?feature=share"> important factors to consider before you choose any side</a>.</p>
<ul>
<li><strong>Gauge the size of our data</strong>: Do you have multiple data sources? How much data do you possibly collect? How are you going to store and channel the data?</li>
<li><strong>What use cases are you going to tackle:</strong> Without understanding how you are going to use the data, building it or buying it is going to be very expensive. First figure the use case, then the medium.</li>
<li><strong>Who is the end user</strong>: The way a technical user uses a platform differs a lot from the way a non-technical user uses it. The UX, features, and applications differ drastically. Your platform needs to address their requirements.</li>
</ul>
<p>You might be thinking of cost as a factor as well. But it's hard to put a number to that. The cost of a platform, whether you build it or buy it, is going to differ for different companies. The three factors listed above are a testament to it. Moreover, we recommend you not see that as a cost and more as an investment. Finalize how much ROI are you expecting out of the platform and then decide on what cost are you willing to pay.</p>
<h2>Takeaway 5: The dusk of third-party cookies is the dawn of first-party data</h2>
<p>Marketing is heavily dependent on buyer psychology, irrespective of whether you are a B2B organization or a B2C organization. And this buyer behavior is constantly changing.</p>
<p>With every major event, the buying behavior changes rapidly. For instance, COVID-19 saw the rise of D2C businesses, but as things got more stable, we are sitting at a time when omnichannel is more important than ever.</p>
<p>Third-party cookies being digital tools won&rsquo;t help you address this shift. You need something that links offline data to online, and first-party data platforms have the potential to do it.</p>
<p>More importantly, <a href="https://youtube.com/shorts/kGZD8SW4bDM?feature=share">first-party data</a> gives freedom from the walled gardens and brings autonomy to businesses. This is an advantage that is worthy of your investment for now and in the future.</p>
<h2>Stepping into the first-party world</h2>
<p>The challenges posed by signal loss and identity issues in advertising are real and pressing. The imminent demise of third-party cookies, compounded by stricter data privacy regulations has forced marketers to seek alternative solutions. First-party data acts as a beacon of hope for those looking to future-proof their advertising strategies.</p>
<p>We at Lifesight are propellers of first-party data and have been helping multiple businesses get started with first-party data and implement it to get the best ROI possible. Our platform not only helps you own your data but also gives you insight into how your marketing campaigns are working, what channels are bringing in revenue, and how much. We help you answer questions like &ldquo;<em>Do I really need to spend so much on this channel</em>&rdquo;? or &ldquo;<em>Is this region bringing in as much revenue as the others</em>&rdquo;?.</p>
<p>So, if you are a curious marketer who regularly asks these questions and is willing to explore the opportunities that first-party data can unlock for your business, <a href="https://www.lifesight.io/demo">sign up for a demo</a>.</p>
<p>We promise you won't be disappointed.</p>

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5 Things Recommended By Experts To Overcome Signal Loss And Identity Issues

Measurements

<p>The stakes for accurate marketing measurements have never been higher than in today's ever-changing digital marketplace. A report by Forrester reveals that <a href="https://www.gartner.com/en/newsroom/press-releases/2022-09-15-gartner-survey-reveals-marketing-analytics-are-only-influencing-53-percent-of-decisions">just over 53%</a> of marketing decisions are influenced by marketing analytics, underscoring a significant gap in leveraging data effectively.</p>
<p>This disconnect becomes even more pronounced, considering Gartner's finding that marketing budgets account for <a href="https://www.gartner.com/en/articles/what-marketing-budgets-look-like-in-2022">almost 11%</a> of overall company revenue. Yet, despite substantial investment, marketers often need to work on measurement mistakes that can significantly impact the ROI and effectiveness of their campaigns.</p>
<p>In this blog, we delve into the most common measurement mistakes plaguing marketers today and explore possible solutions to marketing success in a data-driven world.</p>
<h2>Mistake 1: Over-Reliance on Platform-Centric Reporting</h2>
<p>One of the most prevalent mistakes in the realm of marketing measurement is an over-reliance on platform-centric reporting. This approach, often adopted for simplicity and accessibility, can lead to a myopic view of marketing performance. Typically, platforms provide data inherently biased towards their ecosystem, painting a skewed picture of campaign effectiveness.</p>
<p>A 2020 study by Nielsen highlighted that marketers relying solely on in-platform metrics are likely to miss about 25% of their audience reach.</p>
<p>This fragmented approach can lead to significant gaps in understanding the customer journey, as it spans multiple touchpoints across diverse platforms. Also, in-platform reporting needs more depth to analyze cross-channel interactions and their cumulative impact on consumer behavior.</p>
<h3>The Solution: Embracing Unified Reporting</h3>
<p>The solution to this challenge lies in embracing a more holistic approach to data analysis - Unified Reporting. <a href="https://www.lifesight.io/cdp">Customer data platforms</a> can help you aggregate and analyze data from multiple platforms, offering a comprehensive and unbiased view of the marketing landscape. Unified reporting transcends the limitations of single-platform data, allowing for a more accurate understanding of cross-channel performance and consumer behavior.</p>

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<p>Moreover, modern-day CDPs have a layer of first-party identity graphs that help to recognize &amp; attribute sales from anonymous visitors by stitching marketing &amp; customer identities.</p>

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<h2>Mistake 2: Inaccurately Attributing Conversions</h2>
<p>The advent of stringent privacy regulations and updates like Apple's iOS 14.5 &amp; 17 has reshaped the tracking landscape of digital marketing. Traditional methods, which heavily relied on user tracking across platforms and devices, are becoming increasingly ineffective. One of the critical challenges arising from this new privacy-first world is the need for accurate marketing attribution.</p>
<h3>The Solution: Adopting Sophisticated Multi-Touch Attribution Models</h3>
<p><a href="https://www.lifesight.io/attribution">Sophisticated multi-touch attribution models</a> are a potent solution in this privacy-first era as they provide transparent performance reporting on the channel, campaign, ad set, ad, and creative level. It allows you to integrate data from a variety of sources, both online and offline, to create a comprehensive view of the customer journey. This integration is crucial in today's omnichannel marketing environment, where consumers interact with brands across multiple platforms.</p>

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<p>Moreover, multi-touch attribution models use advanced analytics and machine learning to conclude aggregated data. Marketers can compare different attribution models, and look back windows and business KPIs to see how their campaigns work without compromising the users' privacy.</p>
<p>A brand and community expert <a href="https://www.linkedin.com/in/priyanka-panigrahi26/">Priyanka Panigrahi</a> shared this post about how to choose the right attribution model.</p>

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<h2>Mistake 3: Using Outdated Budget Allocation Methods</h2>
<p>A common yet critical mistake in the realm of marketing is adhering to outdated budget allocation methods. Many businesses still rely on traditional, often static, budgeting approaches that fail to consider the dynamic nature of the digital marketing landscape. This outdated methodology can lead to suboptimal spending, where funds are not allocated efficiently across channels, resulting in diminished returns on investment.</p>
<h3>The Solution: Using Marketing Mix Modeling and Saturation Curve</h3>
<p><a href="https://www.lifesight.io/marketing-mix-modelling">Marketing Mix Modeling (MMM)</a> and Saturation Curves are essential tools for efficient budget allocation in marketing. MMM provides a comprehensive view of the effectiveness of various marketing channels by analyzing historical data. This analysis helps in understanding the return on investment (ROI) for different marketing channels, guiding businesses on where to allocate their budget for maximum impact. MMM is also useful in forecasting the potential outcomes of future marketing strategies, aiding in proactive budget planning.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image8_459e44de71.webp) 

<p>On the other hand, Saturation Curves are instrumental in identifying the point at which additional spending in a specific channel becomes less effective, known as the point of diminishing returns.</p>

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<p>This insight allows businesses to determine the optimal level of spending in each channel, avoiding unnecessary expenses. By comparing the performance of different channels through their respective saturation points, businesses can strategically reallocate budgets from saturated channels to those with higher potential returns.</p>
<p>Together, MMM and Saturation Curves provide a clear framework for businesses to allocate their marketing budgets effectively, ensuring that every dollar spent contributes to overall marketing success without wastage.</p>
<h2>Mistake 4: Ignoring Incremental Outcome Measurement</h2>
<p>A critical oversight in many marketing strategies is the neglect of measuring incremental outcomes. Incrementality refers to the additional value or impact generated specifically by a marketing activity, beyond what would have occurred without it. Without this measurement, businesses struggle to understand the true effectiveness of their marketing campaigns, potentially leading to misguided strategies and inefficient allocation of resources.</p>
<h3>The Solution: Emphasizing Incrementality Testing</h3>
<p>To truly gauge the effectiveness of marketing campaigns, it is imperative to focus on <a href="https://www.lifesight.io/incrementality-testing">incrementality testing</a>. This involves setting up controlled experiments to compare the results of a campaign against a baseline where the campaign did not occur. This approach provides a clear picture of the additional value created by specific marketing efforts, enabling businesses to refine their strategies based on tangible outcomes.</p>
<p>There are platforms that offer robust tools for incrementality testing, such as audience split testing and geo-experiments. These features allow marketers to conduct controlled experiments easily, measuring the incremental impact of their campaigns with precision.</p>
<p>For instance, with audience split testing, marketers can compare the behaviors of a group exposed to a campaign against a similar group that wasn't, providing clear insights into the campaign's effectiveness.</p>
<p>Geo-experiments further enhance this capability by allowing marketers to evaluate campaign performance in different geographical areas. This is particularly valuable for businesses operating in multiple regions, enabling them to tailor their strategies according to regional variations in consumer response.

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<h2>Mistake 5: Failure to Measure The Impact of Marketing on Retention &amp; Profits</h2>
<p>A frequently overlooked aspect in marketing measurement is the failure to assess the impact of marketing strategies on customer retention and long-term profitability. Many marketers focus intensely on immediate advertising performance metrics, such as click-through rates or impressions, neglecting the deeper analysis of how these efforts contribute to sustainable business growth and customer loyalty.</p>
<h3>The Solution: Advanced Analytics and Cohort Analysis</h3>
<p>The key to addressing this oversight is through the use of advanced analytics and cohort analysis. This approach involves segmenting customers into cohorts based on shared characteristics or behaviors and tracking their interactions and contributions over time. Using advanced analytical tools, marketers can gain deeper insights into how marketing efforts influence customer loyalty, repeat purchases, and overall profitability.</p>

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<h2>Conclusion</h2>
<p>Navigating the complex terrain of digital marketing requires more than just intuition; it demands precision and insight. Lifesight's suite of tools addresses key marketing measurement challenges, enabling you to make data-driven decisions with confidence. From optimizing ad spend to dissecting the customer journey, Lifesight transforms challenges into opportunities for growth. Don't let common measurement mistakes hold you back. Elevate your marketing strategy today.</p>
<p><br />Ready to revolutionize your approach to marketing measurement? <a href="https://www.lifesight.io/demo">Book a Demo</a> with Lifesight and step into the future of data-driven marketing success.</p>

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Top 5 Marketing Measurement Mistakes & How To Avoid Them

<p>Among the myriad of testing methodologies, Incrementality Testing and A/B Testing stand out as powerful tools, each offering unique insights into the impact of marketing efforts. Yet, there often needs to be more clarity about what sets these two methods apart and when to use each.</p>
<p>Incrementality Testing and A/B Testing are both pivotal in assessing marketing strategies, but they serve different purposes and provide different kinds of insights. Incrementality Testing is focused on understanding the additional value or 'lift' that marketing efforts bring over and above what would have happened naturally without those efforts. It answers the critical question: "Is this marketing activity adding any extra value?"</p>
<p>On the other hand, A/B Testing, also known as split testing, is about comparing two versions of a webpage or app against each other to determine which one performs better. It&rsquo;s often used for optimizing website design, ad copy, or other elements to improve user engagement or conversion rates.</p>
<p>This blog will explore both methodologies' differences, benefits, and best use cases. Understanding the distinction between incremental testing and A/B Testing is not just an academic exercise; it's a practical necessity for marketers aiming to make data-driven decisions in a competitive digital marketplace.</p>
<h2>Decoding A/B Testing</h2>
<p><a href="https://www.lifesight.io/blog/ecommerce-ab-testing">A/B testing</a> originated in the 1920s with statistician and biologist Ronald Fisher defining its basic principles and doing agricultural experiments using the method. Marketers adopted A/B testing in the 1960s and 1970s to evaluate responses to different marketing campaigns. Would sending a discount voucher in magazines drive more engagement and brand visibility, or would sending a postcard about the latest product do the same?</p>
<p>A/B testing in marketing as we know it today began in the 1990s with the advent of the internet. Today, marketers use A/B testing to compare different email campaign variations (different subject lines, CTAs), app UIs, and more. A/B tests help marketers determine which campaign version generates more responses and drives more conversions. Today, everything from the size and color of a subscribe button to an app or website homepage design, promotional emails, and more is tested using A/B testing. It is one of the most basic forms of a randomized controlled experiment.</p>
<h2>A/B Testing &ndash; Methodology</h2>
<p>A fashion website may create two versions of a buy now button. To run the test, they showed two sets of users randomly picking the different button versions (the only difference being color) and determining which version got more clicks and conversions.</p>
<p>A/B testing focuses on conversion metrics such as bounce rates, click-through rates, and other engagement metrics to evaluate what works better. The end goal is to optimize a design, campaign, or strategy. It enables marketers to discover the most effective strategies and designs to drive desired customer behaviors and enhance customer engagement.</p>
<p>In a <a href="https://www.linkedin.com/posts/abhisheekpatra_emailmarketing-dtc-ecommerce-activity-7115710512562130944-BzI0?utm_source=share&amp;utm_medium=member_desktop">LinkedIn post</a>, Abhishek Patra, a DTC email and SMS marketing expert, stated that these 6 A/B tests achieved 47% to 60% lifts.</p>

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<p>However, more than comparing one aspect of the overall campaign is needed in the privacy-first internet era and an over-crowded marketplace. What marketers need today is a solution that can help them assess the impact of a marketing campaign on their key performance indicators, such as how many people installed their app or the uplift in profitability or conversions before and after running a campaign, etc. This is why <a href="https://www.lifesight.io/incrementality-testing">incrementality testing</a> has become a hot favorite of modern-age growth marketers. Let's explore more about incrementality testing.</p>
<h2>De-mystifying Incrementality Testing</h2>
<p>Incrementality enables brands to analyze the performance of their campaigns and methods by measuring the lift in desired outcomes like profitability, revenue, conversions, and brand awareness.</p>
<p>Incrementality testing is about quantifying the actual incremental effect of a particular advertising strategy. It goes beyond just tracking metrics and comparing different creative versions, unlike A/B testing. It helps brands determine whether or not a particular campaign positively affected a specific outcome, such as increased sales. Incrementality testing also helps assess whether the campaign was necessary and if it resulted in any net gain.</p>
<p>Let&rsquo;s understand the incrementality test with the help of an example.</p>
<p>An online garment company launched a promotional campaign offering customers who downloaded the app a shopping voucher worth $100. One thousand people installed the app after the campaign was launched. But, say, 600 users (60%) may have installed it just because they wanted to and not because of this campaign. Incrementality testing helps marketers get an answer to this.</p>
<h2>Types of Incrementality Testing</h2>
<p><strong>Geo-testing in Incrementality</strong>: <a href="https://www.lifesight.io/blog/geo-experiments">Geo-testing</a> is a popular incrementality method that assesses a tactic's impact by altering media delivery in specific locations and then evaluates the resulting changes in advertising sales to determine the media's true value.</p>
<p><strong>Known Audience Testing</strong>: Offering a granular approach, known audience testing facilitates experiments at a detailed level, leveraging specific audience segments to understand how distinct media treatments impact conversions.</p>
<p>Traditionally, marketers performed incrementality testing using the A/B testing methodology where one group of customers was exposed to a campaign and one group was not. The results of both were compared to see how much was the difference. From the above example, say the group exposed to the campaign resulted in 120 app downloads, whereas the group not exposed led to 100 downloads. Which means the uplift was 20%. However, the traditional method of incremental testing is plagued with innumerable challenges.</p>
<ol>
<li>Implementing A/B tests for large user groups is time-consuming and costly.</li>
<li>Given the new rules and regulations surrounding customer privacy, the detailing required for the old methodologies is impossible.</li>
</ol>
<p>This is where more modern solutions like <a href="https://www.lifesight.io/incrementality-testing">Lifesight</a> can be instrumental, easy, and effective ways to implement incrementality testing and gauge the effectiveness of online and offline campaigns by a brand.</p>
<p>Marketing expert <a href="https://www.linkedin.com/posts/michael-the-data-guy-kaminsky_knowing-whether-brand-search-spend-is-incremental-activity-7071886107633704960-VVEe?utm_source=share&amp;utm_medium=member_desktop">Michael Kaminsky</a> explains how brands can know if their brand search spend is incremental or not using incrementality testing.</p>

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<h2>A/B Testing vs Incrementality Testing</h2>
<p>While both Incrementality and A/B Testing share similarities, like segmenting audiences, their overarching objectives set them apart, with A/B testing serving broader purposes and Incrementality Testing honing in on specific media tactic impacts.</p>
<table>
<tbody>
<tr>
<td>
<p><strong>A/B Testing</strong></p>
</td>
<td>
<p><strong>Incrementality Testing</strong></p>
</td>
</tr>
<tr>
<td>
<p><strong>Purpose</strong></p>
</td>
<td>
<p>It helps identify the best version of one element of a campaign in isolation. The objective is to determine which variation of the CTA button, webpage design, and email subject line works better. It is evaluated based on metrics like click-through rate and email open rate.</p>
</td>
<td>
<p>Incrementality test scratches beyond the surface. The objective here is to test the effectiveness of a campaign rather than one component in isolation. It helps determine whether or not a certain campaign is required or not.</p>
</td>
</tr>
<tr>
<td>
<p><strong>Implementation</strong></p>
</td>
<td>
<p>Two user groups are identified, and one is exposed to one variation of the element and the other to another version to determine which of the two generates the desired action. The variation could be an email subject line or a different colored CTA. The assessed metrics would be &lsquo;email open rates&rsquo; and &lsquo;click-through rates, respectively.</p>
</td>
<td>
<p>Incrementality testing has a broader scope. Marketers typically asses and compare outcomes with and without a campaign. A/B testing was one way to do incremental testing in the past. However, modern marketers employ sophisticated solutions like Lifesight Measure to assess the efficacy of different online and offline campaigns at scale</p>
</td>
</tr>
<tr>
<td>
<p><strong>Insights</strong></p>
</td>
<td>
<p>Offers insights into which variant of a campaign element (be it a landing page, ad design, or email subject line) resonates more with the audience.</p>
</td>
<td>
<p>Delves deeper, unearthing insights on how much real-world impact a campaign or tactic has on overall business outcomes beyond just engagement metrics.</p>
</td>
</tr>
<tr>
<td>
<p><strong>Application</strong></p>
</td>
<td>
<p>Applied to various digital touchpoints (website design tweaks to email marketing campaigns) to improve performance by testing design/layout/content variations.</p>
</td>
<td>
<p>Applied to an entire marketing campaign to ascertain the true value of advertising efforts and to isolate the effect of external influencing factors. The idea is to determine whether or not a certain campaign is even required in the first place.</p>
</td>
</tr>
<tr>
<td>
<p><strong>Test groups</strong></p>
</td>
<td>
<p>Typically, it involves dividing audiences into groups that experience different campaign variations.</p>
</td>
<td>
<p>It involves not running certain tactics and campaigns from specific regions or sets of audiences while parallelly running it with another set of audiences to evaluate the incremental effects.</p>
</td>
</tr>
<tr>
<td>
<p><strong>Effectiveness</strong></p>
</td>
<td>
<p>It yields quick results as it is basically testing individual components in isolation, and hence, the time required to run an A/B test is short, allowing for quick optimizations.</p>
</td>
<td>
<p>Being complex and wider in scope, the time involved in conducting an incrementality test is long. The final result and inferences are drawn considering several internal and external influences.</p>
</td>
</tr>
</tbody>
</table>
<h2>Why Understanding These Differences Matter</h2>
<p>Despite a few similarities, such as audience segmentation, it's important to note that not all incremental tests are A/B tests and vice-versa. As a growth marketer, understanding the distinction between the two is essential, especially in this multi-channel and omnichannel marketing era. With online and offline marketplaces co-existing, understanding what truly works is important to retain customers across channels.</p>
<p>Marketing campaigns cost a lot of money, and identifying the right channels that generate returns is imperative. Grasping the differences between A/B testing and Incremental testing is more than academic; it's pivotal for marketers aiming to pinpoint their campaigns' genuine value and refine their strategies for optimum results.</p>
<h2>Final Word</h2>
<p>As marketers, it is essential to understand that both A/B testing and incremental testing have different purposes. Which is an ideal fit depends on the specific outcomes one tries to address. However, with an average person being exposed to a whopping <a href="https://webtribunal.net/blog/how-many-ads-do-we-see-a-day/#gref">4,000 to 6,000 advertisements</a> in a day, marketers, more than ever, must determine which marketing initiatives are truly generating results and which of them are just noise. This is where incrementality testing helps. A/B testing helps fine-tune the little details. Take your pick basis what you wish to achieve.</p>
<p><a href="https://www.lifesight.io/incrementality-testing">Lifesight&rsquo;s Incrementality testing</a> is an easy and effective way to measure the incremental impact of online and offline marketing initiatives. The meticulously designed automated incremental lift tests are easy to implement and measure the impact of campaigns across multiple channels. Scale your marketing spending with confidence in a privacy-first era with Lifesight.</p>

Incrementality Testing vs A_B Testing - What’s the difference (1).webp

Understand the differences between Incrementality and A/B Testing. Choose wisely to refine your strategies and maximize campaign effectiveness in today's competitive marketing landscape.

The Distinct Roles of Incrementality Testing and A/B Testing

<p>This is the third part of our Geo-Experiments 101 series. The first part spoke about the basics of geo-experiments, why you should care, and how you can design your first geo-experiment. If you have not read it we encourage you to <a href="https://www.lifesight.io/blog/geo-experiments">read part 1</a>.</p>
<p>In the <a href="https://www.lifesight.io/blog/deploy-and-manage-geo-experiment-test">second part</a>, we delved into deploying and managing a geo-test, market selection strategies, market size, and testing volume.</p>
<p>This is the final installment of the series where we will be talking about the analysis and reporting of geo-tests, how to measure incrementality and validation of your geo-tests, maximizing business impact, and explaining why continuous testing matters.</p>
<p>Let&rsquo;s begin.</p>
<h2>Analyzing and Reporting Geo-Tests</h2>
<p>What&rsquo;s the point of running tests if you cannot document your findings and analyze them for further improvements?</p>
<p>Analyzing and reporting your geo-tests is a pivotal phase of the entire process. However, there are different ways a team can approach this. We&rsquo;ll navigate through the manual reporting for beginners, progress to semi-automated reporting for intermediaries, and finally, explore the efficiency of fully automated reporting for advanced marketers.</p>
<h3>Manual reporting</h3>
<p>For beginners, manual reporting is often the starting point. This involves designing reports, aggregating sales data, selecting analysis techniques, and executing the analysis methodically. While this method is fundamental, it requires considerable time and effort, typically 5-10 hours for a basic test. The challenge intensifies as there&rsquo;s no standardized reporting format, making it difficult to compare results across multiple tests and channels.</p>
<p>Despite its limitations, manual reporting provides a foundational understanding of the testing components. However, it&rsquo;s essential to acknowledge its potential drawbacks, such as increased time investment and a lack of standardized comparison metrics.</p>
<h3>Automated reporting</h3>
<p>As you progress to an intermediate level, you&rsquo;ll likely introduce automation into certain aspects of reporting. This alleviates some of the manual burdens, allowing for standardized visual formats and statistical diagnostics. Although the processes are still performed manually, the conclusion of automation reduces the overall reporting time, typically from 5 to 2 hours per test.</p>
<p>Intermediate-level testers often adopt techniques like a &lsquo;flat average&rsquo; approach, comparing sales in test and control groups against a prior period. Additionally, confidence intervals might be applied for statistical precision, enhancing the reliability of results. While this level of automation streamlines reporting, it still requires hands-on execution and might lack the precision achieved in advanced practices.</p>
<h3>Fully automated reporting</h3>
<p>At the advanced level, you step into the realm of fully automated reporting. This sophisticated approach leverages big data analysis, machine learning, and predictive models based on control markets. The end-to-end process, from design to deployment and reporting, becomes automated, ensuring efficiency and precision.</p>
<p>Treatments to campaigns are trafficked automatically via API, significantly reducing the likelihood of human error. Transcript data seamlessly feeds back into an automated analysis framework, populating reports as the test progresses. Notably, reporting is based on a counterfactual prediction, offering precise confidence bounds for any tested result. This standardized approach ensures consistency and comparability across all tests.</p>
<p>Here's a table summarizing the three reporting approaches:</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Geo_experiments_The_three_reporting_approaches_d003bbd4ca.webp) 

<h2>Measuring Incrementality and Ensuring Validity in Geo Tests</h2>
<p>Incrementality is the golden metric in geo testing, representing the lift in Key Performance Indicators (KPIs) attributed to your marketing efforts. It answers the fundamental question: How much of KPI &lsquo;X&rsquo; resulted from my ads, as opposed to what would have occurred organically?</p>
<h3>The journey from basic to advanced methods:</h3>
<p>In the early stages, many opt for matched market tests, where similar geographies are treated differently to observe the impact. However, this method involves uncertainties and assumptions, making it less recommended.</p>
<p>Intermediate testers often employ Difference-in-Difference (DID) analysis, comparing trends in two markets to estimate the effect of ads. While more robust than matched markets, DID introduces complexities and assumptions.</p>
<p>The advanced, industry-standard method is predicted counterfactuals. This technique designates test markets, models synthetic untreated versions, and compares real observed behavior to the modeled versions. Tools like Lifesight automate this complex process, optimizing market selection, model terms, and confidence intervals.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Geo_experiments_The_journey_from_basic_to_advanced_methods_acac659565.webp) 

<h3>Ensuring validity: The guardian of trustworthy results</h3>
<p>Validation is crucial to ensuring that your experimental setup was accurate and the observed results can be trusted. Here&rsquo;s how you can validate your geo tests effectively:</p>
<ol>
<h4>1. Resampling and P-Values:</h4>
</ol>
<p>If you&rsquo;re using DID, resampling can provide a distribution of your estimator, helping calculate a p-value. This statistic indicated the probability that observed results are not due to chance. However, this method has limitations.</p>
<ol>
<h4>2. Retroactive &ldquo;A/A&rdquo; tests:</h4>
</ol>
<p>With predicted counterfactuals, you can conduct retroactive &ldquo;A/A&rdquo; tests, modeling synthetic control for a period with observed untreated data. This validates the model&rsquo;s accuracy and helps detect any artificial lift.</p>
<ol>
<h4>3. Confidence Intervals:</h4>
</ol>
<p>Fully automated platforms like Lifesight implement clear confidence intervals. Results falling outside these intervals are termed &ldquo;statistically significant&rdquo;, indicating low chances of occurring by chance.</p>
<p>As you venture into geo testing, calculating incrementality and validating results are the linchpins of reliable insights. Whether you&rsquo;re adopting basic methods or embracing advanced predictive models, precision and confidence in your results set the stage for informed marketing decisions.</p>
<h2>Maximizing Business Impact: Navigating Risks in Geo-Testing</h2>
<p>Geo-testing, while a powerful tool, is not without its risks. The larger the test market, the greater the potential loss of sales during the testing period. Add to this the challenge of inconclusive results, contaminated data, and the significant investment of time and resources, and you&rsquo;re navigating a complex landscape.</p>
<p>The traditional approach, especially at beginner and intermediate levels, often involves holding out a substantial portion of the country for testing. This can lead to a substantial volume of lost sales. The bigger the market size for testing, the higher the stakes.</p>
<p>Also, beginner and intermediate design and analysis techniques may yield results prone to contamination, noise, and lack of representativeness. Inconclusive or misleading results can leave marketers in a state of uncertainty, impacting their confidence in making significant media investment decisions.</p>
<p>Executing a testing practice that neither yields the volume of tests per year nor the accuracy of conclusive tests can also be a silent killer of time, effort, and resources. Brands might revert to less superior forms of media measurement, stalling progress and hindering strategic decision-making.</p>
<h3>Advanced practices to the rescue</h3>
<p>Advanced geo-testing practices shine as a beacon of efficiency, providing a roadmap to minimize these risks and amplify business impact.</p>
<ol>
<h4>1. Reducing market size with predicted counterfactuals:</h4>
</ol>
<p>The most sophisticated geo-testing involves predicted counterfactuals. By creating models based on trends in control markets, only 5-10% of markets need to be used in a treatment group. This drastic reduction in market size significantly decreases the risk of lost sales during testing.</p>
<ol>
<h4>2. Automated deployment for precision:</h4>
</ol>
<p>Fully automated testing platforms, like Lifesight, streamline the end-to-end process. Automated deployment via APIs ensure precision in trafficking treatments to campaigns, reducing the likelihood of human error and providing early visibility into test performance.</p>
<ol>
<h4>3. Efficient reporting and analysis:</h4>
</ol>
<p>Automated reporting and analysis further contribute to efficiency. Brands can leverage sophisticated analytical practices with standardized visual formats, ensuring precise results and freeing up time for strategic decision-making.</p>
<h2>Why Continuous Testing Matters?</h2>
<p>There are two ways to look at Geo-Testing: You can either use it just as a strategic advantage or make it the heartbeat of sustained growth and enhanced performance. The best brands usually do the latter. But there are reasons for that.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Geo_experiments_Test_Analyze_and_Implement_081a80693f.webp) 

<h3>The power of ongoing insight</h3>
<ol>
<h4>1. Adaptive decision-making:</h4>
</ol>
<p>Continuous testing allows brands to adapt swiftly to dynamic market changes. In a landscape where consumer behavior, trends, and competitive landscapes are in constant flux, the ability to make informed, adaptive decisions is a strategic advantage.</p>
<ol>
<h4>2. Iterative refinement:</h4>
</ol>
<p>Think of continuous testing as a refining process. Each test is a learning opportunity. Brands can refine their strategies, pinpoint what works best, and progressively enhance their approach. Iterative refinement is the heartbeat of sustainable growth.</p>
<ol>
<h4>3. Proactive issue identification:</h4>
</ol>
<p>Unforeseen issues can hamper the effectiveness of marketing strategies. Continuous testing acts as a proactive detective, identifying issues in real time. Brands can address challenges promptly, preventing potential setbacks.</p>
<ol>
<h4>4. Maximizing ROI:</h4>
</ol>
<p>We are already in a dynamic world where things change rapidly. Maintaining and maximizing ROI in such a setup is paramount but needs constant learning. Continuous testing allows brands to fine-tune their campaigns, optimizing for performance and ensuring that every marketing dollar spent contributes to the bottom line.</p>
<h2>Concluding it</h2>
<p>Since the series delved into the everything of geo-testing&ndash;from design to deployment to reporting, we wanted to also stress on some takeaways that can lead to sustainable growth and performance.</p>
<ul>
<li><strong>Embrace change with confidence</strong>: Rather than being reactive, become proactive. Anticipate shifts and stay ahead of the curve.</li>
<li><strong>Build a culture of learning</strong>: Foster a mindset where every test is a lesson, and each lesson contributes to a collective pool of knowledge.</li>
<li><strong>Stay ahead in the competitive landscape</strong>: Consistently optimize your strategies. Ensure your brand is not just keeping pace but leading the charge.</li>
<li><strong>Elevate customer experience</strong>: Tailor your strategies to evolving customer needs, providing an elevated and personalized experience that resonates.</li>
</ul>
<p>As we end the geo-testing series, we invite you to consider the impact of geo-testing on your marketing endeavors. Precision is not just a strategy; it's a journey, and with each geo experiment, you refine your path to excellence.</p>
<p><br />With that being said, you can take your first step towards geo-testing by requesting for a <a href="https://www.lifesight.io/demo">personalized demo with Lifesight</a> which offers fully automated geo-testing capabilities.</p>

Geo-experiments Part 3_ How to Deploy and Manage Your Geo Test.webp

Unlock the power of Geo-Experiments: Analyze, validate, and maximize business impact. Embrace continuous testing for strategic marketing growth.

