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Marketing Mix Model: What are the benefits and how does it work?

Data Science

The importance of Marketing Mix Modeling in the new age of digital ad measurement

Marketing Mix Model (MMMs) is making a comeback in media measurement. One of the reasons for the resurgence of MMM is due to fundamental changes to digital advertising. For example, Apple’s new limits on what advertisers can track has made deterministic user-level measurement of digital advertising effects become more challenging. As user-level data dries up, companies that fail to adapt risk throwing darts in the dark. Marketing Mix Model has a specific advantage in this new landscape because they largely depend on aggregate data and do not require user-level data.

Source: Mediaocean. (June 1, 2022). Leading marketing technology innovations believed to be most impactful for advertising by marketers worldwide as of April 2022 [Graph]. In Statista.

Another factor driving the resurgence of MMM is the growing emphasis on marketing accountability and ROI. With rising interest rates and persistent high-levels of inflation, businesses are under increasing pressure to demonstrate the impact of their marketing spend. MMM can provide a quantitative framework for measuring marketing effectiveness and identifying areas for optimization.

How to do Marketing Mix Modelling effectively in this new digital ad measurement landscape?

Marketing Mix Models are not perfect. Companies must calibrate MMMs by conducting experiments. This will help sharpen their digital marketing approach and remove potential inaccuracies in the model. Therefore, making MMM part of a company’s marketing analytics toolkit is not as easy as turning the lights on. Without careful guidance, they can misinform a company’s marketing decisions.

Source: PR Newswire. (April 26, 2022). Leading methods used to measure advertising campaign outcomes according to consumer packaged goods (CPG) marketers in the United States as of the 4th quarter 2021 [Graph]. In Statista.

Today’s MMM practitioners use two other tools to help calibrate models: Incrementality experiments and Attribution. Market mix models are used to understand what really drives sales including other factors outside media. MMM is used to answer questions such as contribution of marketing to business success, ROI of overall marketing efforts, and how to optimizing marketing mix. Meanwhile, incrementality experiments are ad-hoc statistical tests to identify the causal impact of media. As can be inferred, this framework seeks to provide an explanation to the causal impact of media investment on revenue as well as check if a marketing campaign is really driving incremental conversions. Lastly, attribution models are used for more short-term business and bidding decisions. The focus is more on the ROI of digital marketing activities and placing a business value on each customer touchpoint.

Caption: Attribution: The exercise of allocating points or credit for conversions based on how consumers engage with various ads and ultimately become customers.

Caption: Incrementality: The change caused by exposure to an ad is measured by running randomized controlled tests.


How can Indaru help you reap the benefits of MMM

Indaru offers media consulting services, media audits, and other data analytics projects such as dashboarding. All these offerings are designed to further improve how Marketing Mix Modelling and data science can give your company the competitive advantage.

Want to learn more about Indaru? Check our linkedin page or contact us! Also check out our other article on Marketing Mix Model


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