Published On: 22 Nov 2023
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.
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.
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.
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 MarTech Podcast.
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.
"Marketing mix modeling supplements decision-making by incorporating various data inputs, including spending, conversions, competitor activities, events, and organic data, to optimize budget allocation."
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.
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, "Marketing mix modeling measures the true influence of media channels, online and offline, beyond click-based or view-based attribution".
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.
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.
"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."
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.
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,"Traditional methodologies have evolved, with cloud-based AI capabilities enabling faster marketing mix model outputs for even non-technical or non-marketing analytics users."
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.
"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."
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.
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.
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.
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.
Sign up for a Lifesight demo and embark on your journey towards marketing excellence.
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