What is Marketing Mix Modeling & Why Should You Care?

Most mid-sized businesses use Multi-channel Attribution (MTA) to optimize cross-channel marketing initiatives. However, as companies scale to more than three channels and combine offline and online platforms, tracking the effectiveness of marketing efforts with MTA alone becomes challenging.

This is where the Marketing Mix Modeling (MMM) concept comes into play. The idea of MMM has been in existence since the 1960's. However, with time and the evolution of digital browsers and ad platforms, the MMM has become an aspiration for large enterprises.

There is a notion that only Fortune 500 companies can afford to deploy Marketing Mix Models as they cost over $500K and take as long as six months to show results. Nevertheless, companies like Lifesight introduced the concept of automated MMM a few years ago.

Automated Marketing Mix Models can shorten the processing time from six months to 10 minutes! While Marketing Attribution still helps, large enterprises in the scaling phase should use Marketing Mix Models as an ad to optimize their cross-channel strategy.

In this article, we will discuss what marketing mix modeling is and why it is becoming a growing need for bigger brands.

What is Marketing Mix Modeling?

The Marketing Mix, or the Four P's of marketing, is about having the right balance between your product, place, price, and promotion.

Balancing these factors can be difficult when you don't have reliable data to make an informed decision. That's why understanding the Marketing Mix Modeling definition is critical.

The MMM Model helps you answer some fundamental marketing questions:

• What percentage of ROI is the business generating through different marketing channels?
• Which marketing channels are the most expensive, and which channels are most cost-effective?
• Based on cost and ROI, which channels should I spend on in the next quarter?
• How are the earnings from advertising contributing to my overall business ROI?

Traditional vs. New Marketing Mix Modeling

While the core concept of MMM remains the same, the channels and sources of data collection have changed in the last decade.

Components and Elements of Marketing Mix Modeling 

MMM Models rely on gross sales, ad spend, product, and customer datasets to provide a holistic overview of marketing efforts. Let's deep-dive into each of them.

Marketing channels

• Traditional channels: Television, print media, billboards, magazines, and other non-digital channels.

• Digital channels: Social media platforms like LinkedIn, Facebook, Instagram, and YouTube; Communities like Reddit; search engines like Google; email campaigns and influencer marketing campaigns.

• Direct marketing and promotional channels: Customer loyalty programs, coupons, and point-of-sale promotions.

Temporal effects

Short-term temporal effects like time-sensitive discounts push customers to make purchases faster. On the contrary, prolonged campaigns often delay consumer's decision-making processes.

For example, when launching a new product, ecommerce stores often offer discounts like "Buy 1 Get 1 Free within 24 hours of purchase" or "50% off for repeat customers". Temporal effects like offers create urgency in the buyers' minds and can push them to make a purchase instantly.

External factors

• Economic indicators - GDP, inflation rate, and recession directly impact your potential customer's purchasing power.

• Seasonality: Seasonality is responsible for influencing customer behavior. Running offers and discounts during holiday seasons like Christmas, Halloween, Thanksgiving, and Easter often increase sales.

• Competitive actions: It is also important to note how aggressively your competitors promote their products or brands. If one of your competitors has a solid promotional strategy, it might lead to a temporary or long-term reduction in your sales.

• Event-driven factors: Unexpected events also play a massive role in MMM. In 2022, the Russia-Ukraine war caused a significant reduction in the ecommerce sales of both companies. Particularly in Ukraine, ecommerce sessions were reduced by a record 65%.

Baseline sales and incremental sales

• Baseline sales define the expected demand for a product in the absence of any marketing initiatives. It is directly associated with factors like brand equity and long-term trends in pricing seasonality.

• Incremental sales are the additional sales acquired by promotional activities like digital advertising, direct marketing campaigns, and traditional marketing initiatives. Total sales revenue is anything over and above baseline sales revenue.

Interactions and synergies

Every marketer knows that marketing activities are interrelated. The success or failure of one initiative can directly affect another's performance.

For example, successful online ecommerce brands create a perfect marketing synergy by combining SMS and email marketing. SMSes often have a higher open rate than emails (SMS - 98% vs. Emails - 20%).

Case in point: Whenever a new sale is live, brands notify the customers with an SMS and send specific coupon codes through emails to make it more effective.

Beauty brand Sephora often does this:

Source

Saturation curves

Marketers need to clearly understand the relationship between ad spending and performance and a Saturation Curve helps them understand the optimal level of ad spending to maximize ad performance.