Geo-Experiments Part 3: Analyzing and Reporting Your Geo Experiments

<p>This is the second part of our Geo-Experiments 101 series. In the first part, we discussed the basics of geo-experiments, why you should care, and how you can design your first geo-experiment. If you have not read it we encourage you to read <a href="https://www.lifesight.io/blog/geo-experiments">part 1</a>.</p>
<p>In the second part, we will be delving into deploying and managing a geo-test, market selection strategies, and shed some light on market size and testing volume.</p>
<p>Let's get started.</p>
<h2>Deploying Your Geo-Test: Putting Your Plan into Action</h2>
<p>The deployment phase is where your strategies move from the drawing board to real-life action. How you put your plans into action can have a significant impact on the success of your geo test. We&rsquo;ll explore three deployment approaches, each suitable for different levels of testing sophistication.</p>
<h3>1. Manual Deployment (Beginner):</h3>
<p>At the beginner level of geo experiments, deployment is a hands-on, manual process. You've designed a test where you'll run Google Ads in one set of markets (test group) and pause them in another set (control group). Here&rsquo;s how manual deployment might play out:</p>
<ul>
<li>You access your Google Ads account</li>
<li>You manually select and pause specific campaigns in the control group markets</li>
<li>You adjust ad spend by manually editing budgets for the test group markets</li>
<li>You're cautious but still concerned about human error, so you double-check your changes</li>
</ul>
<p>The manual deployment process in this case is time-consuming and susceptible to errors. Let's say, for example, you forgot to pause a campaign in one control market. This would lead to inconsistent test conditions, potentially skewing your results. Furthermore, because you're running a test in a significant portion of the country, you're taking a substantial risk in terms of lost sales during the testing period.</p>
<h3>2. Semi-Manual Deployment (Intermediate)</h3>
<p>As you progress to the intermediate level, you aim for more efficient deployment. You export inclusion and exclusion lists to ad platforms for your campaigns, which means your process looks like this:</p>
<ul>
<li>You prepare inclusion and exclusion lists for both test and control markets</li>
<li>You export these lists to an ad platform like Google Ads, applying the changes across campaigns</li>
<li>The changes are made more quickly and with less chance of manual errors</li>
</ul>
<p>With semi-manual deployment, you've streamlined the process and reduced the risk of manual mistakes. However, you're still overseeing the deployment process, and human intervention is involved.</p>
<h3>3. Automated Deployment (Advanced):</h3>
<p>The pinnacle of sophistication in deploying geo tests is the fully automated approach. In advanced geo-experiments, you leverage automated deployment technology to ensure precision and efficiency. The key driver behind this automation is the use of Application Programming Interfaces (APIs).</p>
<ul>
<li>You set up your tests in a geo-testing platform like <a href="https://www.lifesight.io/incrementality-testing">Lifesight</a> equipped with automation</li>
<li>You define your test parameters and schedule the tests with a few clicks</li>
<li>The platform interfaces with an ad platform like Google Ads through APIs, making changes to your campaigns on scheduled dates.
</li>
<li>The entire process is executed swiftly and accurately</li>
</ul>
<p>In the advanced deployment approach, you launch multiple geo tests in different regions almost simultaneously. With the aid of automation, you avoid the risk of human errors entirely. This means you can focus on running and analyzing your tests with confidence, knowing that the deployment process is seamless and error-free.</p>
<p>The business impact becomes significantly minimized with automated deployment. You can efficiently test various markets without the need for extended cooling-off periods or concerns about substantial sales losses thereby helping you to not only save time but also enhance the reliability of your geo-experiments.</p>
<p>To summarize, here's a table listing the three deployment methods along with their pros and cons:</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Table_listing_the_three_deployment_methods_along_with_their_pros_and_cons_5cd52bccc6.webp) 

<h2>A Note on Market Selection Strategies</h2>
<p>Selecting the right geographic areas for your geo-experiments is like choosing the perfect ingredients for a recipe. Just as the right mix of ingredients can make or break a dish, the choice of test markets can significantly impact the success of your advertising experiments. In this section, we'll explore the importance of market selection and the strategies to ensure your test markets accurately reflect national performance.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Geographic_Areas_for_Your_Geo_Experiments_cbee3c420d.webp) 

<h3>The importance of choosing the right geographic areas</h3>
<p>Why does market selection matter so much you ask? Well, the answer lies in the heart of what geo-experiments aim to achieve: measuring the true, incremental impact of your advertising efforts. To obtain meaningful insights, your test markets must be representative of your broader target audience.</p>
<p>If you choose the wrong geographic areas, your results may be skewed, leading to inaccurate conclusions.</p>
<p>Firstly, the primary goal of geo-experiments is to make data-driven decisions about advertising investments. If your test markets don't mirror the behaviors of your national audience, the insights derived may not be valid. Inaccurate results can lead to misguided budget allocation.</p>
<p>Furthermore, marketers rely on the results of geo-tests to make strategic decisions. Choosing test markets that are unrepresentative can lead to false conclusions. These misinterpretations can result in wasted ad spend, missed opportunities, or incorrect strategy shifts.</p>
<p>Finally, conducting geo-experiments consumes resources, including time and budget. By ensuring the right market selection, you maximize the utility of these resources and reduce the chances of resource wastage due to inaccurate or unrepresentative tests.</p>
<h2>Market Size and Testing Volume</h2>
<p>For running geo-experiments, the size of your test markets is more than just a logical detail. It's a critical factor that can significantly impact the accuracy, efficiency, and overall success of your advertising experiments.</p>
<h3>Why does test market size matter?</h3>
<p>The size of your test market matters for several reasons, however, the most prominent ones are</p>
<ol>
<li>Representativeness</li>
<li>Resource management</li>
<li>Testing volume</li>
</ol>
<p>For your geo-experiments to provide meaningful insights, your test markets must closely resemble your national audience. Larger test markets increase the risk of introducing non-representative elements that can skew your results.</p>
<p>Also, running experiments in larger markets can be resource-intensive. It requires allocating a significant budget and workforce to ensure smooth execution making efficient resource management crucial for long-term sustainability.</p>
<p>However, testing volume is the most important reason why market size matters. The size of your test markets directly affects how many experiments you can run concurrently. Smaller test markets provide the flexibility to test multiple hypotheses simultaneously, helping you gather insights more quickly.</p>
<h3>Ensuring you run multiple tests concurrently</h3>
<p>Running multiple tests concurrently is a strategic advantage that can significantly enhance the efficiency of your geo-experiments. Here's how you can do it:</p>
<h4>1. Avoid contamination</h4>
<p>Contamination occurs when two or more tests overlap in the same markets, making it difficult to attribute results to a specific test. To run multiple tests simultaneously, avoid overlapping test markets.</p>
<h4>2. Advanced tools</h4>
<p>Leverage advanced tools like Lifesight that support concurrent testing. Such a tool often includes automated design, deployment, and reporting features that streamline the testing process.</p>
<h4>3. Segmenting your audience</h4>
<p>Consider segmenting your audience into distinct groups. By doing so, you can run different tests for each segment concurrently, allowing for a more comprehensive testing approach.</p>
<h2>In the next part ...</h2>
<p>Well, that was the end of the second part of our Geo experiments series. We&rsquo;ve taken you through the journey from manual deployment to semi-manual deployment and finally, the advanced world of automated deployment.</p>
<p>Now, we're excited to guide you into the third and final part of our series, where we'll explore the analysis and reporting phase. Here, you'll discover the secrets of measuring incrementality, validation, maximizing business impact, and why continuous testing is crucial.</p>
<p>Meantime if you want to see how you can design a geo experiment on Lifesight, <a href="https://www.lifesight.io/demo">sign up for a free demo</a> today.</p>

Deploy and Manage Geo Experiments Test.webp

Everything you need to know about deploying and managing a geo experiment for your brand.

Geo-experiments Part 2: How to Deploy and Manage Your Geo Test ?

Geo-experiments Part 2: How to Deploy and Manage Your Geo Test

<p>Data is the lifeblood of any organization. From delivering personalized customer experiences to driving engagement and boosting profitability, everything hinges on data. CDPs (Customer Data Platforms) have made the handling of this data more cohesive, streamlined, and seamless. However, as the CDP ecosystem has evolved, different approaches to building and deploying these platforms have emerged. Two of the most discussed models in the current debate are "Packaged CDPs" and "Composable CDPs." What sets them apart, and how should enterprises choose the ideal fit?</p>
<p>Packaged CDP presents a ready-to-deploy framework, bundling together functionalities and tools tailored for specific use cases or industries. Whereas Composable CDPs offer a more adaptable, component-based structure, empowering businesses to select the elements that align with their objectives.&nbsp;</p>
<p>It's essential to factor in the requirements of your data and marketing teams when choosing the preferred CDP for your business. These teams, though interlinked, have unique roles and demands concerning customer data utilization. In this blog, we will delve deeper into the nuances of each, providing insights to help you chart the best path forward in your data strategy journey.</p>
<h2>What is CDP in marketing?</h2>
<p>David Raab first coined the term in his 2013 blog post, <strong><em>'I've</em></strong><a href="https://customerexperiencematrix.blogspot.com/2013/04/ive-discovered-new-class-of-system.html"><strong><em> Discovered a New Class of System: the Customer Data Platform</em></strong></a><strong><em>.'</em></strong></p>
<p>At its core, CDP in marketing refers to software that collects, centralizes, and organizes customer data from various sources to create a unified or 360-degree view of customer profiles. Other marketing tools and systems can use this unified profile to provide more targeted and personalized customer experiences. An <a href="https://cdp.com/basics/cdp-industry-statistics/#:~:text=CDP%20Industry%20Size%20and%20Growth&amp;text=As%20of%20July%202022%2C%20CDP,with%20a%20span%20of%20offerings.">IDC</a> research indicates that 65% of CDP use cases are front office, such as real-time personalization, lead generation, campaign execution, and measurement.&nbsp;</p>
<h2>What are Packaged CDPs?</h2>
<p>Consider that you have two ways of constructing a LEGO castle set. The first way is to buy a pre-packaged LEGO castle set with a specific design, instructions, and all the bricks you need to build that one castle. This is similar to a Packaged CDP; everything is predefined, and you know exactly what the outcome will look like. We will discuss the other way later in the article.</p>
<p>A packaged CDP, also known as a bundled CDP, is a conventional ready-to-use solution. As described by the CDP Institute, <em>it is "a software system that establishes a consistent, unified customer database, accessible to other platforms.</em>" A few packaged CDPs provide tools for setting privacy laws, implementing data governance procedures, and defining data quality guidelines.</p>
<p>Packaged CDP requires storing a duplicate of the data it collects to resolve identities (ID resolution) and create unified user profiles. Yet, the approach to identity resolution, whether probabilistic or deterministic, differs among vendors. Typically, a Packaged CDP provider allows businesses to craft their packages by blending fundamental features with supplementary tools.</p>
<h2>The advantages of Packaged CDPs</h2>
<p>These systems integrate foundational capabilities with business features, aiming to provide a holistic solution for collecting, consolidating, segmenting, managing, and leveraging customer data.</p>
<p>For companies that have yet to invest heavily in infrastructure or have significant gaps in any of the above areas, a packaged CDP is a compelling option. It offers swift benefits and caters to essential business cases dependent on customer data. Here are a few of them:</p>
<ul>
<li><strong>Quick implementation: </strong>As an all-in-one product solution, Packaged CDPs can be deployed swiftly, allowing businesses to benefit from their features without a lengthy setup period.&nbsp;</li>
<li><strong>User-friendly: </strong>With pre-configured settings and intuitive user interfaces, they are more accessible for marketing teams without extensive technical expertise.</li>
<li><strong>End-to-end functionality: </strong>For brands that haven't heavily invested in Customer Experience (CX) architecture, a bundled CDP can fill the gap. It delivers essential functionalities that would otherwise demand significant development effort, providing a comprehensive solution without the need for intricate in-house development.</li>
<li><strong>Ideal for diverse brand profiles:</strong> By centralizing data and audiences, it offers immediate value, ensuring consistent data handling and marketing strategies for organizations with multiple brands.</li>
</ul>
<h2>How do Packaged CDPs work?</h2>
<p>Packaged Customer Data Platforms (CDPs) leverage cloud-based data warehouses to manage and process customer information. The process begins with data collection, where these CDPs efficiently gather information from diverse sources like CRMs, websites, apps, POS systems, email, and social media. They handle various data types, including transactional, behavioral, and demographic customer details.</p>
<p>The next step is data unification, where the CDP breaks down data silos, merging info from various platforms to create a cohesive customer profile. Identity resolution is a vital part of this, as the CDP identifies and links different identifiers, such as email addresses and device IDs, to consolidate user interactions across various channels.</p>
<p>For data storage, the CDP houses this amalgamated customer data in a dedicated database, maintaining the information until actively removed, which assures a durable and reliable customer view. This centralized data then allows for sophisticated segmentation, providing businesses with the means to categorize their audience for more precise and personalized marketing outreach.</p>
<p>Lastly, data syncing is crucial; packaged CDPs are equipped with built-in integrations for a range of APIs, facilitating seamless data transfer to essential business tools like marketing automation, CMS, and advertising platforms, streamlining the entire process from data collection to campaign execution.</p>
<h2>What are Composable CDPs?</h2>
<p>Coming back to our LEGO castle set example. The second way to build it is by having a collection of individual LEGO bricks of various shapes, sizes, and colors. You don't have a fixed design in mind, but instead, you have the freedom to pick and choose each brick, placing them wherever you like based on your creative vision. You can even modify your castle over time, adding towers, bridges, or whatever else you imagine. Composable CDPs follow a similar approach.</p>
<p>They consist of interconnected tools built from either open-source or proprietary software. Depending on its configuration, a composable CDP might possess all, or only some, of the features found in a packaged CDP. These platforms utilize open-source software, managed versions of open-source software, or specialized SaaS tools.</p>
<p>Instead of offering a fixed set of features in an all-in-one package, composable CDPs allow organizations to build, customize, and adapt their CDP based on their unique needs by choosing from a set of components or modules. They enable a single source of truth across the entire organization, greater flexibility, improved data security, and greatly reduced launch times.&nbsp;</p>
<h2>The Advantages of Composable CDPs</h2>
<p>Initially, CDPs gained popularity for tackling a fundamental issue in the martech space, bridging the divide between data and marketing teams. Yet, their closely-knit architecture also introduced additional intricacies. Composable CDPs present a solution to this dilemma, bringing along numerous benefits.</p>
<ul>
<li><strong>Modularity:</strong> You gain complete autonomy in selecting the technologies and methodologies for data collection, storage, modeling, and activation. This empowers you to design your architecture to align with specific organizational goals.</li>
<li><strong>Flexibility:</strong> Composable CDPs provide instant access to your entire data spectrum, extending beyond just clickstream events. This facilitates the utilization of various data types, enabling you to address the wide-ranging complexities of your use case.</li>
<li><strong>Compatibility:</strong> Being technology-agnostic, a Composable CDP seamlessly integrates with any data framework, ensuring adaptability to prospective infrastructure shifts and preventing technological debt and vendor constraints.</li>
<li><strong>Future-Proofing: </strong>With the ability to continually modify and upgrade components, companies can ensure their CDP remains relevant and up-to-date in the face of future technological advancements.</li>
</ul>
<h2>How do Composable CDPs work?</h2>
<p>A Composable CDP isn't merely a black box that stores and manages your data. It lets you leverage your prevailing data collection, storage, and modeling methods.</p>
<p>Visualize a Composable CDP as the facilitator for audience engagement and management. It empowers you to create audience segments using first-party and third-party data and integrate them seamlessly with your essential marketing tools. This could range from advertising platforms and email systems to comprehensive lifecycle or multichannel marketing tools.&nbsp;</p>
<p>Its technology-agnostic nature ensures versatility, irrespective of where your data resides be it in a data warehouse, data lake, operational database, or even a simple spreadsheet. Moreover, this activation interface overlays your current system architecture without retaining data.</p>
<p>Lastly, from a technical standpoint, data activation platforms operate through Reverse ETL (Extract, Load, Transform), a process of transferring data from your existing data stores and syncing it to your operational systems.</p>
<p><strong>Making the choice: Factors to consider</strong></p>
<p>When deciding between a packaged or composable CDP, it's crucial to weigh the unique advantages each brings against the specific needs and capabilities of your organization. Here are some key factors:</p>
<table>
<tbody>
<tr>
<td>
<p>Factors To Consider</p>
</td>
<td>
<p>Packaged CDP</p>
</td>
<td>
<p>Composable CDP</p>
</td>
</tr>
<tr>
<td>
<p>Business size and complexity</p>
</td>
<td>
<p>Small to mid-sized enterprises with simpler processes will prefer bundled CDPs since they are easier to set up and operate.</p>
</td>
<td>
<p>Larger enterprises with complex data needs will favor composable CDPs due to their flexibility in integrating various data sources and technologies.</p>
</td>
</tr>
<tr>
<td>
<p>Customization needs</p>
</td>
<td>
<p>Offers limited customization, sufficient for organizations with standard data management needs.</p>
</td>
<td>
<p>Provides extensive customization options, catering to unique business requirements and data workflows.</p>
</td>
</tr>
<tr>
<td>
<p>Implementation Timeline</p>
</td>
<td>
<p>Typically, it is quicker to implement, given its out-of-the-box functionality.</p>
</td>
<td>
<p>Require a longer implementation timeline due to the customization involved.</p>
</td>
</tr>
<tr>
<td>
<p>Internal expertise and resources</p>
</td>
<td>
<p>Requires less internal expertise due to its user-friendly design and predefined functionalities.</p>
</td>
<td>
<p>Demands a higher level of Technical expertise and resources for setup, customization, and maintenance.</p>
</td>
</tr>
<tr>
<td>
<p>Budget and cost implications</p>
</td>
<td>
<p>Usually more cost-effective upfront, but could lead to extra expenses down the line.&nbsp;</p>
<br />
<p>For example, organizations might end up purchasing a broad suite of features while only requiring a fraction of the functionality provided.</p>
</td>
<td>
<p>Requires a more significant initial investment than Packaged CDP.</p>
<br /><br /></td>
</tr>
<tr>
<td>
<p>Data privacy and compliance&nbsp;</p>
</td>
<td>
<p>The level of scalability and adaptability is conditional on the specific vendor selected.&nbsp;</p>
</td>
<td>
<p>Offers a flexible architecture that can evolve with your business needs.</p>
<br /><br /></td>
</tr>
</tbody>
</table>
<p>Arpit Choudhury, a data and growth expert, sheds light on what questions a business must answer before deciding on a CDP.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Linked_In_Post_on_what_questions_a_business_must_answer_before_deciding_on_a_CDP_cefb28a7be.webp) 

<p><a href="https://www.linkedin.com/feed/update/urn:li:activity:7049435848240504832?updateEntityUrn=urn%3Ali%3Afs_feedUpdate%3A%28V2%2Curn%3Ali%3Aactivity%3A7049435848240504832%29">Source</a></p>
<h2>Conclusion</h2>
<p>Choosing the right CDP strategy can be an intimidating task, given its invaluable role in effectively harnessing customer data, enhancing customer experiences, and driving higher ROI. The absence of a precise definition and workflow for CDPs can add to the difficulty of identifying the solution that best suits your organization's distinct needs.</p>
<p>Going ahead with Composable CDPs will definitely need a capable data team that can weave all the requisite components together, which can ultimately be a labor-intensive task. On the other hand, Packaged CDPs are synonymous with simplicity and ease, providing a more straightforward approach to managing customer data.</p>
<p>Explore <a href="https://www.lifesight.io/cdp">Lifesight</a> today to discover how you can take your customer data activation and marketing measurement with a flexible solution.</p>

Packaged CDP vs Composable CDP_ What are the differences_.webp

Uncover the distinctions between Packaged CDP and Composable CDP in our insightful comparison. Choose the right solution for your needs.

Packaged CDP Vs Composable CDP: What Are the Differences ?

<p>Welcome to the world of geo-experiments, a word that appears to be coming from geographers. But it doesn&rsquo;t.</p>
<p>It is where the science of marketing meets the art of analytics.</p>
<p>In today&rsquo;s digital age, where privacy regulations are reshaping the advertising landscape, geo-experiments have emerged as a powerful tool for savvy marketers to navigate this dynamic terrain.</p>
<p>Picture this: You&rsquo;re responsible for allocating a substantial chunk of your company&rsquo;s budget to various advertising channels like Facebook, Google, TV, or catalog ads. Your mission?</p>
<p>To figure out how much of an impact these ads truly make on your bottom line.</p>
<p>In essence, you need to answer the question: &ldquo;<em>If we were to pull the plug on our ads, what would we stand to lose?</em>&rdquo; That&rsquo;s where geo-experiments come into play.</p>
<p>In this series, we&rsquo;ll delve into the intricacies of geo-experiments. We&rsquo;ll cover the essential steps, strategies, and tools that can help marketers like you gain a deeper understanding of your advertising&rsquo;s true impact. Whether you&rsquo;re a beginner or an expert, the world of geo-experiments has something for everyone.</p>
<p>This is Part 1, where we talk about designing your geo experiments.</p>
<h2>The Basics of Geo-Experiments: Navigating the Advertising Landscape</h2>
<h3>Geo Test 101</h3>
<p>At its core, a geo test, or geo-experiment, is a marketing research method that allows you to analyze the real impact of your advertising efforts within specific geographic markets. In simpler terms, it&rsquo;s like running a controlled science experiment in the world of advertising.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image1_1107f1ed80.webp) 

<p>Here's how it works: you choose a particular geographic area or market where you plan to alter your advertising strategy in some way. This might involve increasing or decreasing your ad spend in that area, targeting a different audience, or even halting your advertising altogether. You then only closely monitor the changes in sales, sign-ups, downloads, or any other relevant key performance indicators (KPIs) within this market. This data helps you understand the true effect your advertising has on your desired outcomes.</p>
<h3>Why Should You Care?</h3>
<p>If you're a marketer, you should care about geo tests because they can be your guiding light in the complex and ever-changing landscape of advertising. Here&rsquo;s why:</p>
<ol>
<li><strong>Accuracy in Measurement</strong>: In the age of privacy regulations and the diminishing reliability of traditional measurement methods, geo tests provide an ethical and accurate way to measure the true impact of your ads. They don&rsquo;t rely on invasive tracking or complex attribution models, which makes them invaluable.</li>
<li><strong>Data-Driven Decisions</strong>: Geo tests empower you to make data-driven decisions. Instead of guessing the impact of your advertising, you can have clear, actionable insights that guide your budget allocation and advertising strategies.</li>
<li><strong>Cost Savings</strong>: By understanding the actual impact of your ads, you can optimize your advertising budget. You'll know exactly where to allocate your budgets, avoiding wastage and maximizing your return on investment.</li>
</ol>
<h3>The Role of Geo Tests in Measuring Advertising Impact</h3>
<p>Now, you might wonder how geo tests fit into the grand scheme of measuring advertising impact. Traditional methods like multi-touch attribution (MTA) are losing their effectiveness due to stricter privacy regulations. Geo-tests fill this gap.</p>
<p>They serve as a reliable tool to measure casual impact or &ldquo;incrementality.&rdquo; This concept is at the heart of advertising impact assessment. It helps you answer a fundamental question: How much of your KPIs can be attributed to your advertising efforts, and how much would you have achieved without them?</p>
<p>They enable you to differentiate between the audience that was exposed to your ads and the audience that would have acted the same way without them.</p>
<h2>Designing Your Geo Test</h2>
<p>Designing your geo test is like setting up the foundation for a successful experiment. It&rsquo;s where you decide the who, what, and how of your test. Depending on your level of expertise, you can approach it from a beginner, intermediate, or advanced perspective.</p>
<h3>1. Beginner-Level Design</h3>
<p>If you&rsquo;re new to geo tests, it&rsquo;s wise to start with a straightforward approach. Here's what it involves:</p>
<p><strong>Market Selection Strategy:</strong> At the beginner level, you&rsquo;ll rely on readily available data from advertising platforms. You might choose test markets based on demographics, sales data, or other basic criteria. However, this approach has some limitations. Your results may not be entirely reflective of your broader marketing and sales performance because they could include &ldquo;outlier&rdquo; markets, ones that don&rsquo;t behave like your typical audience.</p>
<p><strong>Market Size</strong>: When it comes to market size, you&rsquo;ll often opt for a 50/50 split. This means about half of your business will fall into a test geography. While this can give you a clear picture of your advertisings impact, it comes with the risk of substantial sales loss in the test market.</p>
<p><strong>Testing Volume</strong>: With the 50/50 market split, you'll typically run one test at a time, as running multiple tests in the same market can lead to inconclusive results due to contamination. You&rsquo;ll need to make several assumptions about the comparability of your test and control groups, which can be a challenge.</p>
<h3>2. Intermediate-Level Design</h3>
<p>Once you've gained some experience, you can level up your test design:</p>
<p><strong>Market Selection Strategy</strong>: Here, you&rsquo;ll move towards matched-market testing. It involves selecting test/control market pairs that closely correlate with your national sales for specific business KPIs. This approach significantly reduces the chances of outlier markets skewing your results.</p>
<p><strong>Market Size</strong>: With this level of testing, you won&rsquo;t need to test such a large percentage of your business. Tests can be conducted in smaller, more precise markets&ndash;between 10-25% of your country. This reduces sales loss and lets you run multiple tests simultaneously without contamination risks.</p>
<p><strong>Testing Volume</strong>: Intermediate-level marketers can handle multiple tests at once, thanks to more focused market selection. However, designing the test, deploying it, and analyzing the results will still require substantial manual effort.</p>
<h3>3. Advanced Design</h3>
<p>When it comes to advanced-level design for geo-tests, the approach takes a significant leap in precision and efficiency:</p>
<p><strong>Market Selection Strategy</strong>: At the advanced level, market selection is a highly refined process. It leverages big data analysis, machine learning, and predictive models based on control markets. This allows for a deep understanding of markets that closely resemble your national sales for specific business KPIs. The advanced design ensures your test and control markets are nearly identical in behavior. This reduces the risk of having outliers that could skew your results.</p>
<p><strong>Market Size</strong>: Market size becomes remarkably small. Advanced testing can operate with as little as 5-10% of markets in a treatment group. This means minimal risk to the business, with far fewer sales foregone during the testing period. The ability to conduct multiple tests simultaneously is maximized without any concerns of contamination.</p>
<p><strong>Testing Volume</strong>: Advanced design enables a significant increase in the number of tests you can run concurrently. By leveraging fully automated processes, including rapid deployment and reporting, you can execute tests with the highest level of certainty and efficiency. This level of testing requires less time, effort, and resources, and results are always accurate and precise.</p>
<p>Here's a table summarizing how the three approaches differ while designing geo-experiments:</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image2_6c731db98d.webp) 

<h2>In the next part</h2>
<p>As you move on to Part II, we&rsquo;ll explore the critical phase of deploying your geo test. This is where your test becomes a reality and where errors and inefficiencies could potentially impact the success of your experiments. Well compare manual deployment, semi-manual deployment, and the advanced realm of automated deployment, uncovering the strategies that make your tests truly actionable.</p>
<p>Meantime if you want to see how you can design a geo experiment on Lifesight, <a href="https://www.lifesight.io/demo">sign up for a free demo</a> today.</p>

How to Design Your Geo Test.webp

Discover the art of designing geo experiments in Part 1 of our series. Perfect your geo test for data-driven insights.

