{"id":5396,"date":"2025-12-07T12:11:31","date_gmt":"2025-12-07T11:11:31","guid":{"rendered":"https:\/\/www.htt.it\/?p=5396"},"modified":"2025-12-08T10:42:36","modified_gmt":"2025-12-08T09:42:36","slug":"marketing-mix-modeling-mmm-budget-allocation","status":"publish","type":"post","link":"https:\/\/www.htt.it\/en\/marketing-mix-modeling-mmm-budget-allocation\/","title":{"rendered":"Marketing Mix Modeling (MMM)"},"content":{"rendered":"\n\n\n
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Marketing Mix Modeling (MMM): how to allocate budget across channels in a scientific way<\/h2>\n

You\u2019re investing \u20ac50,000 or more per month in marketing across Google Ads, Meta, email, influencers, SEO and offline channels such as TV, out-of-home or print.
\nThe question is simple and uncomfortable: is this allocation truly optimal, or are you just repeating last year\u2019s plan with a few \u201cgut-feel\u201d adjustments?<\/p>\n<\/header>\n

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In a nutshell<\/h2>\n

Marketing Mix Modeling (MMM) is a statistical model that links spend across different channels, online and offline, to business outcomes: sales, leads, revenue and margins. It becomes really useful when the marketing budget exceeds \u20ac50,000 per month, the company is active on multiple channels (digital and traditional) and there is at least one year of historical data available. It does not replace digital attribution models, it completes them, because it allows you to include TV, connected TV, out-of-home, print, radio, PR, events and in-store activities in the picture. With tools like Robyn and Meridian you can build robust models, simulate multi-channel budget scenarios and understand not so much \u201cwhich channel is the best\u201d, but how to reallocate budget based on marginal ROI to protect investment and improve overall results.<\/p>\n<\/section>\n

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The budget allocation problem everyone has<\/h2>\n

The problem is not theoretical: every month tens of thousands of euros are distributed across digital and offline channels. Often, however, the criteria are poorly structured.<\/p>\n

In practice, the marketing budget is defined starting from historical spend, tweaking the channels that seem to perform better based on gut feeling<\/em> and taking into account internal pressures such as the preferences of the CEO or country manager.
\nThe result is a mix driven more by inertia, perceptions and internal politics than by quantitative analysis.<\/p>\n

Several industry studies show that a significant share of marketing budget is wasted due to inefficient allocation: too much is invested in channels that are already over-saturated and too little in undervalued channels, including offline media.
\nFor annual budgets of \u20ac500,000 and above, this can mean hundreds of thousands of euros potentially misallocated.<\/p>\n

Marketing Mix Modeling<\/strong> was created to answer very concrete questions: where are we over-investing, where are we under-investing and what is the optimal mix between digital and offline channels to maximise return in the medium term?<\/p>\n<\/section>\n

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What is Marketing Mix Modeling (the pragmatic version)<\/h2>\n

Marketing Mix Modeling (MMM)<\/strong>, or media mix modeling, is a statistical model that explicitly connects marketing spend by channel to business outcomes. The question it tries to answer is simple:<\/p>\n

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If I increase investment in channel X by \u20ac1, how much do sales, qualified leads or margin increase in my business?<\/p>\n<\/blockquote>\n

To answer that question, MMM analyses historical spend and performance data over a time horizon typically between 12 and 24 months.
\nIt takes into account not only digital channels but also TV, connected TV, radio, out-of-home, print, catalogues, flyers, trade fairs and in-store activities<\/strong>.
\nIn parallel, it integrates contextual variables such as seasonality, promotions, price changes, competitor actions and macroeconomic factors.<\/p>\n

Unlike other approaches, MMM works on aggregated data and does not track individual users. It observes the system as a whole: how much we spent per channel over a given period, what results we generated and how external drivers behaved.<\/p>\n<\/section>\n

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How it works in practice<\/h2>\n

For each week or day you collect four families of information: how much you invested in the different digital channels, how much you invested in TV, CTV, print, radio, out-of-home and events, what results you generated in terms of sales,
\nleads, revenue and margins, and which external variables had an impact (seasonality, promos, competitors, product availability).<\/p>\n

An MMM model analyses thousands of these data points over time and estimates the incremental effect of individual channels, digital and offline.
\nThis is how insights like these emerge:<\/p>\n