What is media mix modeling?

Media mix modeling is a statistical model that estimates how much each marketing channel adds to sales. It works from weekly totals of spend and revenue, not clicks or cookies. It usually needs two or more years of weekly data and spend that moves. It answers budget questions by channel, not which ad got the sale.

In plain terms, MMM is a regression on your own history. You line up weekly sales against weekly spend for each channel, plus promotions, price and seasonality. The model then estimates how much of the sales line each input explains.

Meta's open-source Robyn guide calls it an econometric model of the incremental impact of marketing and non-marketing activity on a KPI, such as sales. Google's open-source Meridian frames the job as three questions:

• What did each channel return in the past?
• How does a channel's impact change as spend goes up?
• How should the next budget be split to get the most out of it?

Two assumptions sit inside almost every model. Ads keep working after the week they run, and each extra euro buys a little less than the one before. Robyn's docs put the first one plainly: I see ads today and buy next week.

Because it reads totals, MMM needs no cookies, pixels or user-level data. That keeps it working as tracking gets patchier. It also means it never sees a single customer.

What one store's data shows

One store's anonymised GA4 export, 1 January 2024 to 21 August 2026. It holds shares of revenue only: no ad spend, no order counts.

Start with what is missing. The export holds no ad spend, so nobody could fit a media mix model from this file. Every MMM needs spend by channel and by week.

What the export does show is timing. On the Journeys sheet, journeys with 1 touch hold 79.5% of revenue and took 0.5 days to buy. Journeys with two or more touches hold 12.2% + 5.4% + 3.0% = 20.6% of revenue on the Journeys sheet, about a fifth. On the Journeys sheet, those longer journeys took 12.5 to 16.9 days to buy.

For a weekly model, that split matters. A half-day purchase lands in the same week as the touch that started it. A two-week journey spills into the weeks after, and that spillover is what an MMM's lag settings exist to capture.

Then Direct. On the Channels sheet, Direct holds 57.7% of revenue in last click, first click and touched. A media mix model has no Direct line. Sales it does not credit to marketing sit in the baseline. Meridian defines that as the expected outcome with paid and organic media set to zero.

So the two answer different questions. The export shows paths and timing for one store. An MMM estimates what each channel's budget did in total, and never sees a path.

Why do media mix models and GA4 disagree?

They count different things. GA4 credits the visits it recorded. An MMM ignores visits and reads weekly totals, including channels nobody clicks, such as TV or a podcast sponsorship.

So a channel can look weak in GA4 and strong in an MMM, or the other way round. Neither is lying. They are grading different homework.

They also work at different depths. Meridian's docs advise against campaign-level models, calling MMM a macro tool that works well at channel level. For detail below that, the same page recommends data-driven multi-touch attribution for digital channels.

A fair split of jobs: MMM for how much each channel should get, paths and tests for what happens inside a channel.

What can a media mix model not tell you?

• Spend you never tried. Meridian estimates each response curve from the observed range of your spend. Beyond that range it is extrapolating, and its docs ask for caution.
• Whether it is right, from fit alone. Meridian's docs say no R-squared threshold makes a model good or bad. Several models can fit well and still disagree on ROI.
• What a channel with flat spend did. Robyn's guide warns that if an activity stays constant while sales vary, the model struggles to tell what it did.
• Much from thin data. Meridian's own example uses two years of weekly data, 104 data points, with 26 parameters. That is four data points per parameter, which the docs call too low to estimate the model reliably.

That is why both projects push calibration with experiments. Robyn's guide calls lift test results the ground truth to calibrate against.

What to do this week

1. See what your baseline would have to absorb. In GA4, open Reports > Acquisition > Traffic acquisition and read Direct's share of total revenue. Pass: you can explain your Direct, from repeat buyers to typed addresses. Fail: Direct is your biggest channel and nobody knows why, so fix tagging before you model anything.
2. Check that your spend moved. In Google Ads, open the Campaigns table from the page menu, click the segment icon and choose Time, then Week. Pass: weekly spend rises and falls across the year. Fail: it is nearly flat, so a model has little to learn about that channel.
3. Measure your lag. In GA4, open Advertising > Key event attribution paths and read the Days to key events column. Pass: you know how long multi-touch buyers take, which tells you how much carryover to expect. Fail: the table is empty, so GA4 is not recording purchases as a key event.

Check the homework. Your GA4 Attribution paths export already holds the evidence. Causality Engine reads that one file and shows what each channel caused next to what last-click gave it, in 1 to 2 minutes, for €99 once (excluding VAT), refundable within 30 days. Check the homework

Sources, 1 October 2026: An Analyst's Guide to MMM (Meta). Key Features (Meta). Robyn (Meta). An introduction to Meridian (Google). Amount of data needed (Google). About MMM as a causal inference methodology (Google). Media saturation and lagging (Google). Assess the baseline (Google). Traffic acquisition report (Google). Key events attribution paths report (Google). Use segments in your tables (Google).

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