Marketing Mix Modeling for Brands Under €10M: What Meridian Will Not Tell You

Google's Meridian made marketing mix modeling software free when it went generally available in January 2025, but it didn't make the method cheaper to feed: Google's own Meridian documentation recommends at least two years of weekly data for geo-level models and three for national ones. That history has to be clean, with spend that varies enough to separate the channels; without it, coefficients wobble between runs and the uncertainty swallows the answer you paid for. If you don't have that history, the channel question behind most budget decisions has a cheaper answer: a regional holdout on your own sales data. Here's the honest math.

What did Google Meridian actually ship in 2026?

Credit where due: Meridian is a serious free measurement tool. It went generally available in January 2025 as an open-source marketing mix modeling framework.

Free software is not the same as a free answer. The license costs nothing. The data, the cleaning, and the analyst weeks are where the bill lands, and that bill scales with how messy your history is.

Why does marketing mix modeling need so much data?

MMM is regression on aggregated history. You feed it weekly spend per channel, weekly revenue, seasonality, promotions and pricing, and it estimates how much each input moved the total. The math is respectable. The problem is arithmetic: 2 years of weekly data is about 104 observations, and you are asking the model to split credit across several channels that mostly went up and down together.

When channels move together, the model cannot tell which one did the work. Statisticians call it multicollinearity. Practitioners call it "the Meta and Google coefficients swapped sign again between refreshes." Advertisers escape this when their spend varies enough, across regions, channels and time, to create separation in the data. When three channels rise and fall together, that variation isn't there, whatever the budget.

Clean is doing heavy lifting in that sentence, too. Clean means the same channels tracked the same way for the whole period: no store re-platforming, no re-tagging of campaigns, no six-week gap where the pixel was down. Many brands changed something material in the last two years: a new site, a new agency, a new channel mix. Every one of those resets the clock on usable history.

The model tells you so honestly: uncertainty ranges so wide they include both "scale this channel" and "cut it." That is not a Meridian flaw. It is what the method costs, and it is why MMM suits advertisers with long, varied spend history.

What are the real minimum requirements per method?

Here's the table worth having before the first MMM pitch. The MMM history row follows Google's Meridian documentation; the rest are rules of thumb as of 2026, not vendor guarantees, and your mileage will vary with data quality.

Two rows deserve a second look. Platform attribution is instant and free, but an attribution model redistributes credit among touches without asking whether the sale would have happened anyway, and every platform grades its own homework. The causal read row is not a lite MMM either. It answers a different question: not "allocate €5M across 12 channels" but "which channels caused last month's sales, and which only collected the credit?"

Is a causal read a stand-in for MMM?

For channel-level budget decisions, mostly yes. If the question isn't a national media plan across TV, retail and sponsorship, but whether to move €10K from paid social to paid search, whether email deserves the credit last click gives it, or whether the credit a platform reported last month was real, those are incrementality questions about individual channels. You can answer them one channel at a time with no software: cut the channel's spend in one region for a few weeks and compare revenue with the rest. Causal inference on your GA4 export answers the same question for every channel at once; the methodology piece on proving causation without an experiment covers how the counterfactual estimation works.

Where MMM still wins: true budget allocation across many channels including offline, saturation curves for annual planning, and scenario simulation once you have years of history. If that is your problem and you have the data, Meridian is a genuinely good free option. Just do not mistake "free to install" for "ready to decide."

The cadence difference matters as well. An MMM refresh is a project: data collection, validation, a review meeting, so the answer often arrives after the budget has already moved. A holdout or a causal read on last month's data can run every month, which changes what it is: it stops being an annual verdict and becomes a steering wheel.

When does MMM become the right call?

Three signals say you're ready. You have 2 to 3 years of consistent weekly tracking you actually trust. Your spend varies across enough channels and regions to create separation. And the question on the table is allocating a budget big enough that a 10% improvement pays for the analytics work. Until all three are true, MMM gives you the aesthetic of rigor without the rigor.

When should you revisit MMM as you grow?

Treat MMM readiness as a scheduled checkpoint, not a vibe. Revisit the question once a year, and any time one of these shifts: you add channels GA4 cannot see, like retail or offline; your spend spreads across enough regions to give a model real variation; or leadership starts asking for annual scenario planning rather than monthly channel reads. Between checkpoints, protect your future options: keep weekly spend and revenue history tidy, annotate re-platforming and re-tagging events so a future analyst can see the seams, and avoid switching attribution windows mid-year. Brands that do this arrive at their first MMM with usable history. Brands that do not arrive with two years of expensive noise.

There's also a sequencing argument: causal answers now make your MMM better later. Regular holdouts and causal reads build the habit of asking causal questions, give you a record of what each channel caused to check the model against once the data exists, and keep you from making two years of budget decisions on last click while you wait to be "MMM-ready." When the CFO asks why marketing deserves its budget next year, you want a track record of causal answers, not a promise to hire a statistician; the CFO budget-defense kit covers that meeting.

Once a holdout has shown you how one channel behaves, a causal attribution tool like Causality Engine is the bottom row of the table: it reads a GA4 export and shows what each channel caused, next to what last click gave it.

Key takeaways

• The software is free; the data and analyst time are not.
• Google's Meridian documentation recommends 2 to 3 years of weekly data (two for geo-level models, three for national), and the spend has to vary enough to separate the channels. Below that, coefficients wobble and the uncertainty swallows the answer.
• Roughly 104 weekly observations cannot reliably split credit across channels that move together. That is arithmetic, not a Meridian bug.
• For channel-level budget decisions, a regional holdout answers the question MMM would, in weeks instead of years, and a causal read on a GA4 export answers it for every channel at once.
• Causal answers now, MMM later: regular holdouts and reads give you decisions today and a record to check the model against when you have the history.

Further reading

• How to prove a channel caused revenue without running an experiment
• The CFO budget-defense kit: proving marketing caused revenue