Geo-experiments Part 1: How to Design Your Geo Test?

Marketing Mix Modeling

<p>How prepared are you for the post-privacy era? Years after the GDPR and CCPA were implemented, internet behemoths like Apple and Google listened and are now limiting user-level tracking. With the emphasis on data protection expanding in the digital age, marketers must future-proof their marketing measurement plan to remain competitive. This is where MMM comes into play.</p>
<h2>Behind the acronym: What is MMM all about?</h2>
<p>Gathering data at the user level is increasingly tricky, nudging marketers to adopt Marketing Mix Modeling. This approach, built on machine learning algorithms, is a top-down approach that leverages historical data to measure the impact of various marketing activities on conversions.</p>
<p>Originating in the 1950s and resurging in the 1980s, MMM was once exclusive to large corporations due to its complexity. Today, businesses of all sizes are turning to MMM for growth, especially in the new privacy-focused landscape. This shift is deeply linked to the proficiency machine learning models have adopted and the pace with which they&rsquo;re able to recreate models.</p>
<h2>Why MMM is the answer to modern marketing woes?</h2>
<p>With the rise of GDPR and Apple's ATT policy, marketers are grappling with internet tracking limitations with a huge blank space on accurate metrics to draw up their next budgets. This has led to the growing prominence of Marketing Mix Modeling (MMM) as a reliable solution - using a business&rsquo;s owned data to build projections that are reliable, thorough &amp;, most of all, fast to build &amp; deploy.</p>

<a href="https://www.lifesight.io/events/modern-marketing-measurements-mmm"> ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image1_6d9b9ffc1f.webp) </a>

<h2>Building with confidence using Lifesight MMM</h2>
<p>Marketing Mix Modeling has long been a cornerstone for marketers looking to determine the effectiveness of their advertising channels. Lifesight&rsquo;s MMM elevates this to a new level by employing robust machine learning algorithms that not only provide a panoramic view of marketing efforts but also assess the value of each advertising channel with precision.</p>
<p>By implementing Lifesight&rsquo;s MMM, marketers gain the ability to:</p>
<ul>
<li>Harness granular insights: With Lifesight&rsquo;s advanced analytics, marketing professionals can dive deep into data, gaining actionable insights that drive strategic decision-making.</li>
</ul>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image3_1f8fd2f241.webp) 

<ul>
<li>Measure with accuracy: Our marketing measurement model (MMM) utilizes machine learning techniques to analyze extensive datasets spanning years of ad interaction data, enabling precise projections for channel effectiveness.</li>
</ul>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image6_41ba06b8c9.webp) 

<ul>
<li>Prioritize spending: The system helps in mapping predicted KPIs against actuals, enabling efficient allocation of resources and fine-tuning channel spending to maximize ROI. It also helps you assess the accuracy of the marketing mix model created for you, so you know the insights provided by the platform are reliable and actionable.</li>
</ul>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image2_ba95e5617c.webp) 

<h2>What&rsquo;s New: Budget Optimizer</h2>
<p>In the quest for optimal budget allocation, Lifesight&rsquo;s Budget Optimizer is a game-changer. This tool removes the guesswork from budgeting, allowing marketers to input proposed budgets and let the tool&rsquo;s algorithms project the impact.</p>
<p>The Budget Optimizer empowers marketers to:</p>
<ul>
<li>Predict with precision: Advanced machine learning crafts a tailored media mix to ensure that your budget is spent effectively.</li>
</ul>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image4_cc23360732.webp) 

<ul>
<li>Focus on ROI: The tool&rsquo;s ROI-centric approach means every dollar is scrutinized for its potential return, driving better budgeting decisions, while avoiding the drawbacks of ad saturation</li>
</ul>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image5_7bdcd88b10.webp) 

<h2>Feature Deep Dive</h2>
<p>Lifesight's MMM and Budget Optimizer work together to reduce advertising costs with a wide range of features. The platform automatically integrates with your existing data systems to provide a complete view of your marketing efforts from all sources.</p>
<p>Each MMM model isn't just off-the-shelf; it's highly customizable, allowing you to align it precisely with your organizational objectives, seasonal market shifts, and specific industry conditions. This customization ensures that every model is directly relevant to your unique business context and crafted for pinpoint accuracy in its predictive power.</p>
<p>The predictive analytics component takes the guesswork out of forecasting, providing you with clear foresight into emerging market dynamics and consumer behaviors. Armed with this knowledge, you can proactively pivot your channel strategies to capitalize on upcoming opportunities and mitigate potential risks.</p>
<p>Lifesight's scenario planning feature invites you to consider 'what-if' situations, enabling you to simulate and evaluate various budgeting options. This foresight empowers you to distribute your marketing spend more intelligently, ensuring that every dollar is put to work effectively.</p>
<p>Moreover, the dashboards facilitate the simplification of complex data sets, enhancing the understanding of key performance indicators (KPIs), analyzing engagement levels, and enabling more informed marketing decision-making.</p>
<h2>The Bottom Line</h2>
<p>Overall, Lifesight's MMM and Budget Optimizer improve advertising expenditure efficiency. We help marketers make data-driven decisions that resonate with their audiences and optimize spending for the best returns by combining deep insights with user-friendly tools. As we continue to develop features that address the needs of modern marketers, Lifesight remains committed to helping brands navigate the complexities of today&rsquo;s marketing landscape with ease and precision.</p>

Feature Release_ Optimizing Ad Spend With Lifesight’s MMM & Budget Optimizer.webp

Unlock efficient ad spend optimization with Lifesight's MMM and Budget Optimizer feature release. Maximize ROI effortlessly.

Feature Release: Optimizing Ad Spend With Lifesight’s MMM & Budget Optimizer

<p>Tiktok, with its immersive full-screen and diverse video content, has struck a chord with creators, viewers, brands, and advertisers alike.</p>
<p>What's more, it has become one of the most effective promotional platforms, with <a href="https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/">1081 million</a> monthly active users. For marketers, it makes perfect sense to capitalize on TikTok ad campaigns to boost their reach across this user base.</p>
<p>Like any promotional campaign, TikTok advertising also needs an effective tool to measure the overall performance. But the problem is that measuring the success of advertising campaigns on TikTok is not as straightforward as other social media platforms and digital channels. The metrics (conversion rate, click-through rate, and cost per action) do not give a clear picture of the impact of the campaign.</p>
<p>TikTok creates more of a passive impact in driving sales and measuring the impact of TikTok ads on overall ROI is complicated.</p>
<p>Thats why an increasing number of brands are leaning towards <a href="https://www.lifesight.io/blog/marketing-mix-modeling-importance">marketing mix modeling (MMM)</a> to measure the performance of their TikTok ads.</p>
<p>MMM focuses on a holistic approach and analyzes non-marketing data like recession, political trends, and consumer behavior to identify TikTok ads role in your brand&rsquo;s overall growth.</p>
<p>If you're a marketer who's looking to understand how to track TikTok advertising performance with media mix modeling, we have written this article for you.</p>
<h2>Why is it Hard to Measure TikTok Ads Effect on Sales?</h2>
<p>While <a href="https://www.statista.com/statistics/1305708/tiktok-ad-revenue/">TikTok's ad revenue</a> is projected to jump to $33 billion by 2025, measuring TikTok's ad performance is challenging. Especially for brands in the ecommerce and DTC sectors, the traditional metrics are unable to paint a clear picture.&nbsp;</p>
<h3>1. TikTok's role in customer journeys is nonlinear</h3>
<p>While number of clicks&rdquo; is one of the famous metrics to measure ad campaign performances on platforms like Facebook and Instagram, the situation is slightly different for TikTok. Click is a suitable ad KPI in scenarios where the customers are looking for a product and click on a website intending to make a purchase, like say on a marketplace like Amazon.

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image4_266f202b78.webp) 
</p>
<p><a href="https://brands.joinstatus.com/tiktok-customer-journey-data">Source</a></p>
<p>However, the purchase journey on TikTok is not linear. So it builds the customers' interest while they are entertained with a bite-sized dance video. That means users don&rsquo;t always jump to purchase a product immediately after watching a video or viewing a TikTok ad. It might create a demand in their minds, and they might decide later.</p>
<p>TikTok plays a role in driving online shopping, but there is no exact way to measure its impact on a customer's journey.</p>
<p>Other platforms like Instagram encourage users to click on a post or visit the linked website, but TikTok is based on endless scrolls. Users transition from one video to another, but pinning down a single point of engagement is nearly impossible. That's why traditional advertising metrics like click-through rates (CTR) become less relevant for TikTok.</p>
<p>Another challenge for advertisers is that even if the user likes the video, they may not pause or engage directly, but rather continue their scrolling journey. For these reasons, measuring the depth of engagement or interest based on standard metrics becomes challenging.</p>
<p><em>This means the last-click attribution that heavily relies on the final interaction does not work here as it neglects the collective influence of multiple engagements on the customer journey.</em></p>
<h3>2. Regular attribution tools are not effective for TikTok</h3>
<p><a href="https://www.marketingdive.com/news/tiktok-last-click-measurement-solution-campaign-conversions/694089/">Marketingdive</a> highlights that TikTok ads were undervalued in 73% of scenarios when it comes to driving purchases. Due to the unconventional TikTok algorithms and the fact that it doesn't fall under the &ldquo;click-and-buy bucket, regular multi-touch attribution (MTA) tools are not always effective in measuring TikTok advertisement performances.</p>
<p>Marketers must rely solely on TikTok&rsquo;s first-party attribution solution that provides insights on content views, customers actions (like if they added a product to a cart after watching content), checkout initiation, and completion.</p>
<p>So platforms like Google and other social media channels give instant gratification in the form of a click or app download. They provide tangible data that enables the brand to link the ad to a direct result like a sale or a new subscriber.&nbsp;</p>
<p>But TikTok isn&rsquo;t built for direct actions. In other words, some TikTok ads could prompt a click or swipe, but the platform&rsquo;s user behavior does not align with the traditional metrics. Brands cannot solely rely on the age-old metrics to determine the success of their advertising campaigns on TikTok.</p>
<h3>3. Different brands have different goals&nbsp;</h3>
<p>The effect of TikTok ads on your overall ROI is also related to your business goals and industry of operation.&nbsp;</p>
<p>For example, the goal of a game application will be to boost their downloads. In that case, when a TikTok ad appears in the feeds of their potential users, the scope of conversion is higher because they need to perform only one action (click on the download CTA), and there are no financial transactions involved.&nbsp;</p>
<p>However, for a DTC store, &ldquo;conversion&rdquo; means when a potential customer makes a purchase by getting influenced by an ad. The conversion scope is less here as users often tend to perform in-depth research before buying something. Here, the TikTok ad might be the first trigger but owing to the non-linearity of the purchase journey it cannot be directly linked to the action (app download, sale, or subscribe).&nbsp;</p>
<p>Other activities are likely to be involved, like going through the product reviews, checking out competitor products and even waiting for a sale, before they finally decide to buy it.&nbsp;</p>
<p>Estimating the performance of TikTok ad campaigns and stating their impact on overall sales is challenging.&nbsp;</p>
<p>Looking at the shortcomings of the existing TikTok advertising metrics ( like clicks), you must be thinking, is there a better way to measure the impact of your TikTok ads?&nbsp;</p>
<h2>Views on the Video are a More Accurate Metric Compared to Clicks&nbsp;</h2>
<p>This could be in the form of 2-second or 6-second video views.&nbsp;</p>
<h3>2-second video view&nbsp;</h3>
<p>When the viewer is scrolling through the feed, even a 2-second pause is a good indicator of the spark of interest. You&rsquo;ll be in a better position to understand and optimize these brief moments of connection for better results.&nbsp;</p>
<h3>6-second video views&nbsp;</h3>
<p>A major challenge for brands advertising on any channel is sustained engagement. A 2-second view indicates the user&rsquo;s interest whereas a 6-second view signifies engagement. Your brand can use this duration for a deeper connection, possibly giving a better idea of the product, message, or even a promotional offer.&nbsp;</p>
<h2>Applying MMM to TikTok Ads</h2>
<p>Marketing mix modeling (MMM) incorporates statistical methods like time series analysis to measure the impact of advertising campaigns on an organization&rsquo;s overall sales.&nbsp;</p>
<p>MMM is reliable for TikTok ad performance measurement because it emphasizes both regular ad metrics like clicks and content views as well as political and economic factors like seasonality and inflation to give a comprehensive understanding of what truly drives consumer behavior.&nbsp;</p>
<p>MMM media mix modeling is a multifunctional approach. Each function helps decode the different aspects of your brand&rsquo;s marketing strategy to give a well-rounded view of the performance. The functions include: base drivers, budget optimization, and spend efficiency analysis.&nbsp;</p>
<h2>What&rsquo;s the advantage of using MMM for TikTok?</h2>
<p>By now, it is clear that last-click attribution is ineffective for TikTok. Mix models, on the other hand, identify the underlying value of every customer interaction by evenly distributing attribution across multiple touchpoints. With appropriate media marketing mix models, you can quickly grasp TikTok's changing user engagements.&nbsp;</p>
<h2>How do modern MMM solutions generate deeper insights?&nbsp;</h2>
<p>Think of <a href="https://www.lifesight.io/blog/principles-of-marketing-mix-modeling">MMM principle</a> as a holistic, dynamic, and data-rich approach for brands to get critical insights into performance marketing. Let&rsquo;s look at the MMM from the lens of TikTok advertising.&nbsp;</p>
<h3>Campaign objectives and marketing mix modeling&nbsp;</h3>

<br/>
<p>The objectives of TikTok ads can range from:&nbsp;</p>
<p><strong>Awareness</strong> - At this stage, MMM measures if awareness converts into recall.</p>
<p><strong>Consideration</strong> - Since this TikTok advertising campaign is aimed to drive traffic or maximize video views, MMM can help track these interactions and measure the journey from interest to consideration.</p>
<p><strong>Conversion</strong> - Here, media mix modeling helps understand the ad&rsquo;s direct and indirect outcome in generating leads, promoting apps, or website conversions to understand the conversion pathway.</p>


 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image1_86a6589f7e.webp) 
<h3>Bidding Strategies and MMM&nbsp;</h3>
<p>TikToks bidding strategy allows brands to promote ads based on their budget and core objectives. Marketing mix modeling aligns with each of the three bidding strategies including bid cap, cost cap, and lowest cost to show the impact of each of the strategies on the campaign&rsquo;s overall performance. This way, your brand can refine the approach based on insights driven by the marketing models and ensure that the campaign is aligned with your brand&rsquo;s core objectives.</p>
<h2>How does MMM differ from Last-Click Attribution?&nbsp;</h2>
<table>
<tbody>
<tr>
<td>
<p><strong>Factors&nbsp;</strong></p>
</td>
<td>
<p><strong>MMM&nbsp;</strong></p>
</td>
<td>
<p><strong>Last-click attribution&nbsp;</strong></p>
</td>
</tr>
<tr>
<td>
<p>Primary difference&nbsp;</p>
</td>
<td>
<p>Multi-linear path that evaluates every marketing channel drives the consumer&rsquo;s journey.&nbsp;</p>
</td>
<td>
<p>Operates on a linear path and bypasses the multifaceted journey the modern user takes.&nbsp;</p>
</td>
</tr>
<tr>
<td>
<p>A greater understanding of how different marketing channels complement each other to make insights-driven marketing strategies.&nbsp;</p>
</td>
<td>
<p>Undervalues the influence of interactions such as review, video ads, or recommendations that shape the consumer&rsquo;s purchase decision.&nbsp;</p>
</td>
</tr>
<tr>
<td>
<p>Budget allocation&nbsp;</p>
</td>
<td>
<p>It provides a granular breakdown of the influence of each channel even if it has silently shaped the early or mid stages of the consumer journey.&nbsp;</p>
</td>
<td>
<p>Prefer optimizing the budget for the final touchpoint channel even if it has a minimal influence on the consumer journey.&nbsp;</p>
</td>
</tr>
<tr>
<td>
<p>Allows marketing teams to identify channels that have been instrumental in building brand awareness.&nbsp;</p>
</td>
<td>
<p>Neglect channels that play a less direct but important role in shaping consumer decisions.&nbsp;</p>
</td>
</tr>
</tbody>
</table>
<p>What&rsquo;s more, contrary to the <a href="https://www.lifesight.io/blog/marketing-mix-modeling-misconceptions">marketing modeling mix myths</a>, MMM pinpoints channels with diminishing returns so that you can redirect funds to other channels with untapped potential.&nbsp;</p>
<p>Check out this <a href="https://www.lifesight.io/resources/case-study-mmm-drives-15-percent-incremental-ROI">case study</a> to see how a well-known FMCG company used Marketing Mix Modeling to make better campaign investments and learn more about how each of their marketing platforms contributed in different ways.</p>
<h2>LifeSight's MMM Solution for Measuring TikTok Advertising Performance&nbsp;</h2>
<p>With automated MMM tools like <a href="https://www.lifesight.io/marketing-mix-modelling">Lifesight</a>, measuring TikTok advertising performance is rather easier, as compared to the confusing last-click attribution.&nbsp;</p>
<p>Here's how brands can leverage the MMM media mix modeling solution Lifesight:&nbsp;</p>
<ul>
<li>Lifesight's marketing mix model is trained with your brands historical ad performance data. It trains your model within 45 minutes, tests previous budget scenarios, and auto-allocates new budgets based on changing customer lifecycle.</li>
</ul>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image2_35e8bf1c53.webp) 

<ul>
<li>TikTok's confusing analytics is why marketers are often unsure about the ROI on ad spending on this platform. With Lifesight, you can find the answer to - how far you can push your budget to achieve your goals. MMM solution Lifesight draws a clear picture of your TikTok campaigns&rsquo; potential so you can prevent overspending, adjust your TikTok budget as per pre-defined KPIs, and allocate your budgets to the right kind of TikTok ads.</li>
</ul>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image3_9efb7eec09.webp) 

<ul>
<li>Lifesight's marketing mix models are auto-calibrated. It can create data feeds based on all available source integrations. Since your model is calibrated, you can generate outcomes of your TikTok ad campaigns faster and make data-driven decisions.</li>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image5_3912bf014b.webp) 
</ul>
<p><a href="https://www.lifesight.io/demo">Request a demo</a> to explore Lifesight.</p>
<h2>FAQs&nbsp;</h2>
<p><strong>What is a good CTR for TikTok ads?&nbsp;</strong></p>
<p>Any click-through rate of over 1% is considered a good CTR for TikTok ads.&nbsp;</p>
<p><strong>What is media mix modeling?&nbsp;</strong></p>
<p>Mix modeling is a statistical technique for measuring the performance of various advertising campaigns and their impacts on overall sales.&nbsp;</p>
<p><strong>How much do TikTok ads cost?&nbsp;</strong></p>
<p>TikTok ads cost $10 for generating every 10,000 views.&nbsp;</p>

Identifying the true value of TikTok ads using MMM.webp

Discover the true value of TikTok ads with Marketing Mix Modeling (MMM). Learn how MMM provides a comprehensive understanding of TikTok ad performance, surpassing traditional metrics. Understand the differences between MMM and last-click attribution, and explore how automated MMM tools like Lifesight can optimize your TikTok ad campaigns. Find insights to maximize your ROI on TikTok spending.

