What Google opening up Meridian changes for marketing mix modeling
Google made its open-source marketing mix model available to everyone on January 29. Here is what it offers, what it still demands from you, and who should try it.
On January 29, Google announced that Meridian, its open-source marketing mix model, is available to all marketers and data scientists. Google says it tested the model with hundreds of brands before this release. The code is on GitHub, free to download, and open to inspection. That removes a license fee as a reason not to run an MMM. It does not remove the other reasons.
What Meridian actually offers
From Google's announcement, the features that matter to a media buyer are these:
- Bayesian causal inference. You can encode what you already believe about a channel and let the data update it, which suits teams that have some prior evidence.
- Experiments as priors. Meridian can use incrementality test results, regardless of channel or experiment type, to calibrate the model. This is the feature we would care about most, because it ties the model to something you ran rather than only to correlations in spend data.
- Reach and frequency. Google says the model handles reach and frequency, not just impressions. Ten impressions could be ten people once or one person ten times, and the response curves are different.
- Google Query Volume. For Google media, advertisers get impressions, clicks and cost plus added dimensions such as query volume, intended to show how paid search drives results.
- Budget scenarios. You can run several optimization scenarios and use KPIs such as sales, conversions or profit, and include non-media variables such as pricing and promotions.
- A partner program. Google says more than 20 measurement partners are trained and certified on Meridian.
What it does not do
An MMM is only as good as the data and the specification you give it. Google's announcement does not list data requirements, and that is the part to plan for. In practice you need consistent spend and outcome data across channels at a regular cadence, enough variation in spend over time for the model to separate channels, and a sensible record of anything else that moved sales, such as launches, pricing changes and promotions. If every channel went up and down together all year, no model can tell them apart.
Open source also means somebody on your side owns the model. That person needs enough statistics to check convergence, question implausible results, and explain the output to finance. If you do not have that person, use one of the certified partners or wait.
Who should try it now
A reasonable rule: try Meridian if you spend across at least three meaningful channels, have a long enough history of consistent data, and can answer budget questions that platform reporting cannot, such as how much of your paid social impact shows up as branded search. For mobile app advertisers this is especially relevant because install-level attribution is partial on iOS, so an aggregate view is a useful cross-check.
Do not try it if one channel is nearly all of your spend. There is nothing for the model to allocate, and you will get a precise-looking answer to a question you have already settled.
Use it to ask questions, not to hand down budgets
The biggest risk with a free, polished model is false confidence. A few habits help:
- Run it next to your existing numbers. Compare its channel ranking against platform-reported and MMP-reported results. Large disagreements are the interesting part, and they deserve an experiment, not an argument.
- Feed it your tests. If you have geo or holdout results, use them as priors. If you do not, design one test for your biggest uncertain channel this quarter.
- Treat the budget scenarios as hypotheses. Shift a modest share of budget, say 10 percent, toward what the model recommends, and watch real results before going further.
- Check the Google-specific inputs. Query volume is a Google-provided dimension, so ask whether the model is flattering search simply because search gets the richest data. We would pressure-test that with a brand-versus-nonbrand split.
Google also said it will add features and improve the methodology in the coming months, so expect the tooling and guidance to move. Pin a version when you run it, so that results from this quarter and next quarter are comparable.
The practical takeaway: the cost of trying an MMM just dropped, but the cost of doing it well did not. Spend your effort on clean data and a calibration test, and the model will be worth running.