{"id":15817,"date":"2025-05-13T14:57:15","date_gmt":"2025-05-13T12:57:15","guid":{"rendered":"https:\/\/sayagency.com\/magazine\/news\/marketing-mix-modeling-un-percorso-verso-la-consapevolezza\/"},"modified":"2025-09-05T14:46:08","modified_gmt":"2025-09-05T12:46:08","slug":"marketing-mix-modeling-un-percorso-verso-la-consapevolezza","status":"publish","type":"post","link":"https:\/\/sayagency.com\/en\/marketing-mix-modeling-a-journey-towards-awareness\/","title":{"rendered":"Marketing Mix Modeling: a Journey Towards Awareness"},"content":{"rendered":"\n

By Alberto Narenti<\/a><\/em><\/p>\n\n\n\n

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Marketing Mix Modeling (MMM)<\/strong> is a methodology created to quantify the contribution of each marketing channel to business objectives, such as sales, in-store traffic, and customer acquisition.
In the past, MMM was mainly used to analyze television and print advertising, but today it has become an indispensable tool for companies operating in a multi-channel ecosystem and in an increasingly privacy-first regulatory context.<\/p>\n\n\n\n

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In recent years, the return to MMM has been driven by three main factors:<\/p>\n\n\n\n

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The end of third-party cookies<\/strong> and restrictions on user data collection.

The growing complexity of the media mix<\/strong>, with the integration of many different digital channels, influencer marketing, and retail media.

The advancement of AI and Machine Learning<\/strong>, which have made MMM models more accurate and granular.<\/p>\n\n\n\n

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Why is it important to talk about it? Because every company doing digital marketing today should adopt this methodology in order to optimize its investments and improve its reporting capabilities.<\/p>\n\n\n\n

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What we will discuss in this article:<\/p>\n<\/div>\n <\/div>\n

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  1. The History of Marketing Mix Modeling<\/a><\/li>\n
  2. The CRISP-DM Process applied to MMM<\/a><\/li>\n
  3. MMM and Multi-Touch Attribution are complementary, not alternatives<\/a><\/li>\n
  4. The Fundamental Elements of MMM<\/a><\/li>\n
  5. Advanced Models: Robyn and Meridian<\/a><\/li>\n
  6. Marketing Mix Modeling Pills: rules to remember<\/a><\/li>\n<\/ol>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/section>\n\n\n

    The History of Marketing Mix Modeling<\/h2>\n\n\n\n

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    Marketing Mix Modeling<\/strong> was born in the 1960s<\/strong> as a tool to evaluate the effectiveness of advertising campaigns through the analysis of sales and other business data. Between the 1980s and 1990s, it became a standard in the world of fast-moving consumer goods (FMCG).<\/p>\n\n\n\n

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    In the 2000s, with the advent of digital, MMM lost centrality in favor of multi-touch attribution (MTA), which promised a more granular measurement of the user journey. However, with new privacy regulations and the increasing fragmentation of the media mix, MMM has regained relevance in recent years, adapting to new contexts thanks to the use of machine learning and causal analysis.<\/p>\n\n\n\n

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    The CRISP-DM Process applied to MMM<\/h2>\n\n\n\n

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    To structure a Marketing Mix Modeling project effectively, the CRISP-DM<\/strong> framework (Cross-Industry Standard Process for Data Mining)<\/strong> can be adopted, a consolidated method in the data analysis sector. This is a very important framework because without method there is a risk of making gross errors, or even worse, abandoning the path because one gets lost in the labyrinth of complexity.<\/p>\n\n\n\n

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    The 6 phases of the CRISP-DM process:<\/p>\n\n\n\n

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    1. Business Understanding<\/strong>\n