Identifying The True Value of TikTok Ads Using MMM

Identifying the true value of TikTok ads using MMM

<p>Have you noticed how countless discount notifications from ecommerce stores have started pouring in since the beginning of October?&nbsp;</p>
<p>It marks the beginning of a three-month-long festival season, starting with Halloween and followed by Thanksgiving, Christmas, Black Friday, and New Year. Customers around the world are willing to spend more during this time, and ecommerce stores capitalize on this opportunity.&nbsp;</p>
<p>While stores record new sales milestones through such seasonal campaigns, it becomes challenging for marketers to analyze the impact of these campaigns on overall ROI. To understand the effectiveness of your marketing strategies, you need to separate the sales driven by seasonal demand from those by increased marketing activities.&nbsp;</p>
<p>That&rsquo;s where seasonal marketing mix modeling (MMM) allows the modern marketer to dissect and understand the relationship between seasonal trends, marketing efforts, and sales.&nbsp;</p>
<p>Here, dive deeper into the challenges of seasonality analysis in marketing and how seasonal MMM can address them.&nbsp;</p>
<h2>Top Challenges Faced by Marketers in Seasonality Analysis&nbsp;</h2>
<p>Seasonality must be distinguished from other influential factors and this process can be fairly straightforward or extremely complex.</p>
<p>Some of the significant challenges associated with seasonality analysis are:&nbsp;</p>
<h3>Multiple seasonalities&nbsp;</h3>
<p>Let&rsquo;s take the month of December as an example. You can observe multiple seasonal peaks in your sales trend during this month due to pre-Christmas, Christmas, and post-Christmas/New Year shopping drives.&nbsp;</p>
<p>It is a complex process to break down the effects of all these seasonalities on the overall sales.&nbsp;</p>
<h3>Sparse data&nbsp;</h3>
<p>When data is not evenly distributed over time, such as infrequently sampled data or a few data points, seasonality estimation is impacted. On the contrary, seasonal campaigns fluctuate a lot, and each year, businesses get exposed to new data points. The involvement of new data sources increases the possibility of generating more scattered data, making it a complex data collection process.&nbsp;</p>
<p>For better quality seasonality estimation, it is recommended to have at least two years of daily or weekly data handy.&nbsp;</p>
<h3>Data irregularities&nbsp;</h3>
<p>Seasonality analysis is distorted by outliers in the data generated due to external factors such as natural disasters, recession, pandemics, political inconveniences, and so on.&nbsp;</p>
<p>Suppose a cyclone damages life and property during the festive season in November, it would lead to a pause in shopping as people would restrict spending while overcoming their losses. It is standard practice to detect and remove outliers during data screening.&nbsp;</p>
<h2>Seasonality vs. Trends - Do&rsquo;s and Don&rsquo;ts</h2>
<p>Businesses rely on time series forecasting, which is a proven statistical model to predict future data based on historical data analysis. It helps marketers identify the data patterns from previous campaigns and extract useful information like top-performing months, spikes and dips, and other changes during holiday seasons.&nbsp;</p>
<p>It is important to know what seasonality analysis can tell you and where you are likely to misinterpret the trends. Seasonality doesn&rsquo;t always have a timed impact on overall sales. For example, Christmas shopping can start in November and continue until post-Christmas.&nbsp;</p>
<h2>How can you quantify the impact of seasonal trends on your overall sales?&nbsp;</h2>
<p>A simple way to approach this is to identify the popular products during this festival period so that retailers can perform logistic planning and restock these items to avoid stockouts. For instance, an e-retailer can restock chocolates and flowers before Valentine&rsquo;s Day if time series analysis shows that these were the most sold items in the last two consecutive years during this special day.&nbsp;</p>
<p>The output of time series analysis also depends on historical trends. For example, a fantasy collection store may observe a peak in sales volume of costumes, candies, and pumpkins during the 4th of July holiday. However, it is difficult to determine whether this is a regular business trend that depicts early Halloween shopping or a seasonal peak.&nbsp;</p>
<p>The only way to find an answer is by adjusting your seasonal data and removing general trends and peaks from your data sequence. Once the regular peaks are removed, you can proceed with the specific datasets associated with seasonal factors.&nbsp;</p>
<p>If you use unanalyzed data that combines both seasonal and trends, the possibility of misinterpreting the outcomes increases, resulting in inaccurate marketing decisions.&nbsp;</p>
<p>Suppose you sell an electronic product, and overall sales have declined between August and October. This makes you think that your product might have some faults, and accordingly, you decide to invest in testing its functionalities. However, at the beginning of November, you notice a sudden peak in sales, so you don&rsquo;t test the product. Instead, you decide to spend more on advertising to increase the sales order volume further. This leads to a large number of unsold and returned items.&nbsp;</p>
<p>In reality, the increase in sales might be the result of seasonality. Ideally, you should have assessed your product functionalities and reduced your advertisement spending.&nbsp;</p>
<h2>Role of Marketing Mix Modeling in Seasonality Analysis&nbsp;</h2>
<p>Data and timing are the two biggest factors that affect seasonal marketing campaigns. An accurately trained marketing mix model is foundational in executing seasonal analysis.&nbsp;</p>
<h3>Capitalize on data</h3>
<p>It is common to face <a href="https://www.lifesight.io/blog/data-challenges-in-marketing-mix-modeling">data challenges in marketing mix modeling</a>. Nevertheless, access to authentic data helps you correctly train your marketing mix model to get the desired outcomes. You can start by modeling your historical sales and conversion data using a mixed model to identify seasonal impacts. These inputs help predict the role of seasonality in your overall ROI.&nbsp;</p>
<p>Consider training your marketing mix models with customer data. By feeding your model with datasets like demographics and locations of customers, you&rsquo;ll unlock insights like shared preferences and buying patterns of customers to create segments. These in turn help predict the likelihood of purchase by customers who belong to the same segments. The next step would be to plan a personalized ad campaign to target them.&nbsp;</p>
<h3>Use an automated MMM solution&nbsp;</h3>
<p>Analyzing seasonal performance manually is nothing short of a nightmare. You need an automated MMM platform like <a href="https://www.lifesight.io/marketing-mix-modelling">Lifesight</a> to make data-driven decisions.&nbsp;</p>
<p>Unlike most marketing mix models that only model a static ROI, Lifesight tells you how effective your campaigns are during peak seasonal sale days compared to any other day.&nbsp;</p>
<p>That means it doesn&rsquo;t just provide you with an average marketing impact for a particular period; it delves deeper into testing different budget scenarios based on the customer lifecycle.&nbsp;</p>
<p>![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image1_7_18d2ca1ac8.webp)</p>
<p>Lifesight simplifies seasonality analysis by:&nbsp;</p>
<ul>
<li>Allocating budgets automatically based on historical data analysis&nbsp;</li>
<li>Predicting trends based on historic patterns and suggesting when to increase ad budget and when to pause a campaign</li>
<li>Using lift values from attribution and experiments for built-in calibration.</li>
</ul>
<p>Let&rsquo;s take an example here.&nbsp;</p>
<h2>How to Measure Seasonal Marketing Mix Modeling in the Ecommerce Industry?&nbsp;</h2>
<p>As we know, fluctuating conditions can make it challenging to predict the impact of your marketing budgets. That&rsquo;s where marketing mix modeling (MMM) helps marketing teams measure the impact of marketing activity on sales.&nbsp;</p>
<p>More so in the Ecommerce industry, MMM is an essential tool to plan budgets and build out strategies.&nbsp;</p>
<p>So while the Ecommerce industry has unique seasonal challenges, here&rsquo;s how marketers and CMOs can make the most out of their MMMs.&nbsp;</p>
<h3>1. Consider the Shopping Seasons</h3>
<p>Ecommerce brands face distinct seasonal shopping trends, such as back-to-school sales, Black Friday, and Cyber Monday. Understand these periods when forecasting sales. Start with historical sales data during these seasons to differentiate sales driven by natural demand from those driven by marketing efforts. Based on this, you can predict sales for different marketing budget scenarios.</p>
<p>Now, using this model you can forecast the number of conversions you should expect under different budget scenarios.&nbsp;</p>
<p>For example, if you know the sales are highest in November, you need to allocate the budget towards channels that contribute to the highest volume at that time of the year. When the sales decline, consider optimizing your mix towards ROI instead of volume.&nbsp;</p>
<h3>2. Keeping up with the shifting economy&nbsp;</h3>
<p>Economic conditions can directly influence consumer purchasing power and habits. An increase in online shopping might be linked to rising gas prices or changes in brick-and-mortar store accessibility. By incorporating macroeconomic factors, like inflation or unemployment rates, into your marketing mix models, you can anticipate shifts in consumer behavior.</p>
<h3>3. Consumer priorities are on the move&nbsp;</h3>
<p>Every ecommerce customer has a unique journey. Factors like age, location, and browsing habits can significantly influence purchasing decisions. For example, younger generations might be heavily influenced by social media ads, while older consumers may rely more on email marketing. Constantly updating your model with such insights ensures its relevance.</p>
<p>If you are an ecommerce brand, we recommend refreshing your models weekly or monthly and adding insights such as cost of living and savings to get a good idea of the price sensitivity throughout the customer journey.&nbsp;</p>
<h3>4. Don&rsquo;t hold back due to data scarcity&nbsp;</h3>
<p>While a two-year lookback window is ideal, for newer ecommerce businesses, this might not be feasible. If your business faces unique challenges (like disruptions due to global events), consider extending the lookback window to capture a broader range of data.</p>
<p>That said, if you are a relatively new company without years of sales data, consider measuring techniques that require shorter resolution windows like <a href="https://www.lifesight.io/attribution">attribution</a> and <a href="https://www.lifesight.io/incrementality-testing">incrementality testing</a> until you have enough data to run your MMM.&nbsp;</p>
<p>Now is always the best time to learn and start building a data roadmap for the future.&nbsp;</p>
<h3>5. Use search and automated MMM solutions in your planning process&nbsp;</h3>
<p>Every customer journey now starts with search and you need to design your MMM to measure its influence on seasonal trends. Include both organic and paid search activities in your model.&nbsp;</p>
<p>A best practice is to design your MMM to measure branded and generic paid searches separately. That&rsquo;s because they play different roles in customer journeys. It is likely that generic search consumers come early in their path to purchase.&nbsp;</p>
<p>Another important thing: Use an <a href="https://www.lifesight.io/blog/mmm-vendor-choice-matters">AI-powered automated and continuous MMM solution</a> like LifeSight to get a more holistic view of the impact of every channel and data source to build an accurate marketing mix model.&nbsp;</p>
<p>Get your <a href="https://www.lifesight.io/resources/checklist-data-sources-for-marketing-mix-modeling">essential checklist of data sources for MMM</a>.&nbsp;</p>
<p><strong><em>A pro tip: </em></strong><em>The insights gained from MMM depend on the freshness, relevance, and accuracy of the data you input.&nbsp;</em></p>
<h2>Final Thoughts&nbsp;</h2>
<p>Is there a way to not get misguided by seasonal marketing outcomes? Instead, analyze the root cause behind seasonal peaks.&nbsp;</p>
<p>What if we had an answer to these questions?&nbsp;</p>
<p>Lifesight&rsquo;s automated marketing mix modeling allows you to measure the true performance of your marketing so that you can make data-backed budgeting and forecasting decisions backed by data.&nbsp;</p>
<p><a href="https://www.lifesight.io/demo"><em>Request a demo </em></a><em>to know further.&nbsp;</em></p>

Navigating Seasonal Trends with MMM as a tool.webp

Dive into the intricacies of seasonality in marketing mix modeling, optimizing strategies for holiday campaigns and understanding ROI nuances.

The Modern Marketer’s Guide to Seasonality In Marketing Mix Modeling 

<p>More than 98% of brands in the United States use paid advertising to acquire customers. But how can they know whether their ads are necessary?</p>
<p>What if a brand can get as many customers without spending anything on ads?</p>
<p>A/B testing would not help you answer this question as you are testing a channel and not merely a component.</p>
<p>So what is the solution?</p>
<p>Incrementality Testing.</p>
<h2>What is Incrementality Testing and How It Helps?</h2>
<p>The name might sound fancy, but the concept is straightforward. Incrementality testing measures the actual return on investment (ROI) of your advertising efforts, stripping away the confusion and ambiguity between organic traffic and paid conversions. This clarity allows you to make smarter, more informed decisions.</p>
<p>Let&rsquo;s break it down with a simple example:</p>
<p><strong>Meet ModernTrend - Your trendy fashion ecommerce brand</strong></p>
<p>Imagine you run ModernTrend, a flourishing ecommerce brand specializing in trendy fashion. You&rsquo;re about to launch a new line of clothing, and you decide to invest in an advertising campaign to promote it. You run various ad creatives on social media and other platforms, aiming to drive sales.</p>
<p>After a month, you analyze the results and find two things:</p>
<ol>
<li>Some customers made purchases through your ad campaigns</li>
<li>Others bought your new clothing line directly through your website without interacting with your ads.</li>
</ol>
<p>Now, you want to understand the true impact of your advertising campaign. This is where incrementality testing steps in.</p>
<h3>Divide and Conquer</h3>
<p>Incrementality testing follows a scientific method. First, it splits your target audience into two groups:</p>


<strong>1. The Test Group:</strong> This group gets to see your ads. These are the individuals who engaged with your advertising campaigns and made purchases as a result.

<strong>2. The Control Group:</strong> This group is kept separate for analysis and doesn&rsquo;t see your ads. These are the customers who bought your clothing without any interaction with your ads.
<p>By comparing the results from each group, you can see which conversions would not have occurred without your advertising. This difference is expressed as a percentage and is known as &ldquo;incremental lift.&rdquo;</p>
<h3>The Three Possible Outcomes</h3>
<p>Incrementality experiments usually result in one of three scenarios:</p>
<strong>1. Positive Incremental Lift:</strong> This is the best-case scenario. It means your test group (those who interacted with your ads) outperformed the control group. In simple terms, your campaign was a hit and led to increased revenue for your business.

<strong>2. Neutral:</strong> Sometimes, your test and control groups produce the same results. In this case, your campaign generated sales, but there was no noticeable lift from your ads. It's a sign that you might need to tweak your strategy, like changing your ads content or targeting.
<br/>
<strong>3. Negative Incremental Lift:</strong> In rare cases, your campaign can do more harm than good. It results in a negative incremental lift, indicating that your advertising is impacting your brand negatively. This situation prompts a closer look at your testing setup.<br/>

![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image2_ebddd7924e.webp)

<p><em>A graphic showcasing the different possible outcomes of incrementality testing</em></p>
<h2>The Pros and Cons of Incrementality Testing</h2>
<p>To make informed decisions about when and how to implement incrementality testing, it&rsquo;s essential to understand both sides of the coin&ndash;the pros and cons.</p>
<h3>The Pros:</h3>
<br/>
<ul>
<li><strong>
Precision in Measurement</strong>: Incrementality testing is a scientific approach to gauge the true impact of your advertising efforts. It provides a precise measurement of how your ads influence consumer behavior, eliminating guesswork.
</li>
</ul>
<ul>
<li><strong>Channel Effectiveness</strong>: This method helps you assess the effectiveness of various advertising channels. It&rsquo;s invaluable when you&rsquo;re considering new platforms or experimenting with different ad networks, allowing you to allocate your budget strategically.</li>
</ul>
<ul>
<li><strong>Budget Optimization</strong>: By comparing the conversions from paid advertising to organic traffic you can determine whether your ad spend is justified. It reveals whether your budget could be redistributed to more fruitful channels or campaigns.</li>
</ul>
<ul>
<li><strong>Re-Engagement Insights</strong>: For re-engagement campaigns, incrementality testing provides crucial insights into the optimal timing, channels, and messaging. This ensures that your efforts yield the highest incremental lift when reconnecting with your audience.</li>
</ul>
<ul>
<li><strong>Privacy Compliance</strong>: In the era of privacy-focused changes, such as Apple's ATT framework, incrementality testing is an essential tool for brands. It helps you adapt to these shifts by providing data-driven insights while respecting user privacy.</li>
</ul>
<h3>The Cons:</h3>
<br/>
<ul>
<li><strong>Complexity</strong>: Setting up and conducting incrementality tests can be complex. It requires technical expertise, significant data management, and precise configurations. Managing these intricacies can be a challenge.</li>
</ul>
<ul>
<li><strong>Resource-Intensive</strong>: Incrementality testing demands a considerable investment of resources. Connecting to ad network APIs, aggregating data, and ensuring statistical significance can be time-consuming and costly.</li>
</ul>
<ul>
<li><strong>Noise and External Factors</strong>: External factors can skew results. Seasonal variations or unexpected events might impact user behavior and confound your data. Ensuring clean and noise-free results is a constant challenge.</li>
</ul>
<ul>
<li><strong>Segment Size</strong>: The size of the segments used in testing should be significant enough to yield meaningful results. Striking the right balance between segment size and test duration can be tricky.</li>
</ul>
<ul>
<li><strong>Technical Know-How</strong>: Incrementality testing requires a certain level of technical proficiency. Brands may need to invest in training or collaborate with experts to ensure accurate and reliable results.</li>
</ul>
<h2>Incrementality Testing vs Attribution: Unraveling the Differences</h2>
<p>Understanding the impact of your advertising is like navigating a maze. You not only need to know if your campaigns are working but also whether they&rsquo;re essential. Attribution and incrementality testing, both play a role here. However, they serve different purposes.</p>
<h3>The Core Distinction</h3>
<p>The key distinction between these two approaches is their primary question.</p>
<p>Incrementality testing seeks to determine whether your advertising had a genuine impact and if it was even necessary. It&rsquo;s like asking, &ldquo;<strong><em>Would we have achieved these results without our ads?</em></strong>&rdquo;</p>
<p>On the other hand, attribution is all about identifying which specific ad or touchpoint contributed to a conversion. It answers, &ldquo;<strong><em>Which part of our marketing efforts led the customer to take action?</em></strong>&rdquo;</p>
<p>While incrementality testing provides a broad perspective on the campaigns efficacy, attribution delves into the influence of each touchpoint. They aren&rsquo;t mutually exclusive; in fact, they can complement each other. By combining insights from both approaches, you get a more comprehensive understanding of your marketing impact.</p>
<p>Here's a straightforward table breaking down the differences between the two approaches:</p>

![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image1_1568371a59.webp)

<p><em>A tabular comparison of incrementality testing and attribution</em></p>
<p>So, when you need to assess the value of your advertising efforts, you turn to incrementality testing, When you want to pinpoint which ad or touchpoint played a crucial role in conversions, attribution is your guide. These two tools work together to help you navigate the ever-changing landscape of digital marketing effectively.</p>
<h2>When to Use Incrementality Testing: Unlocking the Power of Precision</h2>
<p><em>Disclaimer</em>: These are just some of the instances where incrementality testing can be made the best use of. You can possibly explore more use cases of incrementality testing by signing up for a <a href="https://www.lifesight.io/demo">demo of Lifesight&rsquo;s Incrementality Testing</a> capabilities.</p>
<h3>1. Evaluating New Advertising Channels</h3>
<p>When a brand is venturing into uncharted advertising territories, incrementality testing is a guiding light. Whether you&rsquo;re considering a new social media platform, exploring influencer collaborations, or experimenting with emerging ad networks, this method helps you understand if these channels are worth the investment. It reveals the true impact of your ads, ensuring you&rsquo;re not pouring money into a venture that might not yield substantial results.</p>
<h3>2. Optimizing Ad spend</h3>
<p>Every brand aims to maximize its return on ad spend (ROAS). Incrementality testing empowers you to identify areas where your ad budget is underperforming and make adjustments. By comparing the conversions from paid advertising to organic traffic, you can gauge whether your budget allocation aligns with actual results. If your ads aren&rsquo;t generating incremental value, it&rsquo;s time to redistribute resources for better outcomes.</p>
<h3>3. Enhancing Re-Engagement Strategies</h3>
<p>Engaging existing customers is often more cost-effective than acquiring new ones. Here&rsquo;s where incrementality testing takes center stage. For re-engagement campaigns, this method becomes a game-changer. It helps you pinpoint the ideal channels, messages, and timing to reconnect with your audience. With these insights, you can optimize your re-engagement strategy to achieve the highest incremental lift, ensuring your marketing efforts reach their full potential.</p>
<h3>4. Adapting to Privacy-Driven Changes</h3>
<p>In the age of privacy-focused updates, like Apple&rsquo;s ATT framework under iOS14, traditional measurement methods might fall short. Incrementality testing becomes crucial in filling the gap left by changes in tracking and attribution. It provides a data-driven approach to ensure your brand makes smart, privacy-compliant decisions. By understanding the real impact of your campaigns, you can navigate these shifting landscapes with confidence.</p>
<h3>5. A/B testing vs. Incrementality Testing</h3>
<p>While A/B testing helps compare different versions of ads, incrementality testing goes a step further. In A/B testing, you might compare a blue button to a red button in a banner ad. Incrementality testing, however, evaluates whether running that banner ad at all is better than not running it. This approach is invaluable when you need to assess whether your ads are genuinely necessary or if you could achieve the same results without them.</p>
<h2>Conclusion: Unlock the Power of Incrementality Testing</h2>
<p>As a marketer, it&rsquo;s crucial to make every penny of your ad spend count. That's where incrementality testing steps into the spotlight.</p>
<p>This methodology, grounded in scientific principles, separates the wheat from the chaff, letting you know definitively whether your advertising efforts are driving incremental value.</p>
<p>The power of incrementality testing goes beyond mere metrics; it&rsquo;s about informed decision-making. Whether you&rsquo;re dealing with user acquisition or re-engagement, determining the effectiveness of new media channels, or optimizing your budget allocation, this method gives you the insights to confidently shape your marketing strategies.</p>
<p>Now, how can you make the most of incrementality testing for your brand? That&rsquo;s where Lifesight steps in as your trusted partner.</p>
<p>With our automated incremental lift tests, you&rsquo;ll find the validation you need to confidently navigate the multi-channel marketing landscape, especially in this privacy-first era. We empower modern marketers by enabling them to harness their own data for model-informed strategies, execute audience split tests, and run geo-experiments seamlessly.</p>
<p>Lifesight offers the tools to scale up your spend with assurance, helping you discover growth opportunities, and allocate budgets accurately. You can automatically build actionable scenarios based on your budget or KPIs, optimize on a channel level or for your entire funnel, and ensure that every move you make is a strategic one.</p>
<p>Take the first step towards more confident, data-driven decision-making. Join the ranks of industry leaders like Ray-Ban, LG, Spotify, and many more who have benefited from Lifesight&rsquo;s incrementality testing capabilities.</p>
<p><a href="https://www.lifesight.io/demo">Request a demo</a> now and let Lifesight guide you toward more effective, efficient, and impactful marketing campaigns.</p>

The Pros and Cons of Incrementality Testing.webp

Explore the benefits and drawbacks of incrementality testing in marketing, ensuring informed campaign optimization decisions.

Weighing The Pros and Cons of Incrementality Testing

<p>Hello Modern Marketers!</p>
<p>The holiday season is upon us, and with Black Friday and Cyber Monday (BFCM) looming large, the pressure is mounting to ensure that our campaigns not only shine but also deliver! If you've been wondering how to measure your BFCM efforts effectively, we've got a treat for you. Dive into our freshly released BFCM Measurement-Centric Marketing Guide and get set to transform your festive season strategies!</p>
<p>This isn't just about capitalizing on the heightened retail activity during BFCM. It's about harnessing the insights gathered from these prominent events to optimize your marketing strategies, ensuring they're robust enough to pave the way for sustained profitability that extends through Q5 and beyond.</p>


 <a href="https://www.lifesight.io/resources/made-to-measure">![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Image_banner_of_the_guide_468123055e.webp) </a>

<h2>A Sneak Peek into Precision Planning</h2>
<p>This guide delves into a meticulous plan, starting four weeks ahead of the big days. But it's not just a countdown; it's your marketing compass. The journey begins with a deep dive into Marketing Mix Modeling (MMM), focusing on channel-specific insights and historical data dissection. It's about looking back to move strategically forward, understanding which moves paid off before to replicate and improve them.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image5_4db47ed17b.webp) 


<p>But what are numbers without context? Your targets! We've included a crystal-clear framework for setting up your objectives. This guide walks you through aligning your historical data with ambitious yet realistic goals, fortifying your foundation before the real game begins.</p>
<h2>Calendar of Calculated Moves</h2>

<a href="https://www.lifesight.io/resources/the-incremental-roas-playbook-for-bfcm"> ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image2_195d005146.webp)</a>


<p>Imagine navigating through the busiest days of your campaign with a well-structured calendar, knowing well in advance what needs your attention. That's exactly what this guide provides! It includes a detailed weekly planner, pointing out the key phases of your strategy, from initial budget allocations and prediction model setups to real-time campaign adjustments during the heart of the shopping season.</p>
<p>Here's the best part: we've integrated a feature allowing you to monitor your Key Performance Indicators (KPIs) as you progress through the season. This isn't just a roadmap; it's your scoreboard, continuously showing whether you win the game.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image7_e7f4ce1c44.webp) 


<h2>Assets That Make a Difference</h2>
<p>We know that in the digital world, interaction is key. That's why our guide isn't a passive scroll-through. Included are actionable checklists ensuring you never miss a beat, keeping your strategy dynamic and adaptable. These checklists are designed for practicality, stripped of any jargon, making them your quick go-to in the heat of the moment.</p>

<a href="https://www.lifesight.io/resources/the-incremental-roas-playbook-for-bfcm"> ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image4_df76fb0f01.webp)</a> 

<p>But wait, there's more to this season than just a rush of sales. Our post-campaign analysis framework is a gem, offering a structured approach to understanding the 'why' behind your 'what.' It's a treasure trove of learning, setting you up not just for reflection but for strategic evolution.</p>


 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image3_875e4276f2.webp) 

<p>In addition, the guide is loaded with "Pro Tips" compiled from professionals in the industry to give marketers an edge.&nbsp;</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image7_e7f4ce1c44.webp) 


<h2>Key Takeaways That Empower</h2>
<ul>
<li>Data-Driven Decisions: The guide reinforces how historical data isn't just numbers but lessons from the past, teaching you to leverage it.</li>
</ul>
<ul>
<li>Real-Time Reactions: Learn the art of agility with tips on adapting your strategies on the fly and responding to real-time data for maximum impact.</li>
</ul>
<ul>
<li>Beyond the Numbers: Our guide emphasizes the human side, explaining how to gauge customer satisfaction effectively, turning numbers into narratives.</li>
<li>The Growth Loop: It's not just about this season. The guide shows you how to take your insights forward, building a continuous loop of improvement and success.</li>
</ul>
<p>Our comprehensive guide is more than a document; it's a mentor, a tool, and a companion geared to make this holiday season your most successful yet. It's built on the expertise of industry veterans, packed with the wisdom of years, and carved with today's trends.&nbsp;</p>
<p>Ready to take the leap? Dive into the details, emerge with clarity, and execute with precision. Your ultimate holiday season success is just a guide away!</p>

 <a href="https://www.lifesight.io/resources/made-to-measure">![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Image_banner_of_the_guide_468123055e.webp) </a>


Unveiling the Ultimate Guide for Measuring Your Holiday Season Success and Beyond.webp

Master your holiday sales strategy with our comprehensive guide on measuring success and optimizing for peak performance all year round.

Unveiling the Ultimate Guide for Measuring Your Holiday Season Success and Beyond

<p>Client-side tracking has been the conventional method of collecting data from websites and applications. It has a fascinating origin story though, deeply intertwined with the history of the internet itself.</p>
<p>It all began with the birth of the World Wide Web.</p>
<p>In 1989, the World Wide Web was invented and with the concept of hypertext documents that could be viewed through web browsers. As the web evolved, developers sought ways to enhance the user experience. This led to the creation of the first web browser &ldquo;cookie&rdquo; in 1994. They allowed websites to remember users&rsquo; preferences and login information, significantly improving user experiences.</p>
<p>Web analytics then took another leap forward with the introduction of tracking pixels and JavaScript. Marketers and website owners realize the value of understanding user behavior. They began embedding tracking pixels in web pages, invisible to users but capable of recording various actions. JavaScript enabled dynamic, client-side interactions, making websites more engaging.</p>
<p>With the growth of online advertising, third-party scripts, often served by ad networks, became common on websites. These scripts, while facilitating ad delivery and tracking, also introduced privacy and security concerns.</p>
<p>Client-side tracking thrived, but slowly challenges started emerging.</p>
<p>Users concerned about privacy installed ad blockers, causing data loss for marketers. Additionally, with the advent of stringent privacy regulations like GDPR and CCPA, collecting and processing data from users became subject to legal restrictions.</p>
<p>As a response, server-side tracking emerged as an alternative, providing a more secure and compliant way to collect data without relying on the user&rsquo;s browser. It offers the ability to bypass browser limitations, improve tracking accuracy, and enhance data privacy, making it a compelling choice for modern businesses.</p>
<p>In this blog post, we will uncover the intricacies of server-side tracking, understand how it works, explore its advantages, and demystify its limitations. By the end of this exploration, you&rsquo;ll have a comprehensive understanding of why server-side tracking is the future and how it can transform your business.</p>
<p>So, fasten your seatbelt, and let&rsquo;s dive into the realm of server-side tracking, where data becomes not just a tool but a strategic asset.</p>
<h2>Understanding Server-Side Tracking</h2>
<br/>
<h3>What is Server-Side Tracking?</h3>
<p>Server-side tracking is a method of data collection that directly communicates with a server, eliminating the need for intermediary steps in the tracking process. This method relies on sending data from a website or application to a server, which then transmits this data to a destination API or server endpoint.</p>
<p>Let&rsquo;s understand this further with a simple analogy.</p>
<p>Imagine you&rsquo;re running a lemonade stand and want to know how many glasses you&rsquo;ve sold. You can either track it by sitting at the stand, counting each sale yourself (client-side tracking), or you can have a trusted friend tally up the glasses on your behalf (server-side tracking).</p>
<p>Client-side tracking involves using cookies and pixel tags to collect data on your website or application. These digital breadcrumbs can be incredibly useful, but they come with limitations. For instance, ad blockers can gobble them up, making your data incomplete or inaccurate.</p>
<p>Server-side tracking takes a different route.</p>
<p>Instead of relying on data collected from your audience&rsquo;s browsers (client-side), it captures information directly from your server and relays it to your destination API or server endpoint.</p>
<p>Imagine your lemonade stand; this time, your friend stands by the counter to keep track of the number of glasses sold. This data is more accurate because it&rsquo;s collected at the source.</p>
<h3>Key distinctions between server-side and client-side tracking</h3>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image4_1a9307d29e.webp) 
<p><em>An illustration showcasing the difference between client-side and server-side tracking Source: </em><a href="https://developers.google.com/tag-platform/tag-manager/server-side"><em>Google</em></a></p>
<p>Understanding server-side tracking necessitates grasping the stark contrast it presents compared to conventional client-side tracking. Here are some of the salient differentiators:</p>
<h4>1. Data Accuracy</h4>
<p>In client-side tracking, your tracking pixels can miss out on some data due to ad blockers, slow loading times, or other browser-related issues. Server-side tracking ensures more reliable data collection by eliminating these browser-dependent variables.</p>
<h4>2. Control Over Data:</h4>
<p>With server-side tracking, you decide what data gets sent. It&rsquo;s like handpicking the best lemons for your lemonade, ensuring that only the most relevant information is included in your analysis.</p>
<h4>3. Privacy-Friendly:</h4>
<p>This method reduces the number of third-party cookies and scripts, aligning more closely with privacy regulations. It also bypasses browser restrictions and ad-blockers, making it less intrusive to users.</p>
<h4>4. Speedy Performance</h4>
<p>Fewer scripts to load means a faster website. Speed is essential for retaining user engagement and reducing bounce rates.</p>
<p>Here&rsquo;s a table summarizing the differences between client-side and server-side tracking:</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image1_0c9b30b34b.webp) 
<h2>The Nuts and Bolts of Server-Side Tracking: How It Works</h2>
<p>So, how does this modern marvel actually work?</p>
<p>Let&rsquo;s dive into the intricate machinery that powers server-side tracking, unraveling the mechanics behind its effectiveness.</p>
<h3>Breaking Down the Process</h3>
<p>Imagine your online store is a bustling marketplace, with customers moving about, adding items to their carts, and making purchases. Your goal is to observe their actions accurately, without any blind spots or interruptions. This is where server-side tracking steps in to provide a seamless experience.</p>
<h3>Step 1: Data Routing</h3>
<p>The journey begins with routing event data&ndash;such as adding items to the cart and completing purchases&ndash;from your website to your server. These data streams are often referred to as &ldquo;Sources&rdquo; in tracking solutions like Lifesight. Think of this stage as the process of collecting each sale&rsquo;s total and sending it to the central cashier.</p>
<p>Your server acts as the central cashier, where all the financial transactions occur. This server can be a server-side container in Google Tag Manager (GTM), a fully managed solution like Lifesight, or even your own Google Cloud server. Importantly, these data streams must be routed under your primary domain URL, also known as &ldquo;1st party context.&rdquo; This ensures that you have full control over the data collected.</p>
<h3>Step 2: Destination Configuration</h3>
<p>Once the data reaches your server (GTM or Lifesight), you need to configure the destinations for this information. Destinations are where the data is ultimately routed. For instance, you can configure your server to send events and conversions to Facebook&rsquo;s Conversion API.</p>
<p>Different channels may require different configurations. For example, Facebook may request you to send server-side events and client-side pixel events. Others, might not need both server-side and client-side tracking, allowing you to disable client-side tracking once the server-side is active.</p>
<h3>Step 3: Conversion Routing</h3>
<p>When a conversion event, such as a purchase on your website or an order in your Shopify admin, occurs, your server routes this data to the configured destinations. It&rsquo;s like your central cashier tallying the final purchases and sending this information to the respective departments that need it.</p>
<h3>Step 4: Response from Destinations</h3>
<p>Now, the destination API, which receives the conversion event data, sends a response back to your server. This response is typically a simple message indicating either success or failure. If it's a failure, their response often contains an error code to help identify and resolve the issue.</p>
<h3>Step 5: User Browser Response (Optional)</h3>
<p>Optionally, your server can send a response back to the user&rsquo;s browser to reset header information. This step ensures that any cookies or tracking elements on the user&rsquo;s side are aligned with the data your server has collected.</p>
<h3>Fine-Tuning the Process</h3>
<p>The beauty of server-side tracking lies in its adaptability. You can fine-tune this process according to your specific needs, configuring the data you wish to collect and send. For instance, you can capture data from the user&rsquo;s browser, including IP addresses, user-agent information, and attribution data like cookie values, UTM parameters, and click IDs.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image2_3eeb0fbf7c.webp) 
<h3>Example: Facebook Conversion API</h3>
<p>Let&rsquo;s consider a real-world example: Facebook&rsquo;s Conversion API. When integrating with this API, you would need to provide an event ID to deduplicate server and pixel events. Each channel may have unique data requirements, and server-side tracking allows you to fulfill those needs.</p>
<h2>Unveiling the Limitations of Server-Side Tracking: When It&rsquo;s Not the Silver Bullet</h2>
<p>We admit it. We&rsquo;ve sung the praises of server-side tracking and how it&rsquo;s reshaping the digital marketing landscape. But like any technology, it&rsquo;s not without its limitations.</p>
<p>Let&rsquo;s take a closer look at when server-side tracking might not be the perfect fit.</p>
<h3>1. The Waiting Game: Client-Side Dependencies</h3>
<p>One of the biggest hurdles is the readiness of marketing channels to adopt server-side tracking. Many marketing platforms still heavily rely on client-side tracking, thanks to its powerful fingerprinting capabilities used for attribution.</p>
<p>What&rsquo;s the hold-up?&nbsp;</p>
<p>Some platforms are not technically prepared to handle the influx of data from server-side tracking. Facebook, Snapchat, TikTok, and others are exceptions with fully functional conversion APIs that can receive server-side data.</p>
<p>The key takeaway here is &ldquo;yet&rdquo;. The industry is gradually moving towards server-side tracking and it&rsquo;s only a matter of time before most marketing channels catch up.</p>
<h3>2. The Bottom Line: Cost and Data Volume</h3>
<p>Adopting API integration for server-side tracking involves building additional systems, which translates to higher development costs and resource requirements. Some platforms might be reluctant to make this investment, especially if their existing client-side setup is functioning well.</p>
<p>Another consideration is the volume of data being processed. Server-side tracking often sends less data than client-side methods. While this can be seen as an advantage in terms of privacy and compliance, it could affect how channels attribute performance. They may have to rebuild their attribution models to accommodate the limited dataset.</p>
<h3>3. The Invisible Data conundrum</h3>
<p>One of the mysterious aspects of client-side tracking is its ability to pick up data from users&rsquo; browsers without revealing exactly what information is being collected. This &ldquo;hidden&rdquo; data is sent back to marketing channels, and users and brands might not be fully aware of its extent.</p>
<p>With server-side tracking, the process becomes more transparent. The data you collect is explicitly defined and managed. While this is generally seen as an advantage, it can create limitations when channels have to rebuild their attribution models with a more limited dataset.</p>
<h3>4. The Era of Privacy and Compliance</h3>
<p>With the rise of privacy regulations like GDPR and CCPA, the need for consent and cookie compliance has surged. When sending data server-side to destinations like Google Ads, it&rsquo;s crucial to ensure your server-side integrations align with these regulations.</p>
<p>For example, if you must adhere to Google Ads EU consent compliance requirements, your server-side tracking needs to be configured to handle consent rules accurately. This extra layer of complexity might deter some brads from fully adopting server-side tracking.</p>
<h3>5. When the Puzzle Pieces Don&rsquo;t Fit</h3>
<p>Sometimes, server-side tracking doesn&rsquo;t perfectly align with your specific needs. Certain marketing channels, especially those with a strong client-side focus, might not be compatible with server-side tracking, creating limitations in your overall tracking strategy.</p>
<p>In these cases, a hybrid approach, blending both server-side tracking and client-side tracking, can offer a solution. It allows you to capture the advantages of server-side tracking while still meeting the requirements of more traditional platforms.</p>
<h2>Conclusion: Why Your Business Needs Server-Side Tracking</h2>
<p>We are moving to a digital landscape where data drives decisions and customer engagement is paramount, and server-side tracking emerges as a game-changer. It&rsquo;s the answer to many challenges faced by businesses today, providing a robust, accurate, and privacy-compliant method to collect and utilize data effectively.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image3_e726aec37b.webp) 
<p>Server-side tracking equips your business with the ability to reach more customers and send conversion events with confidence. It&rsquo;s a solution designed for the future, built to adapt and overcome the evolving landscape of privacy regulations and data accessibility.</p>
<p>If you&rsquo;re looking for a partner to guide your business through the world of server-side tracking, Lifesight is your answer.</p>
<p>With Lifesight as your trusted partner, you&rsquo;re equipped to thrive in this data-driven landscape. Our capabilities are designed to lead your business toward a future of better tracking accuracy, more insightful analytics, and improved customer reach.</p>
<p>We invite you to <a href="https://www.lifesight.io/demo">book a demo with Lifesight</a> and discover the limitless possibilities of server-side tracking, unlock your data&rsquo;s full potential, and stay ahead of the curve in a dynamic digital world.</p>

