What is Marketing Mix Modeling?

Marketing mix modeling is a statistical method that businesses use to assess the influence of various marketing strategies and techniques on sales. This entails examining data from many sources, including sales, promotions, advertising, and pricing, in order to comprehend the impact of these factors on consumer behavior and make wise choices for next marketing initiatives. Finding the best marketing tactics, properly allocating resources, and maximizing return on marketing investment are the objectives of marketing mix modeling.

Do You Want A Copy of the 2023 Attribution Playbook?

Marketing Mix Modeling (MMM) is a powerful tool that helps businesses predict outcomes through statistical analysis and multivariate regressions. The regressions analyze the contribution of various marketing tactics and spends to conversions and sales, enabling companies to make informed decisions about their marketing mix.

MMM works by collecting aggregated data from multiple sources over a multi-year period, taking into account external factors such as seasonality, economic data, weather, and promotions. This data is then used to develop a demand model that quantifies the historical impact of each marketing input on business outcomes such as sales and conversions.

For established brands with a wealth of data, MMM can provide valuable insights into their entire media portfolio, making it ideal for long-term strategic planning. However, it has its limitations, particularly when it comes to making tactical or day-to-day decisions. MMM models are based on historical data and assumptions, so they may not accurately predict the impact of dynamic changes to marketing channels or business changes in recent periods.

MMM also relies on probability to estimate marketing impact on business outcomes, which can be subject to the correlation vs. causation dilemma. While well-built models can provide channel lift and forecasts, they are not designed to inform sub-channel-level tactical decision making and are challenged in identifying changes in recent periods.

Marketing Mix Modeling is a valuable tool for decision makers looking for high-level insights into their media portfolio. However, it should be used in conjunction with other data sources and techniques to make informed decisions about their marketing mix and drive better business outcomes.

Here’s what our CEO has to say about the differences between MMM & MTA on the Data Gurus Podcast

“Prior to bottom-up multi-touch attribution marketers only had top-down marketing mix models.

If you were a progressive company you would get a new MMM model done every year and that model would tell you to put ‘X’ percentage of your budget to digital, ‘X’ to TV, ‘X’ to direct mail and then it was up to each agency to determine how to allocate.

With MMM, there are no clear-cut goals in terms of a ‘feedback loop’ if any of the media was actually hitting your numbers until you did another model next year.

And next year’s model would tell you to adjust the channel allocations, but there’s no feedback to the actual on the ground people in terms of telling them what to do.

So marketing mix modeling tells you where to allocate those dollars while multi-touch attribution used to tell you how to allocate within that specific channel and cross-channel at a very very granular level, that was until all of the privacy changes, including IOS changes and the upcoming cookie apocalypse”

Marketing Mix Modeling (MMM) is a statistical technique used to measure the effectiveness and ROI of marketing activities by analyzing their impact on sales and revenue. It uses a combination of data from various sources such as sales data, marketing spend data, and consumer behavior data to model the relationship between marketing activities and business outcomes.

Marketing Mix Modeling provides businesses with insights into the effectiveness of their marketing activities, helping them make informed decisions about where to allocate their marketing budget. It allows companies to evaluate the impact of specific marketing initiatives and provides a clear understanding of the return on investment for each marketing activity.

The key components of a Marketing Mix Model include:

  • Sales data
  • Marketing spend data
  • Consumer behavior data
  • Market data such as competitor and market share information
  • Economic and demographic data

Marketing Mix Modeling differs from other marketing analytics techniques in that it focuses specifically on the relationship between marketing activities and sales, rather than just analyzing marketing data in isolation. MMM takes into account multiple factors and provides a comprehensive view of the impact of marketing activities on sales and revenue, allowing for more informed and effective marketing decisions.