A Marketing Mix Modeling software considers the saturation curves to identify this point and helps brands avoid spending additional budgets on the same campaigns.

A saturation curve looks like:

Source

It typically plots the order values of a product against marketing investments. After a specific time, it reaches a point where order value or sales remain the same despite increased ad spend. That's the point of saturation.

Cost data

Cost data is the overall investment information that businesses make on various marketing initiatives. To calculate profit, brands must subtract the cost data from sales revenue.

Response curves

A Response Curve dictates the relationship between various marketing initiatives and relevant business performance. Marketing teams use them to create media plans and plan campaign budgets more efficiently.

Source

In a response curve, you should plot sales against marketing efforts. If it's a linear response curve, it means that returns have been consistent for different marketing efforts. This shape varies from brand to brand.

Marketing Mix Modeling software aims to analyze response curves to identify the effectiveness of different marketing campaigns.

Model validation metrics

Once you develop a Marketing Mix Model, the next step is validating metrics such as R-squared and mean absolute percentage error to help you determine the accuracy of Marketing-Mix. Model validation compares the Mix Model with the real-life environment to measure its effectiveness.

Benefits of using Marketing Mix Modeling

MMM Modeling offers a refined multi-channel overview of different marketing initiatives to marketers. Some of the benefits of marketing mix modeling include:

1. Accurate forecasting

A Marketing Mix Model analyzes different marketing efforts from the core to identify which initiatives worked and which didn't, giving a clear picture of the marketing strategies to focus on in the next quarter.

The outputs acquired from a Marketing Mix Model allow you to explore the month-on-month impact of marketing campaigns on the overall growth of the business. In turn, it helps predict to what extent the marketing budget needs to be adjusted to reach the optimal point of maximum return at minimum ad spending.

After receiving the first version of the outputs, you can delve deeper and add several filters like locations, campaign types, and channels to create an actionable roadmap of future steps.

2. Earn trust and confidence from stakeholders

Marketing Mix Modeling attribution becomes a great way to win the trust of your C-suite executives and build marketing awareness across the organization.

Going a step further, granular MM reports provide a real-time overview of business segments and their contributions to overall ROI.

3. Avoid access to confidential data

Marketing Mix Modeling attribution doesn't use users' private data, which is why it is an ethical approach to privacy threats. It focuses on aggregate datasets like ad spending and sales to conduct an in-depth statistical analysis of the effectiveness of your marketing initiatives.

In a privacy-first era for the modern marketer, even though privacy regulations change frequently, MMM Models will continue to be the flexible approach that marketers can trust.

That being said, you should integrate Lifesight's AI-enabled, automated MMM solution that simplifies budgeting, forecasting, and campaign optimization to eliminate guesswork.

Some reasons why customers implement Lifesight's privacy-centric automated MMM solution are:

• Allocate budgets automatically to successful campaigns. LifeSight trains your MMM Model with historical data and tests different budget scenarios instantly.
• Scale your marketing spend without overspending, predict your KPIs, and allocate the budget to the right channels.

• The integrated marketing mix modeling platform allows for continuous calibration of your attribution models, leverages existing data integrations to generate MMM data feeds, and uses lift values from attribution and experiments to calibrate models.

Limitations and Challenges of Marketing Mix Modeling

a) Focus on short-term impact

Marketing Mix Modeling is the calculation of sales and ad spend data. Where this approach falls short is in providing in-depth details on the ability of various marketing channels to acquire new customers.

b) Requires a large amount of data

MMM Models require a large volume of data to provide accurate results. This can be a challenge for small marketing teams with limited data or for newer organizations that do not have data collection processes and systems in place.

c) Absence of measurement standards

Marketing Mix Models handle extensive marketing and sales data volumes. If your marketing team does not have measurement standards, they will not be able to understand how these models work and the accuracy of outcomes will be impacted.

d) Not sufficient insights on "why"

While MMM provides insights on the impact of different marketing initiatives, it doesn't explain the "why". In other words, you cannot rely completely on this approach to get granular insights into which channel generated the highest ROI.

However, Lifesight's automated MMM Model offers channel-level visibility into different campaigns and relative spending. Based on the results, it is easier to analyze why a certain campaign performed well for subsequent budget allocation.

Final Thoughts

In summary, marketing mix modeling algorithms are powerful for optimizing resources, making data-driven decisions for budget allocation, and predicting KPIs and future performance. Going forward, operationalizing MMM within your brand will be an important modern marketing skill - Book a demo with our experts now!