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Marketing Mix Modeling (MMM)

Method
Attribution

Definition

A top-down, statistical approach (econometrics). It doesn’t track individuals; it tracks aggregates.

Related Knowledge

Attribution ModelsMTA (Multi-Touch Attribution)

What MMM Is

Think of Marketing Mix Modeling (MMM) as a top‑down, macro view of your marketing performance.

Instead of tracking individual users like attribution models do, MMM looks at aggregated data over time:

  • Total spend per channel (TV, YouTube, Meta, Influencers, etc.)
  • Total outcomes (revenue, sign‑ups, leads, etc.)

    • How MMM Works

    MMM uses historical data to correlate spikes in revenue with spikes in spend across channels.

    Example (real‑world style):

  • You plot a graph of the last 2 years.
  • Every time the company spends $50k on YouTube, total sales go up ~10% three weeks later.
  • You use regression analysis to estimate how much of that 10% lift came from YouTube, while controlling for:
    • Seasonality
    • Promotions and discounts
    • Macroeconomic factors (e.g. recession, Covid, etc.)

    • What MMM Actually Tells You

    MMM is mainly about answering "what really moved the needle?"

  • Saturation

      "We spent $50k on TikTok. If we spend $100k, will revenue double, or are we already close to the audience limit?"

  • Incrementality

      "Did those branded search ads actually drive new sales, or would those people have bought anyway?"

  • Offline Impact

      "Did that podcast sponsorship, TV campaign, or OOH billboard actually do anything for revenue?"

    • These outputs help you re‑allocate budget toward the channels and spend levels that truly drive incremental impact.

    Why MMM Is Coming Back (Privacy & iOS 14+)

    MMM is having a big comeback for one main reason: privacy changes.

  • Apple and Google have made it much harder to track individual users across sites and apps.
  • Cookies are dying and tracking pixels are often "blind".
  • Your ad platform might report 10 sales, while your bank account shows 50.

    • MMM avoids this problem because it:

  • Does not rely on cookies or user‑level tracking.
  • Only looks at aggregated data like Total Spend vs. Total Revenue.
  • Is more robust to privacy laws and tracking restrictions.

    • Visual Example