Understand how every channel contributes to your bottom line. Use advanced statistical modeling to allocate your budget where it matters most.
All your tests, one database
Our MMM doesn't just guess. It uses your database of past lift tests as Bayesian priors to calibrate the model, ensuring results are anchored in reality.
Every geo test, user-level test, and platform lift test you run is stored and automatically available for calibration.
We transform your experimental lift results into priors that guide the regression, preventing unrealistic coefficients.
Automatically validate model accuracy by holding out time periods where you ran known experiments.
The most useful MMM systems are not just model fits. They are operating workflows for priors, calibration, response curves, budget scenarios, and repeated validation.
Convert lift tests and business knowledge into priors or constraints so the model starts from experimentally grounded expectations.
Estimate adstock and saturation so the team can see where spend is still scalable versus where marginal returns are flattening.
Compare budget scenarios and reallocation plans with explicit economic targets such as revenue, margin, or payback.
Re-check the model against new experiments and holdout periods so it remains useful as channels, privacy conditions, and creative mix evolve.
Decompose your sales into baseline, seasonality, and media effects. See exactly how much revenue Facebook, Google, and TV are driving.
Simulate different budget scenarios. Our optimizer suggests the ideal allocation to maximize revenue or ROAS based on saturation curves.
MMM doesn't rely on cookies or user-level tracking. It's the perfect future-proof solution for measurement in a privacy-centric world.
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.