Marketing Mix Modeling (MMM) in marketing is statistical analysis using multivariate regressions on conversions/sales with various marketing tactics and spend as variables to predict sales. The regression provides contributions of each variable which then is used to predict conversions and sales with different inputs or marketing mix. Specifically, Marketing mix modeling (MMM) (sometimes also called Media Mix Modeling) collects aggregated data across marketing and non-marketing factors over a multi-year historical period. Additionally, MMM models factor in external influencers such as seasonality, economic data, weather and promotions. That data is used to develop a demand model which quantifies the historical contribution of each marketing and non-marketing input to business outcome.

 

MMM provides a high-level, top down analysis of all marketing delivering strategic long-term planning insights into your non-addressable and addressable media.

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Marketing Mix Modeling Example
A clothing brand wants to know how each of the media channels contribute to sales. Over time, the brand has collected sales data and media spend for each channel given the same time frame. Based on many different points in time, the brand can run a multivariate test. The analysis shows for a change in media spend, what expected sales will be. Because it’s based on historical data, the brand is only getting correlation, and not necessarily causation. What are Limitations of Marketing Mix Modeling
MMM typically estimates marketing impact on historical business outcomes at the channel level probabilistically, and can be subject to the correlation vs. causation dilemma. For forward looking projections MMM relies on a number of assumptions for non-marketing factors as well the assumption that channel level media mix, cost, and response does not diverge with the historic data that is the basis for the demand model. While well built models based on high-quality data can overcome the correlation vs. causation dilemma to provide channel lift and forecasts, the limitation on degrees of freedom and challenges with overspecified models means that they cannot be used to inform tactical decision making at the sub-channel level. Because models rely on multiple years of historical data to determine an average read for marketing inputs, they are challenged in teasing out dynamic changes to marketing channels and/or business changes in recent periods. What are the Advantages of Marketing Mix Modeling?
The main advantage is that you can use 2 to 3 years of historical data that is most likely readily available if you’re an established brand. MMM also provides a high-level analysis across the entire media portfolio delivering strategic long-term planning insights into your non-addressable and addressable media. But again, you are only seeing correlations and not causation. The other advantage of MMM is its ability to model non-media exogenous variables such as macro-economy influences (like COVID-19), competitive influences, seasonality, promotions and other trends. An alternative, and in some cases better, approach to understand each marketing mix contribution is to run incrementality testing. Here are some pros and cons of Incrementality Testing vs MMM vs MTA With Measured, you can easily run incrementality measurement and testing on 70+ media publisher platform. Utilizing our API integrations with media publisher platforms, you get a cross-channel view of your marketing mix in less than 24 hours. Learn more and ask for a demo here.