Marketing mix modeling services: custom MMM built on open-source foundations (Meridian, Robyn) with your own data pipeline privacy-proof measurement beyond attribution.
Attribution tells you less than it used to
Last-click flatters search, multi-touch models fail as tracking degrades, and platform-reported conversions each claim credit for the same sale. Meanwhile TV, audio, retail media, and brand spend barely leave a click trail at all. MMM answers the question attribution can't: if we move a dollar between channels, what actually happens to revenue?
What an MMM engagement includes
- Data pipeline assembling spend, impressions, revenue, pricing, promo, and seasonality history
- Custom model build on Meridian, Robyn, or a bespoke Bayesian specification whichever fits your data
- Adstock and saturation curves per channel, so diminishing returns are visible before budget is wasted
- Budget-allocation scenarios: what shifting spend between channels does to modeled revenue
- Calibration against experiments and holdouts where available, so the model earns trust
- Automated retraining as new weeks of data land a living model, not a one-off consulting PDF
How we work
Audit data history first MMM needs 2+ years of clean weekly data, and we build the pipeline that assembles it
Start with a model your team can interrogate, not a black box
Validate against known events (price changes, channel pauses) before trusting forecasts
Operationalize: the model runs on your infrastructure and retrains on schedule
Typical stack
Frequently asked questions
MMM is a statistical technique that estimates how much each marketing channel contributes to revenue by analyzing historical spend and outcome data along with pricing, seasonality, and promotions. Because it works on aggregate data rather than user tracking, it is unaffected by cookie loss, iOS privacy changes, or walled gardens.
Attribution assigns credit for individual tracked conversions along a click/view path; it breaks as tracking degrades and can't see offline or brand channels. MMM measures channel contribution statistically from aggregate outcomes, capturing effects attribution misses at the cost of granularity. Mature teams run both: attribution for tactical in-platform decisions, MMM for budget allocation.
Roughly two to three years of weekly data across spend, revenue, and the major business drivers (pricing, promotions, distribution, seasonality). Less history means wider uncertainty. Assembling and cleaning this dataset is usually half the project which is why MMM done well is a data engineering problem before it's a statistics problem.
Open-source frameworks like Meta's Robyn and Google's Meridian are now credible foundations transparent, auditable, and free of per-seat licensing. What they don't include is the data pipeline, calibration, and operationalization around them. We build on open source and invest the budget difference in data quality, which is what actually determines model accuracy.
Go deeper
Find your real channel mix
Tell us your channels and data history and we'll assess whether your data can support an MMM honestly, before you commit.
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