Estimate channel contribution from aggregate data, anchor the model to your lift tests, compare response curves, and choose budget plans with forecasts and intervals. Privacy-safe by design with no cookies or user-level tracking.
The model, the optimizer, and the forecast in one workflow, anchored to experiments you actually ran.
Calibrated by tests
Use geo, user-level, and platform lift results as evidence for the model.
Response curves
See where channels scale, saturate, and start producing weaker marginal returns.
Budget optimizer
Compare reallocations against revenue, margin, payback, or ROAS targets.
Forecasts with intervals
Plan against scenario ranges instead of a single optimistic projection.
An MMM is only as good as what anchors it. Ours uses your database of real lift tests as Bayesian priors, so results stay tied to causal reality, not just historical correlation.
Every geo test, user-level test, and platform lift study you run is stored and ready for calibration.
We turn your experimental lift into priors that guide the model and prevent unrealistic coefficients.
Hold out periods where you ran known experiments and check the model agrees with what really happened.
A modern MMM is not a one-off model fit. It is a workflow that runs end to end and keeps you in the driver's seat.
Add spend, sales, and a few business drivers. Connect a file or a source with no cookies or user-level tracking required.
Pull in your geo, user-level, and platform results as priors so the model is anchored to causal truth, not just correlation.
See how much each channel really drives across baseline, seasonality, and media, each with a clear uncertainty range.
Compare budget scenarios, find the efficient allocation, and forecast revenue under each plan before you commit.
Saturation curves show where each channel is still scalable versus where returns are flattening. The optimizer turns that into a concrete reallocation: same budget, more return.
Recommended reallocation
Response curve · diminishing returns
See where each channel saturates, then shift budget to where the next dollar still works hardest.
Project revenue forward under each budget scenario with honest uncertainty bands, so you can plan against a range instead of a single optimistic line.
Revenue forecast by scenario
Every forecast comes with 65% and 95% confidence intervals — so you see the likely range and exactly how sure the model is.
Decompose sales into baseline, seasonality, and media. See exactly how much revenue each channel drives.
Simulate scenarios and get an allocation that maximizes revenue or ROAS against your saturation curves.
No cookies, no user-level tracking. A future-proof way to measure in a privacy-centric world.
The most useful MMM systems are operating workflows for priors, calibration, response curves, budget scenarios, and repeated validation.
You don't need to be a statistician. The hard math runs under the hood; you point, click, and make the call with the full picture in front of you.
Geo, user-level, and platform tests all feed one central database. That shared truth calibrates and validates your MMM, while attribution keeps everything pointed in the right direction. Run any piece on its own — or run them together and let each one make the others stronger.
Inform. Attribution gives the MMM a fast, directional read on what's working between tests.
Calibrate. Incrementality tests anchor the MMM to causal ground truth, not just correlation.
Validate. Holdout tests check that attribution and the model agree with what really happened.
The models and tests matter, but the workflow around them matters too. Shako Stats is designed to become the operating system around experiment planning, metadata, documentation, and cross-test learning.
Centralize datasets, mappings, and historical records so experiments and models always start from the same source of truth.
Organize tests by audience, creative strategy, bidding logic, or business objective so learnings remain searchable and reusable.
See what tests are planned, in-flight, or completed so overlapping interventions and measurement conflicts are easier to manage.
Turn methodology, definitions, and experiment design guidance into an internal operating system instead of leaving them scattered across decks.
Connect any AI agent through MCP/API to read-only MMM context: priors, response curves, contribution estimates, forecasts, optimizer outputs, diagnostics, and calibration tests. The statistical model stays transparent and reproducible; the AI helps the team ask better questions of the results.
Meet your AI Marketing ScientistOne-click OAuth for Claude & ChatGPT, read-only, anyone on the team can ask
Design tests, understand results, and connect your team to one statistics-backed measurement workspace.
Paid B2B plans built for brands & agencies.