What exactly is server-side tracking, and how does it work_.png

Unlock the evolution of web tracking: from client-side origins to the power of server-side tracking. A journey through data’s history.

What exactly is server-side tracking, and how does it work?

<p>In the rapidly evolving world of digital marketing, it's not just about who you target - it's also about who you choose not to target. Audience suppression, a technique often overlooked by marketers, is steadily gaining ground as a game-changer in enhancing ad efficiency and ROI.</p>
<p>According to a study by AdWeek, 37% of marketers have reported a substantial increase in campaign performance when implementing suppression strategies. But haphazard audience suppression can easily backfire. Without a clear methodology, you may suppress valuable segments and miss out on conversions. You need a strategic, nuanced approach to realize the benefits while avoiding pitfalls.</p>
<p>As industry expert <a href="https://www.linkedin.com/in/jennabmatthews/">Jenna Matthews</a> notes, "Effective suppression isn't just about excluding an audience - it's about cultivating the right audience." So, when should you leverage audience suppression, and how do you do it effectively? Dive in as we demystify the ins and outs of this pivotal marketing strategy.</p>
<h2>Unraveling Audience Suppression</h2>
<p>Not every marketing message resonates equally with every audience. This is where the strategic power of audience suppression shines.</p>
<p>Audience suppression, or exclusion, involves deliberately preventing specific groups of customers or prospects from seeing certain campaigns, ads, or messages. It allows you to fine-tune targeting to align messaging precisely with the needs and interests of the intended audience</p>
<p>For example, an e-commerce retailer promotes a 50% off sale for first-time app customers. Suppressing existing customers from seeing this campaign prevents confusion and maintains loyalty by not advertising special perks only for new users. Suppression optimizes the messaging specifically for the right segment in this case.</p>
<p>When aligned with campaign goals and audience insights, suppression can achieve several key objectives:</p>
<ul>
<li><strong><strong>Prevent Ad Fatigue:</strong> </strong>Seeing the same ad or campaign repeatedly eventually frustrates consumers, decreasing engagement over time. Strategically suppressing groups already overexposed to a message avoids this fatigue.</li>
</ul>
<ul>
<li><strong><strong>Protect Existing Customers:</strong> </strong>Offers tailored specifically for prospects or new users often don't apply or need clarification for your current customer base. Suppressing current customers from these campaigns prevents frustration and helps maintain loyalty.</li>
</ul>
<ul>
<li><strong><strong>Optimize Ad Spend:</strong></strong> Excluding audience segments with historically low intent or high bounce rates allows focus spending on higher-converting groups for a better return on ad spend. Purposeful suppression reduces wasted marketing dollars.</li>
</ul>
<ul>
<li><strong><strong>Enhance Personalization:</strong> </strong>By suppressing groups unlikely to engage, you can craft messages tailored precisely to each target audience's needs and interests for greater impact. Suppression improves personalization.</li>
</ul>
<ul>
<li><strong><strong>Maintain Privacy &amp; Compliance:</strong> </strong>Suppressing groups that have opted out from communication or fall under regulations aids legal compliance and upholds consumer privacy preferences.</li>
</ul>
<p>When deployed strategically based on data insights, audience suppression allows for delivering the optimal message to the right customer groups. But indiscriminate, blanket suppression can easily backfire, blocking you from reaching qualified audiences. A nuanced, segmented approach is key.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Unraveling_Audience_Suppression_98d394ef8f.webp) 
<h2>Steps for Audience Suppression</h2>
<p>Executing effective audience suppression requires careful planning and disciplined execution. Follow these steps to maximize the strategic value of suppression:</p>
<h3>Define Suppression Objectives</h3>
<p>First, determine the goals you want to achieve through suppression. Common objectives include reducing ad fatigue, protecting high-value customer groups, improving personalization, optimizing ad spend, or boosting relevancy. Defined goals will guide your overall suppression strategy.</p>
<h3>Segment Your Audience</h3>
<p>Leverage first-party customer data to divide your audience into meaningful segments based on attributes like demographics, behaviors, interests, purchase history, campaign interactions, and other insights. Advanced segmentation identifies both high-intent groups to target and low-intent groups to consider for suppression.</p>
<h3>Identify Suppression Targets</h3>
<p>With audience segments defined, analyze them to determine which specific groups should be suppressed from which campaigns. For example, you may want to exclude existing subscribers from promotions aimed at new customers or suppress recent purchasers from repetitive retargeting ads.</p>
<h3>Build Suppression Lists</h3>
<p>Compile lists of unique identifiers like email addresses, cookies, or customer IDs that correspond to the audience groups earmarked for suppression. Structured lists ensure you can accurately exclude the right users at the platform level.</p>
<h3>Choose Supporting Platforms</h3>
<p>Select marketing tools and platforms with robust exclusion and suppression capabilities like email service providers, demand-side platforms, and customer data platforms. Most CRMs also allow for suppressing specific contacts.</p>
<h3>Implement Suppression Lists</h3>
<p>Ingest your suppression lists into the selected platforms. Exclusion workflows vary but often involve uploading CSVs or applying rule-based suppression filters. Tag management tools can also exclude defined users.</p>
<h3>Test for Accuracy</h3>
<p>Conduct small test sends or campaigns before full deployment to confirm the right users are being accurately suppressed without unintended exclusions. Preemptively catch any data issues.</p>
<h3>Launch and Monitor Campaigns</h3>
<p>With testing complete, launch your suppressed campaigns and initiatives. Continuously monitor performance and optimize based on insights uncovered through analysis and attribution.</p>
<h3>Update Suppression Lists</h3>
<p>Revisit exclusion lists routinely to add or remove audience segments as behaviors and priorities evolve over time. Keeping lists current ensures maximum relevance.</p>
<p>Audience suppression allows marketers to fine-tune messaging and avoid wasted ad spend. But basic suppression only scratches the surface of its strategic potential. So, let's explore some high-impact applications.&nbsp;</p>
<h2>Advanced Audience Suppression Tactics to Boost Campaign ROI&nbsp;</h2>
<p>With customer intelligence from a unified data foundation, marketers can execute advanced suppression tactics to dramatically sharpen targeting and boost campaign ROI.&nbsp;</p>
<h3>Suppress Regular Buyers from Branded Search</h3>
<p>Many customers habitually search for brand names directly instead of typing the URL. Serving ads to these high-intent "lazy clickers" offers little value. A customer data platform (CDP) like Lifesight Connect can identify regular buyers and suppress them from paid branded searches. This protects their experience while focusing on prospects who need persuasion.</p>
<p><strong>Example: </strong>An athletic apparel brand can use its CDP to identify buyers who have made 2+ purchases in the past 3 months. These repeat customers can be suppressed from paid search ads on branded keywords to avoid overspending.</p>
<h3>Suppress Recent Buyers from Product Ads&nbsp;</h3>
<p>Buyers don't need product ads immediately after purchase. But basic always-on targeting often continues serving repetitive ads, annoying customers. A CDP allows suppressing recent buyers from product campaigns temporarily to avoid over-exposure. When the time is right, suppressed users can be re-engaged via tailored promotions.</p>
<p><strong>Example: </strong>A consumer electronics retailer can suppress customers who have purchased a smart TV in the last 30 days from retargeting ads promoting that same smart TV model. This avoids annoying recent buyers with repetitive ads.</p>
<h3>Suppress Existing Customers from Lookalike Audiences</h3>
<p>Lookalike targeting aims to find new customers resembling your best ones. But many lookalike models inadvertently include existing customers too. Suppressing your customer base avoids wasted ad spend on those already loyal. Excluding existing customers also fine-tunes lookalike targeting purely for acquisition.</p>
<p><strong>Example: </strong>A subscription service for customized vitamins can suppress its subscriber list from lookalike audiences modeled off their high-value subscribers. This refines targeting for new prospects who resemble existing subscribers.</p>
<h3>Suppress Prospects from Sweepstakes Audiences</h3>
<p>Sweepstakes attract new prospects by offering prizes for submitting contact information. But general lookalike targeting often exposes prospects already in your database too. Suppressing existing contacts increases sweepstakes efficiency by focusing exclusively on net-new prospect acquisition.</p>
<p><strong>Example: </strong>An automotive brand running sweepstakes can suppress prospects already in their CRM database from the sweepstakes ads. This reduces wasted ad impressions and keeps the focus on net new lead generation.</p>
<h3>Suppress Churned Customers</h3>
<p>It's impossible to fully prevent churn. But basic suppressing groups that have disengaged avoids wasting resources trying to reactivate them. A CDP's predictive models help identify customers highly likely to churn for proactive suppression. Resources get redirected to high-lifetime-value customers.</p>
<p><strong>Example: </strong>A SaaS platform can use predictive churn models in its CDP to identify at-risk customers with a high propensity to cancel service. These unstable customers can be suppressed from retention campaigns.</p>
<h3>Send Fewer Impressions to Low-Value Customers</h3>
<p>The <a href="https://www.forbes.com/sites/forbesfinancecouncil/2022/02/08/the-8020-rule-is-brutal-thats-why-it-works/#:~:text=Put%20in%20stark%20terms%2C%2020,%2C%20generate%2080%25%20of%20revenue.">80/20 rule</a> means 20% of customers drive 80% of revenue. Suppressing low-value groups from always-on campaigns focuses spend on your most valuable segments. CDP-powered analytics quantifies customer value to inform nuanced tiered suppression.</p>
<p><strong>Example: </strong>A retailer can use their CDP to segment customers into value tiers. Low-value tiers can have lower campaign impression caps compared to high-value VIP segments that see higher caps.</p>
<p>Sophisticated suppression requires a CDP to connect data across channels for unified profiles reflecting true customer value. With holistic intelligence, suppression evolves into a scalpel for ultra-targeted messaging. Consumers see only relevant ads while ROI climbs.</p>
<p>CEO of Camelot Strategic Marketing &amp; Media <a href="https://www.linkedin.com/in/sambloom/">Sam Bloom</a> said in an interview that advertising campaigns that work need to reach the right people at the right time and avoid reaching the same people twice through different media platforms.&nbsp;</p>

<iframe width="700" height="450" src="https://www.youtube.com/embed/qEG6Peqn2og?si=iQiPwnxCsRB2CFC2" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
<p><a href="https://www.youtube.com/watch?v=qEG6Peqn2og">Source</a></p>
<p>With the help of a <a href="https://www.lifesight.io/cdp">Customer Data Platform</a>, you can swiftly locate, categorize, and suppress audiences that are not likely to respond to your marketing campaigns. These cutting-edge suppression techniques assist you in enhancing the reputation of your business, raising your return on investment, and eliminating superfluous ad expenditures.</p>
<h2>Achieve strategic suppression at scale with Lifesight</h2>
<p>To move beyond basic suppression, marketers need complete customer intelligence. Lifesight Connect provides the unified data foundation to power strategic exclusion across channels.</p>
<p>Connect ingests data enterprise-wide, resolving identities to construct unified customer records. This single view reveals hidden insights for segmentation and personalization.</p>
<p>Robust analytics within Connect quantify metrics like customer lifetime value and purchase propensity. Predictive models identify high-value customers versus churn risks. These insights inform a nuanced suppression strategy.</p>
<p>Seamlessly integrating with your martech stack, Connect activates audience data to suppress targeted segments across advertising, email, web experiences, and more. Suppression options include:</p>
<ul>
<li><strong>Exclusion Lists - </strong> Upload specific identities for blanket suppression based on unique customer criteria. Keep high-intent groups like recent purchasers or loyal subscribers from seeing irrelevant ads.</li>
<li><strong>Algorithmic Audiences - </strong> Leverage Connect's analytics to <a href="https://www.lifesight.io/segments">automatically suppress users</a> matching churn, value, or propensity data models. Automatically exclude customers unlikely to convert.</li>
<li><strong>CRM Audiences - </strong> Build audiences in your CRM then sync to Connect for suppression across other channels. Keep prospects out of customer-only promotions.</li>
<li><strong>Custom Segment Syncing - </strong> Create dynamic segments in Connect based on behavior then sync these groups to your ad platform for suppression. Exclude recent site visitors from retargeting.</li>
<li><strong>Event-Based Triggers - </strong> Set up triggers to suppress users who complete key actions. Automatically remove purchasers from cart abandonment campaigns.</li>
</ul>
<p>With Connect, audience data flows freely across tools through APIs and integrations. Ongoing profile enrichment within Connect allows for evolving suppression in step with changing customer needs. Suppressed groups receive only the messages most relevant to their current state. Touchpoint cadence aligns tightly with the lifecycle stage.</p>
<p>Strategic suppression is no longer a manual, siloed process. Connect provides the customer intelligence engine to personalize engagement through exclusion at the enterprise scale.</p>
<h2>Postscript</h2>
<p>In today's crowded marketplace, strategic audience suppression is imperative for breakthrough results. Excluding specific groups from campaigns reduces waste and bolsters relevance.</p>
<p>With unified data as the foundation, tailored exclusion reaches its full potential. Customer data platforms consolidate cross-channel information into the holistic profiles required for nuanced suppression.</p>
<p>When leveraged strategically, suppression becomes a powerful instrument for next-level campaign performance. Wasted spending declines as relevance sharpens. Consumer fatigue gives way to sustained impact.</p>
<p>Now is the time to evolve suppression into an engine of growth and efficiency. Let Lifesight Connect provide customer intelligence to target audiences with surgical precision.</p>
<p>Discover how our Connect is helping leading brands execute suppression that transforms campaign ROI. <a href="https://www.lifesight.io/demo">Request a demo today</a>!</p>

Audience Suppression_ How And When To Use It.png

Uncover the strategic use of audience suppression for precise targeting. Learn how to wield this powerful tool to enhance your marketing strategies and drive impactful results.

Audience Suppression: How And When To Use It

<p>The online marketplace is crowded and plagued with intense competition. Gathering customer data and analyzing it to craft data-backed impactful strategies is imperative to drive business growth, but is that enough? Probably not; a thorough competition analysis is what it takes for a business to stay ahead. However, in a world where customer privacy matters the most, gathering data, whether internal (own website) or external (competition website), can be complex.</p>
<p>Enter clickstream data and clickstream analytics, a goldmine of insights to understand user behavior. However, deriving insights from clickstream data requires proper processing of such data, and only a few customer data collection platforms (CDP) have this expertise.</p>
<p>In this blog, we dive deep into understanding what clickstream data is, its importance in the current digital business landscape, and how Lifesight seamlessly integrates clickstream data in its toolkit to help businesses understand the effectiveness of their online storefront and ways to improve.&nbsp;</p>
<p>With over 5 billion internet users globally and retail e-commerce sales expected to exceed <a href="https://www.statista.com/topics/871/online-shopping/#topicOverview">$5.7 trillion</a>, clickstream data is the next big thing. The clickstream analytics market will be worth <a href="https://www.alliedmarketresearch.com/clickstream-analytics-market-A05942">$251.16 million</a> by 2026. The importance of clickstream data cannot be undermined in the current business climate.&nbsp;</p>
<h2>What Is Clickstream Data?</h2>
<p>At its core, clickstream data is the digital footprint users leave behind as they navigate through websites and online platforms. Simply put, it is a user's journey on a website or mobile application. Such data tracks every single click a user does on a browser. The data collected is stored in data log files or databases and provides deep insights into a specific user's behavior and preferences.&nbsp;</p>
<p>As a user explores a website, clickstream records every click on the screen and stores it in a log. The data log contains information such as website URL, browser details, and events (log-in, log-out, product added to and removed from cart, mode of payment, product ID, total time spent per session, etc.). Information like the website link a user clicked on, the web page visited, the sequence in which websites and pages within them were browsed, average time spent per page, ads clicked on, CTAs and weblinks clicked, kind of interaction on a website are all examples of clickstream data.&nbsp;</p>
<p>Let&rsquo;s understand clickstream data with the help of an example. Say you type Lifesight customer data platform on a Google Chrome browser. The below screen appears:</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Clickstream_Data_Example_520c3b790c.webp) 

<p>Next, you click on the third link appearing on the search results, which leads you to the blog page below:</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Clickstream_Data_Example_Lifesight_Blog_1c0c1a92e9.webp) 

<p>When reading the blog, you scroll down to see the bottom with links to other blogs. To learn more about CDP, click on the next blog or one of the product features in the footer.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Clickstream_Data_Example_Lifesight_Blog_Footer_5db8714b76.webp) 

<p>By tracking your clickstream, Lifesight knows how much time you spent reading the blog, which sections of the blog you went back to repeatedly, and what were your following actions. Armed with this data for you and several other website users, Lifesight knows what kind of content to create to get more demo requests and blog topics that may interest you, which can help them showcase suggestions personalized to your interest and more.</p>
<p>This is not it; there are umpteen benefits of harnessing the power of clickstream data for a business. Let&rsquo;s find out what are these.</p>
<h2><strong>Benefits of Analyzing </strong><strong>Clickstream Data</strong></h2>
<p>Any collected data must be cleaned, organized, and structured to make sense of it and derive insights. This process of tracking, cleaning raw clickstream data, and putting it in an easy-to-understand structure is clickstream analytics.&nbsp;</p>
<p>Accessing user behavior data on a competitor's website can be an excellent way for brands to optimize their website and create a marketing plan to drive more audience engagement and better sales. But that is a far-fetched dream.</p>
<p>This is where clickstream data analysis can be essential for brands and drive significant benefits for businesses across sectors. Clickstream data gives deep insights into a customer's journey on a website. However, just having this information is not enough.&nbsp;</p>
<p>There are several ways in which businesses can leverage this wealth of data and reap benefits.&nbsp;</p>
<h3>1. Deep understanding of customer navigation behavior</h3>
<p>It helps businesses understand how users navigate and interact with various online platforms (apps/websites). Regarding e-commerce websites, their online presence is the only point of contact with a customer. Getting this experience right is, therefore, critical, and clickstream data gives brands the data that can help them better understand the most comfortable navigation path for a customer.</p>
<h3>2. Optimized User Experience (UX)</h3>
<p>Clickstream data is a goldmine of information containing details on views: products searched for, products bought and rejected, average time spent on a page, following action, CTAs clicked, and more. This information allows brands to deliver a more personalized and easy-to-navigate user experience.&nbsp;</p>
<p>YouTube, for example, suggests videos to a user basis the kind of videos saved for &lsquo;watch later&rsquo; and the kind of videos a user completed in the past. This makes the user experience flawless as they can quickly locate the content they are searching for.</p>
<h3>3. Hyper-personalized targeted marketing campaigns</h3>
<p>Clickstream data enables marketing teams to segment customers. It gives insights like where your customers are coming from, their age group, what is it that they are looking for, and many such small details. Armed with this data on what excites a specific customer group, what engages them more, and what is helping get those conversions. This clear understanding of what interests a particular set of users enables marketing teams to design targeted, personalized, and effective marketing campaigns for each.&nbsp;</p>
<p>For an online store selling apparel and cosmetics, sharing promotions and recommendations for cosmetics to a user buying apparel is of no use. It may be perceived as spamming for them, resulting in irritating them. Clickstream data helps group customers and send them information and recommendations relevant to them.</p>
<h3>4. Reduced Cart Abandonment</h3>
<p>The cart abandonment rate in 2023 was <a href="https://baymard.com/lists/cart-abandonment-rate">70.19%,</a> which is enormous. By using clickstream data, online retailers can identify the pain points in the checkout process, why users do not complete a purchase, and what helps convert those carts into actual purchases. With this information, a business can iron out the pain points, ensuring a smooth, hassle-free checkout and driving more sales. </p>
<p>Bikash Joshi, Sr. Product Manager &amp; UX Design Lead at Jio, shared his insights on key points to consider while <a href="https://www.linkedin.com/posts/bikashjoshi_clickstreamanalysis-userbehavior-webanalytics-activity-7065891460771946496-GZP-/?utm_source=share&amp;utm_medium=member_desktop">analyzing clickstream data</a>.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Analyzing_Clickstream_Data_Social_Proof_0501e83389.webp) 

<p>Now, let&rsquo;s delve deep into the components that qualify as clickstream data.</p>
<h2><strong>Components of Clickstream Data</strong></h2>
<br/>
<p><strong>1. Page Views:</strong> A record of every page a user visits on your website.&nbsp;</p>
<p><strong>2. Click Paths:</strong><strong></strong> The sequence or order in which a user visits pages on a website. If a user visits a homepage, clicks on the product page then clicks on resources, selects case-study, and heads to the page to read a case study, all of the pages a customer visited on your website get recorded. This information can tell you a lot about how users navigate your content and help you identify ways to improve the user navigation experience on your website.</p>
<p><strong>3. Entry and Exit Pages:</strong> The entry page is where a user starts their journey on your site, and the exit page is the last one they visit before leaving. Knowing these can help you understand what attracts users to your site and what might be causing them to go.</p>
<p><strong>4. Time Spent: </strong>A high bounce rate indicates potential problems on your website. In the internet world, if a user visits your website and leaves without visiting any pages, it is known as a bounce. Time spent measures how long a user spends on a specific page or your site. This data can help identify pages on your website that are appealing to your users, and you can accordingly make improvements on other pages to enhance user engagement.</p>
<p><strong>5. Clicks: </strong>Any click on a link, button, image, or any interactive element on a website the user clicks on gets recorded. Understanding the factors that engage customers more can help improve other similar elements on your website.</p>
<p>For example, you may have CTAs in 2 color schemes (orange and blue) on your website, and by analyzing the clicks, you may realize that orange CTAs have a higher click rate than blue. Then, consider changing other CTAs to orange as well.</p>
<p><strong>6. Referring URLs:</strong> It helps identify external links that may drive traffic to your website. This data lets you understand which external sources drive traffic to your site. A Google search, social networking, or a reference link on another website. This statistic shows how much SEO your website needs.</p>
<p><strong>7. Search Terms and Queries: </strong>If your website has a search function, this data records what terms users are typing in. It's a direct insight into what your users are looking for.</p>
<p><strong>8. Error Messages:</strong> If users encounter any errors while on your site, like a broken link or a 404-page error, it gets logged here. This can help in identifying and fixing problems on your website.</p>
<p><strong>9. Downloads and Uploads:</strong> Records any files or documents that users might download from your site or upload, like PDFs or images.</p>
<p><strong>10. Shopping Cart Activity (for e-commerce sites):</strong> This tracks actions related to online shopping, such as adding items to a cart, removing them, or making a purchase.</p>
<p>Here&rsquo;s a case study to see how clickstream data helps businesses make intelligent decisions, keep customers interested, and boost conversions.</p>
<h2>Case Study: Leveraging Clickstream Data to Boost Sales</h2>
<p><strong>The Business: </strong>A small online store selling handmade crafts with a fairly decent presence and recognition among its customers.</p>
<p><strong>The Challenge: </strong>Despite having a steady stream of visitors, sales declined, and many users left the site without making a purchase. The store had invested in paid ads on Google and was active on their social media page. However, there needed to be a way to track from which source they were getting more conversions.</p>
<p><strong>Step 1: Gathering the Data</strong></p>
<p>In the initial phase of enhancing the store's digital strategy, a pivotal move was made to incorporate a clickstream data tool. This tool was instrumental in obtaining a wealth of information, such as user demographics, their behavior while navigating the website, and the specific paths they followed. It also provided insights into the products visitors most frequently searched. The store also aimed to identify potential bottlenecks by closely monitoring customer issues at checkout and tracking cart abandonment rates.</p>
<p><strong>Step 2: Observations from the Data</strong></p>
<p>After analyzing the data, the brand had the following findings:</p>
<ul>
<li>Most of the users came to the website through social media pages. Here, the brand had positioned itself as one that supported local crafts and artisans.</li>
<li>The average age group was 25-40; most visitors were in metro cities.</li>
<li>The visitors spent a long time on the 'About' page, which told the story of the crafters and also talked about the origin of specific craft techniques.</li>
<li>Many users visited product pages but only spent a little time there or added products to their carts.</li>
<li>The exit rate was highest on the checkout page.</li>
</ul>
<p><strong>Step 3: Inferences Drawn</strong></p>
<p>To begin with, the page did not do an excellent job of selling the products because it lacked an engaging image and detailed information on the item. Secondly, users were interested in the story behind the crafts, suggesting they valued authenticity and connection. Thirdly, being a small business, the checkout process could have been better, discouraging users from completing their purchases.</p>
<p><strong>Action Taken: </strong></p>
<ul>
<li>For Product Pages: The store improved product descriptions and added more photos from different angles. They also included short stories about the crafters behind each product.</li>
<li>For the 'About Us' page, they added video interviews with the crafters, giving a more in-depth look into their lives and crafts.</li>
<li>For the Checkout Process: They simplified the process by reducing the number of steps and adding multiple payment options. They also included reassuring elements like security badges to build trust.</li>
</ul>
<p><strong>Results</strong></p>
<p>After these changes:</p>
<ul>
<li>The add-to-cart rate improved by<strong> 25%</strong></li>
<br/>
<li>Time spent on the 'About' page increased, but more importantly, it translated to higher sales of products associated with featured crafters.</li>
<br/>
<li>The checkout abandonment rate dropped by<strong> 40%</strong></li>
</ul>
<p><strong>Conclusion:</strong></p>
<p>By listening to the silent feedback of clickstream data, the online store improved sales and provided users with a better, more engaging experience. This case proves that with a bit of observation and action, clickstream data can genuinely transform a business's digital journey.</p>
<h2>The Endgame</h2>
<p>With nearly <a href="https://thesocialshepherd.com/blog/ecommerce-statistics#:~:text=There%20Are%20Between%2012%20and,over%20the%20next%20few%20years.">24 million online stores</a> worldwide, the competition is intense. Traditional ways of analyzing user data no longer make sense in this ever-expanding realm of online shopping. Clickstream data offers easy-to-understand, real-time insights into customers'' online behavior.</p>
<p>Lifesight Connect, with its deep segmentation capabilities and the ability to give a 360-degree view of customer behavior and a unified customer profile, helps you create data-driven strategies. Features like real-time CDP, first-party ID graph, server-side tracking, and more help generate more revenue per customer. <a href="https://www.lifesight.io/demo">Book a demo</a> today to super-charge your business growth in a privacy-first era.</p>

What Is Clickstream Data & How Can It Help Your Business_.webp

Unlock the power of clickstream data! Dive into its definition, benefits, and strategies to boost your business. Discover a data goldmine today!

