Marketing mix modeling (MMM) quantifies the impact of various marketing inputs on sales and conversions based on the aggregated data. It pinpoints the effectiveness of each marketing channel in terms of return on investment (ROI) and helps marketers understand how to reallocate their budget. It is privacy-friendly and considers offline activities, including more traditional media like television advertising, direct mail campaigns, radio promotions, and out-of-home (OOH) advertising.
It also includes non-marketing effects such as seasonality, product innovations, competitors’ actions, price, placement, and broader macroeconomic conditions. Thus, MMM is a great option for marketers trying to find the path from marketing spend to sales and understand how different marketing channels can get you there.
Limitations of marketing mix modelling
MMM helps analyze both online and offline media and offers a high-level strategic view, but there are some drawbacks with the approach:
Requires lots of data: You need at least two years of historical data for reliable MMM results, which isn’t always feasible. With limited datasets, MMM might show statistically strong results that don’t align with real-world marketing performance.
Limited granularity: MMM looks at trends at a higher level, weekly or daily, so it’s not great for diving into individual campaigns or ad groups.
Contradicting results: Two equally good MM Models could have contradicting attribution results.