Media Mix Model
Finally know which channels actually drive revenue.
The Problem
Last-click attribution lies. Your CFO wants proof that marketing spend is working. A media mix model delivers it — decomposing revenue into the true contribution of each channel, accounting for adstock effects, diminishing returns, and cross-channel interaction.
How It Works
Data Integration
We connect your media spend, revenue, and contextual data into a unified dataset.
Bayesian Modeling
We fit a Bayesian MMM with adstock transforms, saturation curves, and informative priors.
Budget Optimization
We generate optimal budget allocations across channels based on marginal ROI curves.
Calibration
We calibrate model outputs against holdout tests and provide an ongoing refresh framework.
What You Get
- Channel-level ROI estimates with confidence intervals
- Optimized budget allocation recommendations
- Scenario planner for what-if budget analysis
- Calibration framework using holdout or incrementality tests
- Executive summary for stakeholder communication
Who It's For
Brands spending $50K+/month on paid media across 3+ channels who need to justify and optimize their marketing budget.
What This Looks Like in Practice
A multi-channel retailer spending $400K/month across Meta, Google, TikTok, and TV couldn't agree on which channels deserved more budget. Our MMM revealed that TV had 3x the attributed ROI of what last-click showed, while Meta was hitting steep diminishing returns. Reallocating 15% of Meta budget to TV generated an estimated $180K in incremental quarterly revenue.