What Is Clickstream Data & How Can It Help Your Business?

<p>As a marketing analyst, have you ever looked at your business&rsquo;s user acquisition snapshot on Google Analytics and wondered what exactly are the sources for the &ldquo;direct&rdquo; and &ldquo;unassigned&rdquo; traffic?</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image1_34bf9ec58a.webp) 
<p><em>A snapshot of the Google Analytics dashboard with &ldquo;direct&rdquo; and &ldquo;unassigned&rdquo; traffic sources highlighted. What exactly are they?</em></p>
<p>Google says that the traffic attributed as &ldquo;Direct&rdquo; refers to the website visitors who land on your website after typing in your URL directly into their browser or by bookmarking and revisiting your website later. On the other hand, &ldquo;Unassigned&rdquo; traffic refers to sessions that are not attributed to any particular traffic source or medium.</p>
<p>Our marketing analyst in particular went on a quest to get into the depths of these traffic sources and track them down to the tee. She used UTM parameters for every link possible to understand the true origin of these traffic sources. For an established business replacing thousands of links was a cumbersome task. But our analyst was hellbound to uncover the truth.</p>
<p>The result?</p>
<p>She still couldn&rsquo;t identify the origin. Yes, the numbers for these traffic sources came down a little because of using UTMs, giving us an insight that even traffic from emails is sometimes counted under &ldquo;Direct&rdquo;. However, we still did not have clarity on what happens under the hood for these traffic sources.</p>
<p>So, we went on a new quest to gain a deeper understanding of Google Analytics.</p>
<h2>How does Google Analytics work?</h2>
<p>To keep things simple, we are going to look at Google Analytics in two distinct steps:</p>
<ol>
<li>The Data Gathering Process</li>
<li>The Analysis Power</li>
</ol>
<p>We will be providing the gist of both these steps without diving into the intricacies but we will make sure that you have enough information to go ahead and discuss it with your Marketing Data Scientist.</p>
<h3>1. The Data Gathering Process</h3>
<p>At its core, Google Analytics or GA is a tracking tool. It systematically collects data on website visitors and their interactions with the site. But the answer to its working lies in a clever interplay of JavaScript, cookies, and data science.</p>
<ul>
<li><strong>JavaScript Magic</strong>: Google Analytics operates by embedding a small piece of JavaScript code into a website&rsquo;s HTML. This code acts as a spy, gathering data on visitors&rsquo; actions. It records everything from page views to button clicks, form subscriptions, and much more.</li>
<li><strong>The Cookie Trail</strong>: Cookies are the breadcrumbs in the digital realm. Google Analytics relies on cookies to identify and track users. When someone lands on a website, a unique client ID is assigned via cookies. This helps in recognizing returning visitors and aggregating data.</li>
<li><strong>Data Collection</strong>: When a user interacts with the website, the JavaScript code sends data to Google&rsquo;s servers. This data is then processed and aggregated into comprehensive reports that include valuable information like the source of traffic, user demographics, the devices they use, and their behavior on the site.</li>
</ul>
<h3>2. The Analysis Power</h3>
<p>Once the data is collected, Google Analytics offers a treasure trove of analytical tools. These tools provide businesses with the capability to dive deep into the data and extract actionable insights. It answers questions like: Who visits the site? How long do they stay? What are the most popular pages? What leads to conversions? The answers to these questions are usually presented in different reports.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image2_31dd9f3f7c.gif) 
<p><em>A gif showcasing Google Analytics 4&rsquo;s reporting capabilities. Source: </em><a href="https://www.searchenginejournal.com/google-announces-new-reporting-features-in-ga4/410195/"><em>Search Engine Journal</em></a></p>
<ul>
<li><strong>Audience Analysis</strong>: Understanding your audience better with insights into their demographics, interests, and locations. This knowledge is invaluable for tailoring your content and marketing strategies.</li>
<li><strong>Acquisition Analysis</strong>: Analyzing the sources that drive visitors to your site. This can range from organic searches to social media referrals.</li>
<li><strong>Behavior Analysis</strong>: Delving into user behavior on your website. Learning which pages engage users the most and identifying potential bottlenecks in your conversion funnel.</li>
<li><strong>Conversion Tracking</strong>: Tracking your goals and measuring the effectiveness of your marketing campaigns. Google Analytics provides the data needed to optimize your efforts for higher conversions and ROI.</li>
</ul>
<p>On the surface GA&rsquo;s capabilities come across as a strong analytical tool. And do not get us wrong, it was, until the latest changes in the world of the internet and browsing.</p>
<h2>The Limitations of Google Analytics: Navigating a Cookie-less World</h2>
<p>In October 2020, Google launched the newest version of its analytics platform - Google Analytics 4. The main reason behind this significant change was the shift happening towards a cookie-less world and customers preferring a privacy-oriented browsing experience.&nbsp;</p>
<p>While Google Analytics 4(GA4) represents a significant leap forward in adapting to a changing digital landscape, it&rsquo;s not without its limitations. In recent years, the world of online data collection has faced seismic shifts, and GA4 has had to adapt to new challenges in tracking user interactions effectively.</p>
<p>Let&rsquo;s talk more about them&hellip;</p>
<h3>Deprecation of Cookies</h3>
<p>The deprecation of website cookies is perhaps one of the most formidable challenges facing GA4. Cookies, once the backbone of web tracking, are being phased out by browsers and privacy regulations. Users now have greater control over cookie tracking, and they&rsquo;re becoming more cautious about sharing their data. This shift directly impacts GA4&rsquo;s ability to monitor user behavior accurately.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image8_461b64d9af.webp) 
<p><em>Tobin Thomas, the founder of Lifesight, talking about how Google Analytics 4 falls short in accurate measurement and reporting. Source: </em><a href="https://www.linkedin.com/posts/activity-7118113053262413825-c9NL/"><em>LinkedIn</em></a></p>
<p>Without cookies, the reliability of tracking returning visitors and piecing together complete user journeys is compromised. Users&rsquo; ability to reset cookies or block them entirely further exacerbates this issue. The inevitable result is a fragmented view of user interactions on websites, making it difficult for businesses to get a comprehensive understanding of their audience.</p>
<h3>Ad Blockers: Digital Shields Against Tracking</h3>
<p>The rise in ad blocker usage is another obstacle for Google Analytics. Ad blockers are browser extensions or applications that prevent the loading of advertising content on websites. However, they have an unintended consequence - they block Google Analytics scripts as well.</p>
<p>This means that even if users have not explicitly opted out of web tracking through browser settings, ad blockers might be automatically doing so on their behalf. As a result, GA4 data is missing crucial insights from this segment of users, potentially underestimating website traffic and skewing user behavior data.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image4_3389b7ea37.webp) 
<p><em>Ad blockers have been growing over the past few years making tracking a hassle for marketers. The insurgence of privacy browsers too has added more weight to this challenge. Source: </em><a href="https://www.admonsters.com/accceptable-ads-rise/"><em>AdMonsters</em></a></p>
<p>The impact of ad blockers on tracking capabilities not only hinders the accuracy of visitor statistics but also raises ethical questions. Users employ ad blockers for privacy, security, and improved web performance. As such, businesses must navigate these challenges delicately, respecting user preferences while still seeking valuable insights.</p>
<p>So, what&rsquo;s the road ahead?</p>
<h2>Marketing Mix Modeling: A Path to Unbiased, Comprehensive Analytics</h2>
<p>Where Google Analytics struggles with privacy and inaccurate tracking, Marketing Mix Modeling (MMM) emerges as a robust solution, offering a path forward.</p>
<h3>The Advantages of Marketing Mix Modeling:</h3>
<ol>
<li><strong>Privacy-Compliant Data Collection</strong>: One of the fundamental differences between MMM and GA is the way they handle data privacy. MMM respects user privacy by design, using aggregated data and modeling techniques, rather than relying on individual user tracking. This approach allows marketers to gather critical insights while adhering to evolving privacy regulations and user preferences, making it a more robust choice for privacy-compliant data collection.</li>
<li><strong>A Broader Perspective</strong>: Marketing Mix Modeling provides a more comprehensive view of your marketing impact. While GA4 excels at website and digital interaction tracking, MMM takes a holistic approach. It not only considers online channels but also offline media like TV, radio, and print advertising. This broader perspective is invaluable in understanding the real-world impact of marketing efforts.</li>
<li><strong>Statistical Rigor</strong>: Marketing Mix Modeling utilizes statistical techniques like regression analysis to understand the relationships between variables. This methodology provides more robust, quantifiable insights into marketing performance. It helps marketers move beyond surface-level metrics or intuition and dive deep into the data, uncovering what truly drives business results.</li>
<li><strong>Long-Term Forecasting</strong>: MMM isn't just about the here and now. It enables marketers to forecast the long-term impact of their marketing activities. Businesses can plan and allocate resources more effectively by examining historical data and market conditions. In contrast, GA4 is often seen as a tool for immediate, short-term insights.</li>
<li><strong>MMM as a Privacy-Safe Solution</strong>: With an emphasis on maintaining privacy and complying with regulatory changes, Marketing Mix Modeling provides a reliable alternative in a post-cookie and privacy-legislated world. The legal hurdles posed by some privacy regulations are less of a concern when using MMM, giving peace of mind to marketers.</li>
</ol>
<h2>MMM in action: How Marketing Mix Modeling Works</h2>
<p>To help you understand the possibilities MMM unlocks, we have taken the liberty of diving deeper into the mechanics of Marketing Mix Modeling with a practical example, utilizing Lifesight as our MMM tool.</p>
<p>As the first step, MMM needs data collected and sourced from all marketing channels possible. Instead of only relying on cookies, MMM needs data from various sources like your sales data, marketing expenses, customer data, competitor information, economic indicators, and data from ad platforms like Google, Facebook, and the like. The more variables you feed your MMM model, the more accurate will be the prediction.</p>
<p><strong><em>Pro tip</em></strong>: <em>Using a CDP can ease this task as it will have all your customer and business data in a single place.&nbsp;</em></p>
<p><a href="https://www.lifesight.io/cdp"><em>Learn more about a CDP</em></a></p>
<p>Since Lifesight comes with a CDP, we could easily pull the data for a food supplementary e-commerce brand that uses both online and offline advertising channels.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image7_0affc34bbb.webp) 
<p><em>An ecommerce brand&rsquo;s primary spends pulled from Lifesight&rsquo;s CDP and seen on its MMM tab</em></p>
<p>Upon running this data through our MMM model, the brand got a comprehensive perspective that covers both online and offline channels, helping you understand how all marketing efforts contribute to the brand&rsquo;s goals.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image5_c22227aeec.webp) 

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image6_380aeb84e5.webp) 
<p><em>Lifesight&rsquo;s MMM model showcasing how every advertising channel, online and offline, have contributed to total revenue along with listing the platform CPA.</em></p>
<p>In order to help marketers understand the customers in a better way, Lifesight also provides the capability to dive deeper into the customer journeys and analyze how and when a sale happens.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image3_2299a6e629.webp) 
<p><em>Lifesight&rsquo;s Customer Journeys feature in action showcasing how the platform not only helps you understand the revenue channels but also dive deeper into your customers&rsquo; shopping behavior<br /><br /></em></p>
<h2>Conclusion: Evolving Beyond Google Analytics with Marketing Mix Modeling</h2>
<p>The need for robust measurement and tracking tools has never been more critical. Google Analytics has been a steadfast companion for marketers, offering valuable insights into user behavior, website performance, and digital advertising effectiveness. However, as we&rsquo;ve explored, it's not without its limitations.</p>
<p>This is where Marketing Mix modeling emerges as a strategic alternative that marketers should consider. MMM offers a holistic and privacy-compliant approach to measuring the effectiveness of marketing efforts.</p>
<p>With its statistical credibility, MMM allows marketers to back their reports with hard data, enabling them to understand causation and correlations, not just surface-level associations.&nbsp;</p>
<p>Google Analytics remains a valuable tool for tracking digital interactions, but the changing digital landscape necessitates a more comprehensive approach to measurement. By considering MMM as part of their analytics toolkit, marketers can gain deeper insights and make more informed decisions.</p>
<p>If you&rsquo;re interested in exploring the power of Marketing Mix Modeling, sign up for a demo of Lifesight&rsquo;s MMM tool. Lifesight offers transparent, GDPR-compliant, and unbiased insights, helping you change the narrative, challenge the status quo, and drive genuine growth in your marketing strategies. Make data-driven decisions that move your business forward and discover the potential of MMMM today.</p>
<p><a href="https://www.lifesight.io/demo">Sign up for Lifesight&rsquo;s MMM tool</a></p>

2-Why should you Opt for Marketing Mix Modeling over Google Analytics_.png

A data-driven showdown between Marketing Mix Modeling and Google Analytics to help you make smarter marketing decisions and learn which is the right fit for your business.

Why should you Opt for Marketing Mix Modeling over Google Analytics?

<p>Seasoned marketers have been familiar with the marketing mix modeling (MMM) framework for decades. Back in the 1950s, when promotion was restricted to radio, television, newspaper, magazine, and billboard advertisements, marketing mix modeling was one of the best KPI tracking tools to estimate the impact of these advertisement campaigns on an organization&rsquo;s overall ROI.&nbsp;</p>
<p>As marketing evolved and new digital channels like LinkedIn, Facebook, and Google emerged, it was time for new-age marketers to try advanced data analytics methods like multi-touch attribution (MTA).&nbsp;</p>
<p>However, despite being an effective technique, MTA is unsuitable for growing ecommerce businesses. Firstly, MTA&rsquo;s capability is restricted to only three digital channels at a time for ad performance tracking. Secondly, MTA uses individual user-level data to track ad metrics, and smartphone manufacturing brands like <a href="https://www.apple.com/legal/privacy/en-ww/#:~:text=Apple%20may%20share%20personal%20data,for%20their%20own%20marketing%20purposes.">Apple</a> have introduced strict guidelines against user-level data sharing.&nbsp;</p>
<p>This is why ecommerce marketers use a refined and more evolved version of <a href="https://www.lifesight.io/blog/marketing-mix-modeling-importance">marketing mix models</a>. Marketing mix modeling performs multi-channel KPI tracking through statistical methods and capitalizes on aggregate data like overall sales, store distribution, seasonality, inflation, and more.&nbsp;</p>
<p>That&rsquo;s why adopting marketing mix modeling for ecommerce KPI tracking is important, and in this article, we will explain this more comprehensively.&nbsp;</p>
<h2>The Role of Marketing Mix Modeling in Ecommerce&nbsp;</h2>
<p>Marketing mix modeling helps ecommerce retailers estimate how marketing ad spending, the price of products, the assortment of various items, and other promotional strategies impact the store's overall sales.&nbsp;</p>
<h3>Optimizing Ad Spending&nbsp;</h3>
<p>Trained marketing mix models combined with statistical techniques like saturation curves can help optimize ad spending. The benefits of using marketing mix modeling for optimized ad spending include:&nbsp;</p>
<ul>
<li>A trained marketing mix model leverages the relationship between media strategies and dependent variables like market trends and seasonality to capture how a campaign performs over time. As a result, ecommerce marketers can identify the point of diminishing return for each campaign and plan ad spending accordingly. Thus, there is no additional spending beyond that point of saturation.&nbsp;</li>
</ul>
<ul>
<li>With the combination of saturation curves and mix models, marketers can analyze historical data to predict future ad spending and visualize the impact of ad spending on the outcomes. This, again, guides marketers to allocate ad budgets efficiently.&nbsp;</li>
</ul>
<h3>Price Optimization&nbsp;</h3>
<p>Price elasticity is critical for ecommerce stores as it explains how the demand for a specific product changes due to the change in its price. Marketing mix modeling offers insights into price elasticity. As an outcome, marketers predict how a changed pricing strategy impacts a product&rsquo;s overall sales and adjust the pricing plans accordingly.&nbsp;</p>
<h3>Product Placement &amp; Assortment</h3>
<p>Marketing mix models focus on identifying the ideal variables to generate maximum revenue from marketing operations. Some key variables include product, distribution channels, package size, customers&rsquo; location, timing, and so on.&nbsp;</p>
<p>Let&rsquo;s take the example of Ikea. The company primarily sells furniture and home decoration items through their website and offline stores. With the help of marketing mix modeling, it can identify which distribution channels, location, and package size can be more profitable for it in the long run.&nbsp;</p>
<h3>Promotion Effectiveness</h3>
<p>Marketing mix models help marketers to measure the power of various advertising campaigns in a business. The models rely on different dependent and independent datasets to identify how these campaigns impact overall sales and their roles in driving conversion for an ecommerce business.&nbsp;</p>
<p>Suppose you have just started your ecommerce store journey and are trying multiple promotional techniques like paid ad campaigns, social media marketing, search engine optimization (SEO), and so on. Due to budget constraints, you must pick any of these channels to invest in. Based on historic performance, you can use marketing mix modeling to predict which marketing channel will be most profitable for you.&nbsp;</p>
<p><a href="https://www.linkedin.com/in/neil-roy/">Neil Roy</a>, VP of Marketing at Lifesight, talked about what sort of macro-level questions MMM can answer for ecommerce brands</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image3_f66f8a9d5c.webp) 
<h2>Important Ecommerce KPIs for Media Mix Modeling</h2>
<p>Below are some of the most important ecommerce KPIs that you can track through a marketing mix model: <br /><br /></p>
<ul>
<li><strong><strong>Revenue incl. </strong></strong>VAT before returns (Rev. incl. VAT pre-returns): This KPI represents total sales revenue, including value added tax (VAT), before deducting factors like returns or refunds in an ecommerce business transaction.</li>
</ul>
<p>By tracking this KPI, ecommerce stores identify accurate pricing, promotion, and product strategies and enhance precision in the marketing mix model.</p>
<ul>
<li><strong><strong>Revenue excl. </strong>VAT after returns (Rev. excl. VAT post-returns)</strong>: This KPI refers to total ecommerce sales after deducting returned items It provides insights into ROI calculations and helps marketers with strategic decision-making.</li>
</ul>
<ul>
<li><strong><strong>Revenue from customers who did not accept analytics cookies (No-cookie Rev.): </strong></strong>Ecommerce sales generated without cookies are called no-cookie revenue and it determines which marketing channels and strategies will be most effective for customers who value privacy?</li>
</ul>
<ul>
<li><strong><strong>Gross Margin after returns (GM post-returns): </strong></strong>When the cost of goods sold is deducted from the revenue, the gross margin after returns is calculated. Ecommerce and DTC companies can gauge profitability based on GM-post return rate.&nbsp;</li>
</ul>
<ul>
<li><strong><strong>Customer Lifetime Value (CLV): </strong></strong>An ecommerce business's CLV is the revenue they expect from customers throughout their relationship. Marketing budgets can be allocated more effectively when marketers understand CLV as it predicts long-term profitability from each customer.&nbsp;</li>
</ul>
<ul>
<li><strong><strong>Customer Acquisition Cost (CAC): </strong></strong>There are costs related to marketing, advertising, paying the sales team, and so on. By monitoring and contrasting CAC with CLV, ecommerce retailers evaluate the efficacy of their marketing channels.&nbsp;</li>
</ul>
<ul>
<li><strong><strong>Return on Ad Spend (ROAS): </strong></strong>This KPI determines the revenue from advertising campaigns for an ecommerce store relative to the expense of those ads. Tracking ROAS data offers valuable insights into how online advertising affects overall sales.</li>
</ul>
<ul>
<li><strong><strong>Advertising Cost of Sale (ACOS): </strong></strong>ACOS is the ratio of advertising spending to total revenue generated from advertisements.</li>
</ul>
<ul>
<li><strong><strong>Cost Per Acquisition (CPA): </strong></strong>CPA is the overall cost of obtaining a new customer. Businesses determine the most profitable marketing channels for campaign execution by tracking CPA in marketing mix models. <br /><strong><br /></strong></li>
</ul>
<p>Ecommerce stores can estimate the least expensive acquisition strategies by comparing CPA across several channels, including social networking platforms, search ads, and email marketing.<br /><br />For example, MMM model shows the share in ad spend and return for each paid channel which would enable marketers to identify if channel is underperforming or over performing and redistribute ad budgets accordingly</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/MMM_model_shows_the_share_in_ad_spend_and_return_for_each_paid_channel_6b627eddae.png) 
<p><a href="https://www.lifesight.io/glossary/acquisition-cost">Learn more about acquisition costs here</a>.</p>
<ul>
<li><strong><strong>Average Order Value (AOV):</strong></strong> It refers to the typical purchase amounts made by customers on a website during a given time frame. Customers who spend more money per transaction have greater AOVs, which boosts overall revenue.&nbsp;</li>
</ul>
<h2>Challenges Ecommerce Businesses Face When Tracking KPIs&nbsp;</h2>
<p>For large ecommerce businesses with multiple product lines, KPI tracking in fixed time frames is a significant challenge.&nbsp;</p>
<p>Let&rsquo;s consider <a href="https://www.statista.com/topics/1243/nike/#topicOverview">Nike</a>, which has three broad product segments&mdash;shoes, apparel, and equipment. For each of these segments, Nike offers multiple product lines. Multiple KPIs will be tracked for each business segment to understand which product lines perform the best, which promotional campaigns acquire the highest return, which ad channels generate the highest revenue, and so on.&nbsp;</p>
<p>It is impossible to track all these KPIs manually as several challenges may occur, like missing out on critical data, creating consolidated reports, and tracking online and offline campaigns simultaneously.&nbsp;</p>
<p>In this section, we are explaining these challenges in more depth:&nbsp;</p>
<h3>Data Overload and missing out on Critical Information</h3>
<p>Ecommerce stores generate large volumes of data every day. These data types include sales data, in-store data, in-app data, shopper data, product data, and so on. If you are a marketer responsible for analyzing these vast datasets, suffering through data overload is normal.&nbsp;</p>
<p>While marketers often rely on tools like Google Analytics (GA) for data analysis, it is common to miss critical datasets because GA doesn&rsquo;t capture conversions from users who don&rsquo;t accept cookies.&nbsp;</p>
<h3>Real-time Monitoring and Reporting&nbsp;</h3>
<p>As an ecommerce marketer, even though you navigate through the data overload and find a way to collate all required ecommerce KPI datasets in one place, another problem still needs to be solved. You need to find a way to monitor these raw datasets in real time and create advanced visual reports for stakeholders to decode the underlying insights from these datasets.&nbsp;</p>
<h3>Offline Data Tracking becomes Difficult&nbsp;</h3>
<p>Digital channels might be dominating the world of ecommerce, but specific offline marketing channels like billboards, newspaper advertisements, and television ads still contribute to the success of an ecommerce store.&nbsp;</p>
<p>Modern MTA tools cannot track ecommerce KPIs for offline channels as these tools need user-level data for KPI tracking. Therefore, ecommerce brands often end up not focusing on high-potential offline channels.&nbsp;</p>
<p>Learn more about the common data challenges related to marketing mix models <a href="https://www.lifesight.io/blog/data-challenges-in-marketing-mix-modeling">here.</a>&nbsp;</p>
<h2>How does MMM Solve These Challenges?&nbsp;</h2>
<p><a href="https://www.lifesight.io/marketing-mix-modelling">Lifesight&rsquo;s Marketing Mix Model</a> helps ecommerce stores measure the accurate performance of their online and offline marketing campaigns with an automated marketing mix model. Here&rsquo;s how.&nbsp;</p>
<h3>Seamless integration and continuous calibration&nbsp;</h3>
<p>Lifesight&rsquo;s automated marketing mix models can integrate with multiple data sources, regardless of platform type. It integrates with GA, Shopify, Google Ads Manager, Facebook Business, LinkedIn, Instagram, TikTok, and other data sources where ecommerce stores usually run ad campaigns.&nbsp;</p>
<p>Marketers can easily access all data types and formats and merge critical datasets related to sales, products, and customers with their web analytics KPIs.&nbsp;</p>
<p>Additionally, Lifesight&rsquo;s marketing mix model auto-integrates and calibrates with your existing attribution models to generate marketing mix modeling data feeds and retain models with just one click.&nbsp;</p>

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<h3>Consolidated Real-Time Reporting&nbsp;</h3>
<p>Lifesight&rsquo;s Marketing Mix Model can automatically integrate with diverse data sources to develop a consolidated overview of multiple ecommerce KPIs. By combining Lifesight&rsquo;s MMM with various data connectors, ecommerce stores generate standardized data schemas, pipelines, and outputs.&nbsp;</p>
<p>That means marketers can collect KPIs from multiple sources and convert them into simple formats that are easy to analyze. This model also helps you eliminate broken datasets and inconsistencies across various channels to create intuitive dashboards.</p>
<p>You can also integrate datasets from GA to make your reports even more in-depth. The best part is that you generate these real-time reports within a few minutes with the help of Lifesight&rsquo;s automation features and avoid manual interventions.&nbsp;</p>
<h3>Seamless Offline Campaign Measurement&nbsp;</h3>
<p>Lifesight empowers ecommerce stores with self-service data collection regarding offline campaign performance measurement. You can download <a href="https://www.lifesight.io/resources?filters=Checklists">Lifesight&rsquo;s templates and checklists</a> to collect and add inputs related to offline campaigns.&nbsp;</p>
<p>Make sure to input the datasets based on Lifesight&rsquo;s guidelines so that the MMM captures required datasets and analyzes the effectiveness of offline campaigns on overall sales.&nbsp;</p>
<h2>Final Thoughts&nbsp;</h2>
<p>If a large part of your marketing responsibilities is tracking ad campaigns, identifying insights from those campaigns, and forming strategies for future business goals, you must learn to leverage marketing mix models well.&nbsp;</p>
<p>Learning to track the ecommerce KPIs with your marketing mix model will help you create real-time, consolidated marketing reports by auto-integrating with multiple data sources. Use these reports to convince your C-suite on how the marketing strategies will impact overall sales.&nbsp;</p>
<p><em>Get started with Lifesight&rsquo;s automated marketing mix model to explore ecommerce KPI tracking further. </em><a href="https://www.lifesight.io/demo"><em>Book a demo!</em></a><br /><br /><br /></p>

Why Marketing Mix Modeling is Key to Ecommerce KPI Optimization_.png

Explore how Marketing Mix Modeling plays a vital role in enhancing Ecommerce KPIs. Learn how to leverage its capabilities to propel your business to new heights of success.

Why Marketing Mix Modeling is Key to Ecommerce KPI Optimization?

<p>In the constantly changing digital world, the data environment is going through a major change. A new era of transparency and permission is replacing the era of "don't care" data collection, which is represented by the disappearance of third-party cookies and the change in privacy laws. A clear truth stands out against this changing background: first-party data isn't just a buzzword; it's the foundation of effective and ethical marketing.</p>
<p>The question arises: How can businesses leverage this paradigm shift and adeptly harness this invaluable resource? The answer lies in Customer Data Platforms (CDPs), the catalysts propelling the first-party data revolution. Within this blog, we'll delve into the pivotal role of CDPs in facilitating the acquisition, organization, and optimization of your first-party data.&nbsp;</p>
<h2>Why Is Third-Party Data Being Phased Out?</h2>
<p>For a long time, advertisers and marketers used third-party cookies as their favorite way to track users' journeys across different websites, get insights, and offer personalized interactions. In an effort to deliver more relevant and engaging ads, marketers spent $22 billion dollars on third-party data in 2021. However, the uncontrolled accumulation of data raised serious privacy concerns, which made regulators and the general public angry.&nbsp;</p>
<p>Recently, a study conducted by Boston Consulting Group and Google found that:</p>
<ul>
<li>Two-thirds of people only want to see ads that are relevant and useful to them</li>
<li>64% of people don&rsquo;t trust companies to protect their data.</li>
</ul>
<p>This response led tech giants like Google and Apple to take decisive action by phasing out third-party cookies from their platforms.&nbsp;</p>
<p>David Temkin, Director of Product Management, Ads privacy, and Trust at Google, said</p>
<p><em>&ldquo;People shouldn't have to accept being tracked across the web in order to get the benefits of relevant advertising. Advertisers don't need to track individual consumers across the web to get the performance benefits of digital advertising. Advances in aggregation, anonymization, on-device processing, and other privacy-preserving technologies offer a clear path to replacing individual identifiers.&rdquo;</em></p>
<p>This turning point forces marketers to rethink their strategies and look for more sustainable and ethical alternatives. Beneath the surface of this seemingly challenging scenario lies a chance to connect with customers in a precise, meaningful way that is based on their consent, mostly through the strategic use of first-party data.</p>
<h2>First-Party Data: The New Goldmine</h2>
<p>First-party data is the treasure trove of information that companies get directly from their interactions with customers. It includes website visits, mobile app engagement, social media interactions, customer feedback, completed purchases, and more. In contrast to third-party data, which can be inaccurate or out of date, first-party data is accurate and up-to-date, and it gives real-time insights into how customers act and what they like.</p>
<p>One of the best things about first-party data is that it can be used in many ways. Whether harnessed for <a href="https://www.lifesight.io/blog/behavioral-segmentation">customer segmentation</a>, precision-targeted advertising, or predictive analytics, its potential applications seem infinite. Importantly, because this data comes from the heart of customer experiences, it helps marketing become more sophisticated and personalized.&nbsp;</p>
<p>As you directly collect this data from your customers, often fortified with their explicit consent, you're navigating on firmer legal ground within data privacy regulations like GDPR or CCPA.&nbsp;</p>
<p>Regulations addressing the collection and use of customer data exist in every region across the globe with new regulations constantly emerging. Having a plan in place for managing user privacy choices is the bedrock of a strong first-party data strategy.</p>
<h2>How marketers should think about building a first-party data strategy?</h2>
<p>We must all recognize the importance of user-shared data and the need for clear privacy policies that allow user control and transparency.&nbsp;</p>
<p>Every interaction your consumer has with your company provides an opportunity for you to learn more about them and build a better relationship. That is why it is critical that you have the tools and consent to manage their data. So, how do you go about doing that?</p>
<p>Let's start with information about people who visit your site. You can use the information people share when they use your website more effectively if you set up a strong tagging system. Setting up site-wide tags can help you use first-party cookies to get more accurate conversion data. When you use tools like Google's global site tag or Google's Tag Manager, you can tag things in this way. Mobile app users can also share first-party info. You can get a software development kit for your mobile app, which will help you track what people do inside the app.&nbsp;</p>
<p>But what do you do when direct consumer isn&rsquo;t viable? <a href="https://www.linkedin.com/in/jaylenbaca/">Jaylen Baca</a>, global product lead for privacy at Google, suggests using partnerships or B2B deals to connect directly with the users and build up your first-party data.</p>

<iframe width="780" height="450" src="https://www.youtube.com/embed/IrRAAYT8Bkg?si=Em3-w2Pls2b23j15" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
<p><a href="https://www.youtube.com/watch?v=IrRAAYT8Bkg">Source</a></p>
<p>For example, if you are a food and beverage brand, you may explore partnering with a home meal kit vendor to expand your customer reach and data.&nbsp;</p>
<h2>How will it change the consumer experience?</h2>
<p>Now, most of you are probably wondering if first party data activation will result in more relevant advertising and fewer obnoxious ones, or if buyers will cease seeing commercials for a product after they actually buy it.</p>
<p>You see, the great thing about first-party data activation is that it will force companies to tell better stories. By adding all of these different pieces of data together, they can make a better personas that they can use to sell better stories. Consumers will have a better experience and should be able to have better conversations with their brands across all of the different touch points and platforms in a responsible and appropriate way.</p>
<h2>The Central Role of CDPs</h2>
<p>As per Polaris Market Research, the global customer data platform market size is expected to reach USD 49.80 billion by 2030 at a CAGR of 35% during the forecast period.&nbsp;</p>
<p>Here&rsquo;s a break down of the CDP market for your better understanding.</p>

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<p>In simpler words, <a href="https://www.lifesight.io/blog/guide-to-customer-data-platform">CDPs </a>are a unified repository where data from various touchpoints like websites, mobile apps, email marketing platforms, CRM systems, and even offline sources converge. They also offer tools to manage data privacy settings and consent mechanisms, ensuring that the first-party data is stored and utilized in compliance with data protection regulations. This feature is crucial in the current climate of increasing scrutiny over data handling practices.</p>
<p>But how exactly do they accomplish this? Let&rsquo;s break down the mechanisms and features that make CDPs indispensable for leveraging first-party data.</p>
<p>One of the most significant challenges that businesses face is data fragmentation. With multiple platforms and touchpoints generating data, creating a single view of the customer can be daunting. CDPs address this by unifying all first-party data sources into a centralized hub, thus eliminating data silos. Moreover, CDPs excel at capturing and processing first-party data in real-time. This capability allows marketers to respond to customer behaviors instantaneously, whether that involves personalizing a webpage or triggering a targeted email.</p>
<p>Beyond just collecting and centralizing data, CDPs offer features for data enrichment and segmentation. They can categorize and tag data based on specific criteria, making it easier for businesses to target specific customer segments.&nbsp;</p>
<p>This granularity enhances the efficacy of personalized marketing campaigns. Once first-party data is centralized and segmented, CDPs facilitate its activation across multiple marketing channels. Be it social media advertising, content personalization, or email marketing, the platform ensures that the data is readily available for real-time, multi-channel campaigns.</p>
<p>Most businesses use the native form builder in next-generation CDPs for their NPS. Every response is added to the person's profile, which lets you target them (or leave them out of) certain marketing campaigns. Even <a href="https://www.linkedin.com/in/alita-harvey-rodriguez/">Alita Harvey-Rodriguez</a>, Founder of MI Academy, suggests this underutilized strategy of CDP, NPS, and Email.</p>

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<p>Also, one of the most underestimated areas of data management is user accessibility. CDPs often have user-friendly interfaces that allow even non-technical workers to extract insights, create segments, and execute data-driven campaigns. This democratization of data access increases the value of first-party data across the business.</p>
<h2>Optimizing Data Collection for First-Party Data</h2>
<p>Gathering data isn't just about piling up lots of information. It's about getting the right kind of information intelligently. This helps your Customer Data Platform (CDP) better understand your customers' wants.</p>
<p>The first step is to look at how you're currently collecting data. Are your online forms easy to use? Are you consistently collecting data across all your platforms? Consistency is vital to ensuring your data is reliable and can be combined in your CDP.</p>
<p>People interact with your business in different ways&mdash;through your website, customer service, live events, and even other online forms. You need to grab the chance to collect data in these situations. But remember, you need permission from customers to use their data. This helps with following laws and building trust.</p>
<p>When designing forms that collect data, balance is everything. If your form is too long and complicated, people won't finish it. You won't get the information you need if it's too short. This is where <a href="https://www.lifesight.io/blog/ecommerce-ab-testing">A/B testing</a> can help.&nbsp;</p>
<p>Once the data comes in, you've got to check it for mistakes. This ensures that the email addresses, phone numbers, and other information you collect are correct. That way, you can be sure you're reaching out to real people and improving their experiences.</p>
<p>Last but not least, remember that data changes. People move, change jobs, or get new contact info. You must keep your data up-to-date so your CDP always has the freshest information, allowing you to better serve your customers.</p>
<h2>Closing Musings</h2>
<p>Traditional data collection methods don't work in modern business, where consumer expectations are higher than ever. The era of third-party cookies has ended, and new rules about privacy have changed the data environment. In this significant change, customer-centricity is built on first-party data. The CDP is the marketer's best tool for doing great work, from ensuring data is correct to personalizing it in any way they want.&nbsp;</p>
<p>Are you ready to unleash the full power of your first-party data and create customer resonance? <a href="https://www.lifesight.io/demo">Click here</a> to learn more about Connect by Lifesight, which connects, analyzes, and galvanizes customer data to create powerful, targeted marketing that yields success.</p>

The Rise of First-Party Data and The Central Role of CDPs..png

Unleash the true potential of first-party data with the central role of Customer Data Platforms (CDPs). Discover how CDPs are revolutionizing data-driven decision-making.

The Rise of First-Party Data and The Central Role of CDPs

<p>Customer data is the core of any successful marketing strategy. It provides essential insights into customer behaviors and preferences, allowing marketers to personalize engagements and improve customer experiences.&nbsp;</p>
<p>Marketers today grapple with numerous complications from privacy constraints and disparate data sources. Data is often scattered across various platforms, making it challenging to derive meaningful insights. The heightened emphasis on user privacy also necessitates diligent data handling and compliance with stringent regulations.</p>
<p>&nbsp;As brands vie for the attention of increasingly discerning customers, traditional marketing tactics often need to be revised. The key to creating resonant, impactful interactions with your audience is leveraging customer data effectively.&nbsp;</p>
<p>In this post, we'll explore the fundamentals of customer data management and how to optimize it across three key phases: Collect, Unify, and Activate.</p>
<p>This methodology turns fractured data into unified customer intelligence. Collect establishes an expansive, trustworthy data foundation. Unify identities into complete customer profiles. Activate makes data operational across platforms and teams.</p>
<p>Whether you're a small business owner just dipping your toes into data analytics or a seasoned marketing executive at a large corporation, understanding how to collect, unify, and activate customer data is fundamental for building a robust engagement strategy.</p>
<h2>How To Collect Customer Data?</h2>
<p>The first step is ingesting customer data from all sources and touchpoints. This includes zero, first, second, and third-party data via APIs, batch uploads, etc. The goal is to amass a rich dataset encompassing all customer interactions.</p>
<p>There are numerous approaches to data collection, but the most common method is to utilize existing marketing resources. It involves recognizing various touchpoints across the customer journey, from website visits and social media interactions to purchase history and customer service interactions. Every customer interaction with your brand is a potential source of valuable data.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image1_c51878ac12.webp) 
<p>Say, for example, you are interested in learning more about how visitors interact with your website. Analytical tools allow you to monitor site activity and provide insights into what users most frequently require assistance with.&nbsp;</p>
<p>This step is crucial as it lays the groundwork for all subsequent actions, enabling a deeper understanding of customer behaviors, preferences, and needs.</p>
<h2>How To Unify Customer Data?</h2>
<p>Unify your data: The subsequent step after collecting data is to consolidate it into a single location. Marketers should aim to avoid the inconvenience of navigating between multiple platforms to access comprehensive data. In order to accomplish that objective, it is recommended to utilize a customer data platform.&nbsp;</p>
<p>With a CDP like <a href="https://www.lifesight.io/connect">Lifesight&rsquo;s Connect</a>, you can put together reports and charts that show patterns in your data about your customers and marketing. It allows you to retarget anonymous users via first-party identity graphs while managing privacy compliance.&nbsp;</p>

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<p>However, if you lack the necessary expertise, obtaining and setting up these tools can be a time-consuming, difficult, and expensive process so it's often best to <a href="https://www.lifesight.io/demo">work with an expert</a>.</p>
<h2>How To Activate Customer Data?</h2>
<p>Activating customer data means leveraging the unified, coherent customer data to drive marketing strategies, enhance customer engagement, and ultimately boost business outcomes. It is the stage where insights derived from unified data are operationalized to create more resonant and effective marketing initiatives.</p>
<h3>How does activating data help you?</h3>
<ul>
<li><strong>Audience Targeting:</strong> The process of activating data enables the identification of the target audience. Your customer data can tell you a lot about the types of people who buy from you and what they desire. This allows you to further refine your marketing efforts by segmenting your audience into smaller, more precise subgroups. Additionally, you can reach a more relevant audience and get more conversions if you market to people who have traits similar to the ones you find in your data.&nbsp;</li>
</ul>
<ul>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image4_45584fc77c.webp) 
<li><strong>Boost marketing efficiency: </strong>Data activation is also helpful in determining which marketing strategies get the best results. After all, it takes more than just knowing your audience to succeed. You need to reach them with a strategic marketing campaign as well. Data activation in marketing allows you to assess the efficacy of various strategies and identify areas for improvement. After that, you may double down on what's helping and abandon or improve what isn't.&nbsp;</li>
<li><strong>Budget reallocation:</strong> Last but not least, data activation can greatly improve the efficiency of your business&rsquo;s operations. From a marketing and sales perspective, it is possible to identify areas where financial resources are being expended on endeavors that do not contribute to organizational objectives.&nbsp;</li>
<p>Once a unified view of the customer is obtained, data activation involves creating and executing personalized marketing campaigns to engage customers effectively. A well-orchestrated, personalized campaign can result in improved customer retention and superior customer experiences, ensuring that the right message reaches the right customer at the right time.</p>
<p><a href="https://www.linkedin.com/in/piyush-gupta-b5368b122/">Piyush Kumar Gupta</a>, a founding member of EasyInsights, an analytics platform, offered his insights on data activation.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image3_78e7a10895.webp) 
<p><a href="https://www.lifesight.io/engage">Engagement platforms</a> empower brands to operationalize insights from unified customer data. It allows for the orchestration of tailored 1:1 messaging, which enhances customer retention and provides an enriched customer experience.&nbsp;</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image5_673edfcd3e.webp) 
<p>Moreover, activating customer data should be manageable. Therefore, most of the available engagement solutions are designed to allow marketers to enact many growth use cases without extensive technical resources, enabling efficient resource management and ensuring that brands can scale their campaigns without unnecessary expenditure.<br /><br /></p>
<h2>Postscript: Achieve data-driven engagement</h2>
<p>It&rsquo;s been established that customer data is a mandatory engagement strategy. But deriving value requires rigorously collecting, unifying, and activating it across your stack.&nbsp;</p>
<p>With the right strategies and technologies, fractured data transforms into unified customer intelligence - the foundation for personalized experiences. Modern solutions like Lifesight Connect simplify managing the data complexity. Connect provides end-to-end capabilities to collect, unify, and activate data with machine learning.&nbsp;</p>
<p>Now, you can deliver tailored omnichannel experiences powered by a complete view of each customer. You move from assumptions to intelligence-driven engagement. See Connect in action with a demo tailored to your goals.&nbsp;</p>
<p>The future is bright when you truly know your audience. Let us give you a personalized demo tailored to your stack and strategic engagement objectives. Discover how a <a href="https://www.lifesight.io/demo">next-gen customer data platform</a> can help you achieve data-driven engagement success.<br /><br /><br /><br /></p>

How to Collect, Unify, and Activate Customer Data to Drive Your Engagement Strategy.png

Discover the art of collecting, unifying, and activating customer data to supercharge your engagement strategy.  Learn the secrets to data-driven success.

How to Collect, Unify & Activate Customer Data to Drive Engagement

How to Collect, Unify and Activate Customer Data to Drive Your Engagement Strategy

MTA vs. MMM_ Determining The Right Fit For You (1).png

This blog post compares MMM and MTA marketing measurement models to help modern marketers decide which model is best for improving marketing results.

MMM vs MTA: Which Marketing Measurement Model is Right for You

<p>Recently, we hosted three marketing measurement experts to discuss and analyze what the future looks like for marketers in a cookieless world.</p>
<p>So, if you couldn&rsquo;t attend the event, here are some gold-standard strategies that you need to implement to accurately measure and provide ROI of your marketing efforts.</p>
<p>Alternatively, if you want to get the full scoop on everything that was discussed, get the full recording of the discussion here: <a href="https://www.lifesight.io/events/measuring-omnichannel-marketing-roi-panel"><em>Measuring Omnichannel Marketing ROI - Deep dive into the future.</em></a></p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Measuring_Omnichannel_Marketing_ROI_Deep_dive_into_the_future_b5504ad6f8.png) 
<h2>Takeaway 1: The privacy era brings in the importance of gold-standard measurement techniques</h2>
<p>The past five years have been marked by significant transformations in marketing - from the rise of omnichannel strategies to the digital revolution and now the imminent phasing out of cookies. These shifts have not only challenged marketers but also prompted them to adapt and rethink their approaches to measuring marketing ROI.</p>
<p><a href="https://www.linkedin.com/in/madanbharadwaj/">Madan Bharadwaj</a>, a marketing measurement expert and the founder of M^2, aptly described the current era as the &ldquo;privacy era of marketing&rdquo;. A period where user data, which was once considered the fuel for marketing measurement, is either vanishing or becoming increasingly restricted due to privacy concerns and evolving regulations. This shift has forced marketers to pivot their focus from measuring individual users to analyzing cohorts and audiences as a whole.</p>

<iframe width="780" height="450" src="https://www.youtube.com/embed/MoBj-ZE0wTM?si=GjoL9jYnUMXxqgf_" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

<p>Within this context, two approaches are gaining prominence in modern marketing measurement: <a href="https://www.lifesight.io/marketing-mix-modelling">Marketing Mix Modeling (MMM)</a> and <a href="https://www.lifesight.io/incrementality-testing">Incrementality Testing (IT)</a>.</p>
<h2>Takeaway 2: Move beyond attribution and drive toward the complexity of marketing measurements</h2>
<p>In today&rsquo;s digital landscape, consumers interact with brands across multiple touchpoints before making a buying decision. It&rsquo;s not uncommon for a person to click on a Facebook ad and convert, but it&rsquo;s equally likely that they take a more intricate journey involving engaging with the brand on social media, reading a blog post, watching an advertisement, and eventually directly visiting the website to make a purchase.</p>
<p>In both scenarios, your marketing efforts are working but how do you quantify the value of each marketing touchpoint within a customer&rsquo;s journey?</p>

<iframe width="780" height="450" src="https://www.youtube.com/embed/q8JA-E7mCFk?si=nGUctPctUrLUDfQO" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

<p>While solutions like MMM can help you derive the answer to the above question, <a href="https://www.linkedin.com/in/rokhrastnik/">Rok Hrastnik</a>, CEO of Guericom D.O.O and an international ecommerce leader, emphasized how it is important to delve deeper into the metrics like Blended Customer Acquisition Cost (CAC), CAC vs Contribution Margin, Retention by Cohorts to reveal the true story of business growth.</p>
<h2>Takeaway 3: Optimize for real business outcomes and align stakeholders</h2>
<p>The ultimate goal of marketing has always been to achieve real business outcomes. Whether it&rsquo;s driving sales, increasing brand awareness, or fostering customer loyalty, every marketing effort should contribute meaningfully to the bottom line.</p>
<p>Though MMM and IT emerge as two potent tools to help you measure the impact of your marketing, a key challenge in their implementation is aligning all the stakeholders involved.</p>
<p>Marketing is a multifaceted discipline that involves the CMO, marketing managers, and the boots-on-the-ground soldiers&ndash;the media buyers responsible for executing campaigns.</p>
<p>To harness the full potential of MMM and IT, Rok Hrastnik recommends having a seamless flow of information from the top of the hierarchy to the bottom. The insights gained from these tools should guide not only strategic decisions at the executive level but also the day-to-day actions of media buyers and marketing managers. When all stakeholders are aligned in their understanding of what drives business outcomes, a harmonious and efficient marketing function is the result.</p>
<h2>Takeaway 4: Solving the biggest puzzle in measurement: Reliable Unified Data</h2>
<p>While data is the lifeblood of modern marketing measurement, how organizations approach data collection can significantly vary, depending on their scale and complexity.</p>
<p>In large enterprises, data collection can be a formidable challenge. <a href="https://www.linkedin.com/in/gauravchandra03/">Gaurav Chandra</a>, Senior Data Scientist at Nestle, revealed that building a robust MMM model might stretch over several months because of the intricate process of securing sign-offs from various departments to gain access to the requisite data sources.</p>

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<p>Conversely, as revealed by Madan Bharadwaj, with fewer layers of bureaucracy and a leaner organizational structure, mid-market and SMBs can swiftly gather and consolidate data from various sources. This agility enables them to pivot swiftly in response to emerging trends, competitive shifts, or evolving customer behaviors.</p>
<h2>Takeaway 5: Transform millions of data messes into reliable sources of truth</h2>
<p>Marketing is inundated with a vast array of data from diverse sources&ndash;social media, email campaigns, websites, and more. This volume of data, though valuable, can become overwhelming and misleading if not managed correctly.</p>
<p>To overcome this challenge, all our experts unanimously stressed the importance of implementing robust data governance practices. This extends across all touchpoints within an organization, from marketing campaigns to sales transactions. Maintaining uniformity in data naming conventions, ensuring the use of correct UTM parameters, and adhering to standardized practices when collecting and storing data can all eliminate discrepancies and reduce errors.</p>
<p>At the same time, the panelists emphasized how effective data governance can directly impact metrics such as P&amp;L and EBITDA by providing accurate and reliable data for financial reporting and analysis.</p>
<h2>Takeaway 6: Choose a measurement solution that proves &amp; predicts ROI at scale</h2>
<p>The first piece of advice to identify the right measurement solution was to assess whether your business truly needs a measurement solution. Smaller enterprises may find that they don&rsquo;t necessarily require third-party measurement solutions at the outset. However, as the business scales, perhaps reaching a spending threshold of a million dollars annually, the numbers provided by ad platforms can become increasingly unreliable.</p>
<p>Secondly, avoid hasty decisions to adopt the latest measurement tool simply because it&rsquo;s popular. Instead, take a step back and thoroughly understand your business&rsquo;s unique requirements before making a decision. What works for one organization may not be suitable for another.&nbsp;</p>
<p>Finally, while evaluating measurement solutions, always <strong>prioritize customizability, flexibility, and scalability</strong>. These qualities are the hallmarks of a measurement tool that can grow with your business and adapt to its evolving needs.</p>
<h3>A peek into the future of measurements</h3>
<p>Within Lifesight's growth platform, we have Measure, our marketing measurement suite designed to assist businesses in optimizing marketing spend and achieving their goals amidst the privacy-first era's challenges, such as the decline of third-party cookies and difficulties in measuring offline channels. Measure operates through four core strategies: Universal Attribution provides comprehensive insights into the entire customer journey, Marketing Mix Modeling (MMM) helps understand marketing's impact on metrics like sales and brand awareness, Incrementality Testing (IT) fine-tunes channel-level strategies and Unified Reporting empowers data-driven decisions. Whether a small or large business, Lifesight's measurement stack offers a solution to enhance marketing effectiveness. To elevate your marketing efforts, <a href="https://www.lifesight.io/demo">request a demo</a> today.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image1_8501e87f98.webp) 
<p><em>Lifesight's Dashboard shows the different metrics that a business tracks and how they are performing across all paid channels.</em></p>
<h2>Summing it all up</h2>
<p>Marketing measurement is going through an evolution that is leading to marketers embracing advanced attribution models, ensuring data governance, and gradually scaling measurement strategies to optimize ROI tracking. As third-party cookies continue to decline, preparing for a cookieless future through multipliers and advanced measurement techniques will be essential.</p>
<p>The future of marketing measurements is dynamic and ever-changing. Staying informed, adapting to new technologies, and maintaining data quality will be the pillars of successful omnichannel ROI measurement in the years to come.</p>
<p><em>Want to know more? </em><a href="https://www.lifesight.io/demo"><em>&nbsp;Schedule a demo with our experts now!</em></a></p>


Omnichannel ROI Measurement_ Adapting to a Cookieless Future.png

Discover expert insights on optimizing ROI in the Omnichannel landscape and preparing for a cookieless future. This blog presents six actionable takeaways from discussions with top industry leaders.

Experts Discussing Omnichannel ROI Measurement & Cookieless Future

6 Takeaways from Three Experts Discussing Omnichannel ROI Measurement and Adapting to a Cookieless Future

<p>According to <a href="https://www.gartner.com/en/marketing/topics/marketing-budget?utm_source=press-release&amp;utm_medium=press-release&amp;utm_campaign=GML_GB_2023_GML_NPP_PR1_CMOSPENDSURVEY2022&amp;utm_term=ebook">Gartner</a>, digital platforms accounted for 56% of total marketing channel spending in 2022. Platforms like Google Ads, LinkedIn, and Facebook play an integral role in modern marketing.&nbsp;</p>
<p>Quarterly or yearly marketing budgets are set based on the effectiveness of multi-channel digital marketing efforts. For this, CMOs perform in-depth analysis of diverse digital channels to develop strategies that reduce spend and maximize ROI.&nbsp;</p>
<p>To perform such analysis, they need authentic data points. However, there is widespread awareness about the ethical implications of cross-app user data sharing. Brands like <a href="https://www.apple.com/legal/privacy/en-ww/#:~:text=Apple%20may%20share%20personal%20data,for%20their%20own%20marketing%20purposes.">Apple </a>and <a href="https://policies.google.com/privacy?hl=en-US">Google</a> have even introduced more privacy-friendly data frameworks to limit the sharing of personal data.&nbsp;</p>
<p>As a result, Marketing Mix Modeling (MMM), a famous concept introduced in the 1950s, is gradually making a comeback in an increasingly cookieless world.</p>
<p>MM Modeling is a smarter way to analyze and optimize multi-channel marketing efforts. Unlike a marketing attribution tool, Marketing Mix Modeling doesn&rsquo;t focus on user-level data. It bases its analysis on aggregate datasets such as ad spends, social outreach, sales, impressions, and more.&nbsp;</p>
<h2>Components of a Marketing Mix Model&nbsp;</h2>
<p>The foundation of any <a href="https://www.lifesight.io/blog/marketing-mix-modeling-importance">Marketing Mix Model</a> is its four components - Product, Price, Place, and Promotion.&nbsp;</p>
<h3>Product&nbsp;</h3>
<p>The first &ldquo;P&rdquo; in the Marketing Mix stands for Product.&nbsp;</p>
<p>Product (or service) refers to the object that a brand sells to customers. Knowledge of the product and its features is not enough for marketers. You must also understand the needs of your target audience and how the product benefits your customers in different stages of the buyer&rsquo;s journey.&nbsp;</p>
<p>Apart from this, you must know the following:&nbsp;</p>
<ul>
<li>The pain points and challenges your product solves&nbsp;</li>
<li>How your product compares to that of your competitors and where it stands out</li>
</ul>
<p>Knowing all these aspects well can make your advertising campaigns more targeted and value-driven.&nbsp;</p>
<p><strong>Role in Marketing Mix Modeling</strong></p>
<p>As a part of MM Modeling, you need to dive deeper to track various product metrics like sales volume, customer satisfaction rate, and customer retention rate. All these are dependent variables that control the performance of your Marketing Mix Model.&nbsp;</p>
<p>Let&rsquo;s take the example of the global ecommerce giant, Amazon, and how its growth was propelled by adding different products to its Marketing Mix Model.&nbsp;</p>
<p>Amazon began its journey as an online retailer in the late 1990s. Back then, the only product it was selling was books. It was a smart choice, considering books were one of the few products with universal demand. So much so that in 1997 alone, Amazon earned <a href="https://www.latimes.com/business/la-fi-amazon-history-20170618-htmlstory.html#:~:text=Not%20beholden%20to%20the%20physical,in%20more%20than%20150%20countries.">$148 million</a> in sales by selling only books.&nbsp;</p>
<p>The success of this venture made its founder, Jeff Bezos, realize that global shoppers were fond of buying products online and were ready to pay for the convenience of getting their desired products delivered to their doorsteps. As a result, Amazon started adding new products (like music, computer games, clothes, and so on) to its Marketing Mix every year while keeping track of product metrics like sales volume and market demand. Soon, Amazon became the online &ldquo;Everything Store&rdquo; Jeff Bezos had always dreamed of.&nbsp;</p>
<h3>Price&nbsp;</h3>
<p>Price is the second &ldquo;P&rdquo; in Marketing Mix Modeling.&nbsp;</p>
<p>It represents how much a brand charges for a product (or service) from their customers. Although marketers do not build pricing strategies, any change in pricing directly impacts the performance of marketing campaigns.&nbsp;</p>
<p>Therefore, they should know the following considerations behind setting up a marketing strategy:&nbsp;</p>
<ul>
<li>Make sure you know the brand&rsquo;s perspective on choosing a particular pricing strategy. If your company sells a luxury product with a high manufacturing cost, you may be allowed to reduce its price or offer aggressive discounts on the item to boost campaign conversion.</li>
</ul>
<ul>
<li>A pricing strategy also helps with lead conversion. If your product has a freemium model, the possibilities of conversion increase. Similarly, conversion possibilities may be reduced if your brand doesn&rsquo;t have a free trial option and prospects are forced to buy a specific plan to explore the product.&nbsp;</li>
</ul>
<p><strong>Role in Marketing Mix Modeling</strong></p>
<p>Marketers must be aware of factors like price changes, promotional pricing, and competitor pricing to understand how pricing impacts ROI.&nbsp;</p>
<p>For example, all top OTT platforms like Netflix, Hulu, and Amazon Prime offer subscription-based pricing to their users. These brands have several tiered plans targeted at different user groups.</p>
<p>Even though there are differences in their exact pricing, it is clear that all these brands use a competitive pricing strategy as part of their Marketing Mix Models. Their ROI depends on how many monthly/yearly subscriptions they acquire; these factors control their MM Models.</p>
<h3>Place</h3>
<p>The third &ldquo;P&rdquo; in a Marketing Mix Model stands for Place.&nbsp;</p>
<p>Here, &ldquo;place&rdquo; signifies where your products are available for the customers to explore and purchase. Earlier, &ldquo;place&rdquo; represented brick-and-mortar stores only, things have changed now. Today, a brand sells products in multiple places like offline and online stores, social media platforms, and more.&nbsp;</p>
<p>You may or may not be responsible for deciding where your brand&rsquo;s products will be available. However, it is still essential to find answers to the following questions:&nbsp;</p>
<ul>
<li>Which geographies should my product be available in?</li>
<li>Which distribution channels are being used to sell my product?&nbsp;</li>
<li>Which channel is more profitable? Digital or offline?&nbsp;</li>
<li>Which channels are most effective for my target audiences?&nbsp;</li>
</ul>
<p><strong>Role in Marketing Mix Modeling&nbsp;</strong></p>
<p>Different places or distribution channels have different potential. For example, if a new brand decides to sell its products through top e-retailers like Amazon, it is easier to boost its reach as compared to selling the products through its ecommerce website. The reason is that Amazon has a <a href="https://www.statista.com/topics/846/amazon/#topicOverview">514 billion</a> user base in the US alone as of 2022, while a new ecommerce website will need time and dedicated search engine optimization (SEO) efforts to boost its reach.</p>
<p>Therefore, CMOs must integrate these factors to develop a Mix Model that provides detailed insights into the ROI potential of different &ldquo;places&rdquo; or distribution channels.</p>
<h3>Promotion</h3>
<p>The fourth and final &ldquo;P&rdquo; in an MM Model stands for promotion. Promotion is directly associated with a brand's marketing function because it represents how the brand communicates the value of its product to existing and potential customers.</p>
<p>Promotion includes multiple digital channels like social platforms, SEO, digital ads, emails, PR campaigns, and offline channels like radio ads, television ads, newspaper ads, billboard ads, and more.</p>
<p>Here are aspects you should be aware of:</p>
<ul>
<li>What type of content/creative works for which channel?&nbsp;</li>
<li>Which message is driving the highest number of clicks and impressions?&nbsp;</li>
<li>What does the customer journey for your brand look like?&nbsp;</li>
<li>Which types of campaigns work for which groups of customers?&nbsp;</li>
</ul>
<p><strong>Role in Marketing Mix Modeling</strong></p>
<p>Each promotional activity performed by a brand should be evaluated based on factors like sales, impressions, clicks, ROI, and so on. All these metrics determine the success rate of different campaigns and simplify the channel-based budget allocation for the upcoming financial year.&nbsp;</p>
<p>Returning to Netflix, this brand had a marketing budget of<a href="https://www.nytimes.com/2023/04/03/business/media/netflix-marketing.html"> $2.5 billion in 2022</a>. Nevertheless, the global streaming marketplace is getting hugely competitive, and Netflix has a limited marketing budget, considering it spends $17 billion a year on programming.&nbsp;</p>
<p>Recently, <a href="https://www.linkedin.com/in/marian-lee-bb27843/">Marian Lee, the marketing chief of Netflix</a>, said that spending on ads no longer results in more viewers. As a result, the brand is shifting its strategy and focusing more on establishing itself as a content organization.&nbsp;</p>
<p><em>&ldquo;I&rsquo;m trying to enable creativity to bring this content to more people worldwide. I also want the rest of Netflix to understand the marketing strategy: We support the content organization,&rdquo;</em> says</p>
<p>&nbsp;- <strong>Marian Lee</strong>.</p>
<p>This explains how Netflix is ready to shift its promotional strategy from paid advertising to content-based marketing by tracking one of its significant KPIs - an increase in viewers. This shift has been possible due to this OTT brand&rsquo;s careful monitoring of its MMM and making informed decisions based on the outcomes.&nbsp;</p>
<h2>Why is Marketing Mix Modeling Important?</h2>
<p>Marketing Mix Modeling provides brands with a holistic overview of their marketing campaigns.</p>
<p>For example, you might have taken all the right strategies for your brand&mdash;a power-packed website, comprehensive content, and creative ad campaigns. But that&rsquo;s not where it ends. In fact, it is just the foundation you built. And once the foundation is ready, you need a roadmap to keep going.</p>
<p>Marketing Mix Models provide you with that exact roadmap. These models analyze sales data alongside different marketing initiatives to identify their effectiveness and help a marketer plan the next steps.&nbsp;</p>
<p>Here&rsquo;s why MM Modeling is important:&nbsp;</p>
<ul>
<li>MMM evaluates the performance of multi-channel campaigns to showcase which channels are most profitable and contribute to maximum ROI and which are least profitable and can be discontinued.&nbsp;</li>
</ul>
<ul>
<li>Once you evaluate various marketing campaigns and list the insights, MM Modeling helps you deploy those insights to streamline your marketing efforts.&nbsp;</li>
</ul>
<ul>
<li>Marketing Mix Models forecast sales based on historical data and identify the potential negative factors that affect a brand&rsquo;s performance in the long run.</li>
</ul>
<ul>
<li>MM Model is relevant in a world with privacy-focused customers and policies. For example, the General Data Protection Regulation (GDPR) mandates a &ldquo;cookie-less&rdquo; business landscape, making it hard for marketers to use cross-app user data.&nbsp;</li>
</ul>
<p>Marketing Mix Modeling analyzes <a href="https://www.lifesight.io/resources/checklist-data-sources-for-marketing-mix-modeling">aggregate datasets</a> to conduct in-depth analysis and doesn&rsquo;t consider users&rsquo; personal data.&nbsp;</p>
<h2>Traditional vs. Digital Marketing - What are the Differences?&nbsp;</h2>
<p>Traditional marketing refers to conventional marketing techniques like television, radio, print, and billboards before digital channels existed. These marketing channels used to be popular until the early 1990s.&nbsp;</p>
<p>However, digital marketing has been ruling the world since the early 2000s, and some of the essential digital channels include social media platforms, search engines, and online communities.&nbsp;</p>
<p>Marketing Mix Models have also evolved over the years and can analyze the performance of a brand&rsquo;s traditional and digital marketing initiatives.&nbsp;</p>
<p>You must be aware of the key differences between traditional and digital marketing to leverage the MMM framework more effectively:</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Screenshot_2023_09_26_at_5_51_19_PM_1c21df56a5.png) 
<p>While it appears that MTA offers more advantages for marketers because you can track every click, every ad, and every channel. MTA is falling short owing to signal loss caused by cookie and privacy first compliances. Furthermore, as your marketing budget grows and you have several channels, cross-channel macro optimizations such as budget reallocation or incremental lifts provided by one channel vs. the others become crucial, which MMM solves.</p>
<h2>How does Marketing Mix Modeling Work?</h2>
<p>An MM Model uses statistical methods like multi-linear regression to analyze the relationships between dependent and independent variables and how they impact a brand&rsquo;s overall sales.&nbsp;</p>
<p>Here is a step-by-step breakdown of how a Marketing Mix Model functions:&nbsp;</p>
<h3>Step 1: Data Collection</h3>
<p>Marketers start with identifying data repositories and data points specific to the business goals and integrate these sources with the MM Model to expedite the data collection process.&nbsp;</p>
<p>Contrary to <a href="https://www.lifesight.io/blog/marketing-mix-modeling-misconceptions">Marketing Mix Modeling myths</a>, data quality matters in this framework. Hence, focusing on granularity is important at this stage.&nbsp;</p>
<p>For example, if you are measuring the performance of an email campaign, MM Modeling will focus on metrics like open rate, unsubscribe rate, CTR, and more. Nevertheless, the outcome depends on how granular these datasets are.&nbsp;</p>
<p>Therefore, you should partner with reliable email marketing service providers that offer authentic, correct data to feed your MMM model for appropriate outcomes.&nbsp;</p>
<p>Some of the most common data types that work as inputs for an MM model include:&nbsp;</p>
<ul>
<li>Weekly, monthly, or quarterly sales data&nbsp;</li>
<li>Cross-channel marketing expenditure vs. cross-channel sales&nbsp;</li>
<li>Various external factors like competitors&rsquo; activities, latest trends, seasonality, distribution&nbsp;</li>
<li>Internal activities like IPO launch, funding rounds, and price changes</li>
</ul>
<h3>Step 2: Models and Data Analysis&nbsp;</h3>
<p>Next, it is time to develop the Marketing Mix Model. Based on the analysis of the datasets, brands can develop different types of statistical models. Some popular statistical techniques associated with these models include time series, linear regression, and multi-linear regression.&nbsp;</p>
<p>This model estimates factors like sales data and internal and external factors (mentioned previously) to identify the relationship between sales and marketing initiatives.&nbsp;</p>
<p>In the analysis stage, an MM model opts for coefficient analysis to determine which of the variables (internal and/or external) has the most substantial impact on the brand's overall sales. It further identifies the optimal level of implementation for each variable to maximize overall sales.</p>

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<h3>Step 3: Generating Insights&nbsp;</h3>
<p>By the end of the analysis stage, businesses should be able to derive critical insights from the Marketing Mix Model.&nbsp;</p>
<p>In this stage, marketers can find answers to the following questions:&nbsp;</p>
<ul>
<li>Which elements affect sales directly?&nbsp;</li>
<li>What is the cost vs. ROI scenario for each marketing channel?&nbsp;</li>
<li>What percentage of sales lift can be expected if you increase the budget of a certain channel by XX%?&nbsp;</li>
<li>What is the saturation point of a marketing channel?&nbsp;</li>
<li>What will be the expected sales for the upcoming quarter?&nbsp;</li>
</ul>
<p>The Marketing Mix Model helps marketers with intuitive visualizations to decode the relationship between variables and sales in a more straightforward manner. CMOs use these visualizations to communicate the performance of marketing initiatives with other stakeholders.&nbsp;&nbsp;</p>

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<p><a href="https://www.lifesight.io/marketing-mix-modelling">Source</a></p>
<h3>Step 4: Iteration and Validation&nbsp;</h3>
<p>The final stage is to iterate your Marketing Mix Modeling based on the insights derived.&nbsp;</p>
<p>A critical part of this stage is to conduct a &ldquo;what if&rdquo; analysis for your brand. The outputs of an MM model look like equations that represent relationships between various variables.&nbsp;</p>
<p>As part of the &ldquo;what if&rdquo; analysis, marketers can change the inputs for different variables to identify the optimal point that provides maximum ROI with minimum cost.&nbsp;</p>
<p>Suppose your <a href="https://www.lifesight.io/marketing-mix-modelling"><strong>Marketing Mix Model</strong></a> shows that LinkedIn is the most profitable paid channel for your brand. By investing 10% of your marketing budget, you are currently generating 35% of the overall ROI from this channel.&nbsp;</p>
<p>In the iteration and validation stage, you can change the percentage of the overall budget and keep increasing it to identify how your ROI will also change.&nbsp;</p>
<p>While changing inputs, there will come a point where, despite increasing the budget, the ROI will become static. The takeaway here will be that even though LinkedIn is a profitable channel for your brand, beyond a certain point, it will not be effective, even though you keep increasing the budget.&nbsp;</p>
<p>Hence, you can plan the marketing mix accordingly and start exploring other channels to strike a balance.&nbsp;</p>
<h2>Final Thoughts&nbsp;</h2>
<p>Marketing Mix Modeling is a great way to analyze the effectiveness of your marketing initiatives. Nevertheless, it is a costly framework and only Fortune 500 companies with budgets over $500K can afford this model. Additionally, an MM Model takes close to 6 months to analyze large data volumes and produce results.&nbsp;</p>
<p>Does that mean MM Models are out of reach for mid-level organizations? Not really, as there is an alternative called an automated <strong>Marketing Mix Model </strong>developed by<a href="https://www.lifesight.io/marketing-mix-modelling"> Lifesight</a>.&nbsp;</p>
<p>Lifesight optimizes your existing marketing mix with a processing time of just 18 months. Additionally, this tool can:&nbsp;</p>
<ul>
<li>Train your Marketing Mix Models within 45 minutes and test different budget scenarios to optimize the budget allocation process</li>
<li>Help marketers allocate appropriate budgets to the appropriate marketing channels to avoid overspending&nbsp;</li>
<li>Auto-calibrate your Marketing Mix Model to scale operations.</li>
</ul>
<p><em>Want to know more? </em><a href="https://www.lifesight.io/demo"><em>&nbsp;Schedule a demo now!</em></a></p>

An Introduction to the Principles of Marketing Mix Modeling.png

Embark on your journey to understand Marketing Mix Modeling (MMM) with this comprehensive introduction. Explore the fundamental principles that underpin MMM and how it can revolutionize your marketing strategies.

An Introduction to the Principles of Marketing Mix Modeling

<p>According to a study by IBM, poor data can cost businesses <a href="https://www.datamaticsbpm.com/blog/the-cost-of-bad-data-and-its-impact-on-your-b2b-sales-marketing/#:~:text=According%20to%20a%20study%20by,lost%20productivity%20and%20missed%20opportunities.">$3.1 trillion</a> annually in the U.S alone. When it comes to marketing mix modeling (MMM), which relies heavily on accurate and comprehensive data, these imperfections can make or break a campaign's success.</p>
<p><a href="https://www.lifesight.io/blog/marketing-mix-modeling-importance">Marketing Mix Modeling</a> analyzes cross-channel marketing datasets through statistical methods like multi-linear regression to forecast incremental sales and ROI.</p>
<p>The level of accuracy of an MMM model&rsquo;s prediction depends on the authenticity, granularity, and accuracy of the data fed to this model. However, challenges like incomplete and multi-collinear datasets affect the final output of the model.</p>
<p>This article will help you identify and address the most common data challenges associated with MMM Models.</p>
<h2>The Importance of Quality Data for Marketing Mix Modeling</h2>
<p>Data quality plays a critical role in Marketing Mix Modeling. The output accuracy depends on the granularity, quality, and relevance of the input datasets. Irrelevant and inaccurate data increases the processing time of the Marketing Mix Model, and there&rsquo;s no guarantee that the final output will be authentic.</p>
<p>Suppose you are trying to estimate the effectiveness of LinkedIn as a marketing channel for your brand&rsquo;s US-based operations on a month-on-month basis. To get complete visibility, you need to feed the Marketing Mix Model with the following inputs:</p>
<ul>
<li>Overall sales data for the past few months&nbsp;</li>
<li>Overall spending on US-based ad campaigns for the past few months&nbsp;</li>
<li>Any non-marketing or external factors that might have impacted the business&nbsp;</li>
<li>Organic Marketing Data</li>
<li>Seasonal sales data&nbsp;</li>
<li>List of Holidays</li>
<li>Competitive data (if applicable)</li>
</ul>
<p>If one or more of the above datasets is missing, the output will be vague, and the predictions will be inaccurate.</p>
<p>The same pattern was observed in <a href="https://www.nielsen.com/wp-content/uploads/sites/2/2022/09/Marketing-Mix-Modeling-Best-Practices-EN.pdf">this study</a>, where the output's accuracy level significantly shifted as the level of granularity was changed for the input data. This study was based on 19 MMM studies, and the Marketing Mix Models with regional-level data provided more accurate outcomes than the MMM with state-level data.</p>
<h2>Types of Data Used in Marketing Mix Modeling</h2>
<p>The data types in MMM can be segmented into three groups: KPI or metrics-based data, marketing data, and non-marketing or external data.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image1_0ea3f2bf03.webp) 
<h2>Common Data Challenges in Marketing Mix Modeling</h2>
<p>Data is the cornerstone of any Marketing Mix Model. However, marketing professionals often face some critical data challenges that impact the business's overall performance. Below are some of the standard data pitfalls associated with Marketing Mix Models:&nbsp;</p>
<h3>Incomplete data</h3>
<p>When businesses adopt Marketing Mix Modeling, they often realize they don&rsquo;t have access to appropriate datasets to measure the effectiveness of cross-channel marketing initiatives.&nbsp;</p>
<p>For example, an ecommerce organization may have been using only country-level data to identify the performance of marketing campaigns for one of its subsidiaries. However, once it applies the Marketing Mix Model, it will realize that the data is incomplete. It needs more distinct data, like regional or city-based breakdowns of sales performance.&nbsp;</p>
<p>While building frameworks to collect such granular data is time-consuming and may take months, MM Models need complete or sufficient data to draw accurate conclusions.</p>
<p><strong>Solutions for incomplete data</strong></p>
<p>Incomplete data-related challenges in MMM can be addressed with techniques like data imputation, forecasting, and deletion.&nbsp;</p>
<ul>
<li><strong><strong>Data imputation - </strong></strong>The data imputation technique replaces missing data with estimated values. Marketers often use mean, median, and mode to do that. For example, if the sales data for July is missing, you can take the mean value of the sales data for June and August to add an estimated value.&nbsp;</li>
</ul>
<ul>
<li><strong><strong>Forecasting - </strong></strong>Time-series forecasting is also used to reverse-forecast the missing data. You can also use the moving average method to recover lost data.&nbsp;</li>
</ul>
<ul>
<li><strong><strong>Deletion - </strong></strong>Sometimes, it is best to delete missing data. For example, in a survey or poll, adding missing data based on assumptions is not ethical. In those cases, deleting the respective fields is better.&nbsp;</li>
</ul>
<h3>Lack of measurement standards&nbsp;</h3>
<p>Not having a universal measurement standard often becomes a roadblock to implementing Marketing Mix Models. The effects of a marketing campaign can be short-term, long-term, and very long-term.&nbsp;</p>
<p>MM Models often <a href="https://www.lifesight.io/measure">measure</a> the short-term impacts of these campaigns on overall sales. However, due to missing data and limited measurement techniques, estimating factors like repeat purchases that generate long-term impacts on overall sales is challenging.&nbsp;</p>
<p>Most marketing professionals use return on marketing investment (ROMI) and effectiveness techniques to measure the ROI associated with a marketing campaign.</p>
<p><strong>ROMI - Incremental sales on the campaign/ Dollars spent on the campaign.</strong></p>
<p><strong>Effectiveness - Incremental sales from campaign/ Impressions.</strong></p>
<p>Both of these techniques depend on &ldquo;incremental sales from campaigns&rdquo;. This factor varies a lot for each campaign as the concept of &ldquo;lead&rdquo; changes from campaign to campaign. Hence, these techniques only focus on the short-term effects of an initiative on overall sales.&nbsp;</p>
<p><strong>Solutions for lack of measurement standards&nbsp;</strong></p>
<p>A great way to address this challenge is by following the unobserved component modeling technique.&nbsp;</p>
<p>This technique performs a time series decomposition into variables like sales, trends, and seasonality to predict the effectiveness of a campaign. This analysis allows marketers to assess the impact of their advertising over a longer period of time.</p>
<p>Note that the margins of error for this type of analysis become more extensive as the period considered for this model becomes longer. This model isn&rsquo;t perfect for assessing the effectiveness of advertising in the long term. Nevertheless, it takes an aggregated view across more than 500 marketing campaigns, and this analysis helps marketers get closer to understanding how their ad campaigns impact profitability over the long run.</p>
<h3>Difficulty in measuring the performance of ad content&nbsp;</h3>
<p>Both messaging and creatives play a significant role in the success of ad campaigns. To date, we don&rsquo;t have any absolute measurement techniques to identify the impact of content and creatives on the effectiveness of ad campaigns.&nbsp;</p>
<p>While MMM uses standard ad metrics like impressions and clicks to measure the effectiveness of ad creatives, it cannot differentiate between two or more creatives with the same number of impressions and clicks.&nbsp;</p>
<p><strong>Solutions for difficulty in measuring the performance of ad content</strong></p>
<p>A good way to handle this challenge is through surveys. You can create questionnaires with questions focused on the messaging and creatives of an ad campaign. Adding Likert scale questions with a five-point scale simplifies the data collection and analysis processes. This is time-consuming, but it provides you with distinct data related to the ad campaign performance.&nbsp;</p>
<p>You can distribute such surveys in various communities, focus groups, and through emails and use the collected data as additional inputs in your MMM to generate more precise outcomes.&nbsp;</p>
<h3>Multi-Collinearity&nbsp;</h3>
<p>For large enterprises, multiple marketing efforts take place simultaneously. As a result, the independent variables become collinear and are strongly associated with one another. A classic example of multi-collinearity occurs during peak seasons when e-commerce stores promote the same coupon code through social media ads, billboard ads, and email campaigns.&nbsp;</p>
<p>As a result, it becomes difficult to track which of these campaigns influenced overall ROI the most. When variables are highly correlated, the outcome presented by Marketing Mix Modeling showcases the joint effect of these variables.</p>
<p><strong>Solutions for multi-collinearity&nbsp;</strong></p>
<p>To avoid multi-collinearity, marketers can implement the following steps:&nbsp;</p>
<ul>
<li>Delete the common data predictors that are correlated, like coupons and discount data&nbsp;</li>
<li>Use the principal component analysis technique to convert the collinear variables into composite indexes&nbsp;</li>
<li>Incorporate other estimation methods like ridge regression that handle critical multi-collinearity use cases&nbsp;</li>
</ul>
<p><a href="https://www.linkedin.com/posts/ridhima-kumar7_marketingmixmodeling-marketingeffectiveness-activity-7023899326837858304-RtO3/?originalSubdomain=lt">In this post</a>, an MMM expert, Ridhima Kumar, writes about how fixing multicollinearity leads to another problem called endogeneity.&nbsp;</p>

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<h2>Advanced Data Solutions</h2>
<p>Two advanced data solutions, namely machine learning algorithms (MLA) and time series analysis, simplify the deployment of Marketing Mix Modeling by addressing common data challenges.&nbsp;</p>
<h3>Machine Learning Algorithms&nbsp;</h3>
<p>MLA automates time-consuming data processing tasks like cleaning, formatting, and integrating data from various sources. Advanced algorithms analyze large volumes of data within seconds and reduce errors by removing incomplete and inconsistent datasets.&nbsp;</p>
<p>Automated MMM solutions like <a href="https://www.lifesight.io/marketing-mix-modelling">Lifesight</a> include a built-in calibration tool to generate MM Model data feeds using existing data sources. This tool uses lift values to auto-calibrate the Marketing Mix Models and retrains the model with just a single click. With automation in the picture, the scope of using erroneous data is much less than that of manually driven MM Models.</p>

 ![](https://mighty-basketball-fd4aeed960.media.strapiapp.com/image3_0b9dcd0bc1.webp) 
<p><strong><em>You can explore Lifesight&rsquo;s MMM solution by </em></strong><a href="https://www.lifesight.io/demo"><strong><em>Booking a demo</em></strong></a><strong><em>.</em></strong></p>
<h3>Time Series Analysis&nbsp;</h3>
<p>Time series analysis is another technique to address the common data challenges related to MMM. A critical step in the process is to assess the <a href="https://www.lifesight.io/resources/checklist-data-sources-for-marketing-mix-modeling">data sources for <strong>Marketing Mix Modeling</strong></a> at fixed weekly, monthly, or quarterly intervals. Due to routine assessment, the datasets get updated regularly, reducing the possibility of using poor-quality data.&nbsp;</p>
<p>A good practice is to convert time series data into cross-sectional form before building the MM Model. In this way, marketers can adjust the data's seasonality factors and correlated variables.</p>
<h2>Conclusion&nbsp;</h2>
<p>Data challenges in Marketing Mix Models are common. However, with automation gaining widespread acceptance in the marketing world, it has become easier for marketers to identify and take action against inaccurate datasets within an MMM Model. Overcoming the data challenges becomes easier by focusing on data quality, granularity, consistency, and accurate measurement methods. Going forward, operationalizing MMM within your brand will be an important modern marketing uplift - <a href="https://www.lifesight.io/demo">Speak with our experts now!</a></p>

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