A media mix model you can actually trust.
A Bayesian MMM on ingested or uploaded data. Stella shows the metrics that prove it predicts, MAPE, R², and VIF, with forward and back testing, then turns the model into a budget you can act on.
- ✓Build a model that predicts out-of-sample, not one that just looks confident.
- ✓Reallocate budget to maximize incremental revenue, the whole point of an MMM.
- ✓Model on spend or impressions, add unlimited controls, calibrate with your holdouts.
Included in every plan, free and up. No credit card.
Trusted by growth teams who left enterprise measurement vendors
Any MMM outputs a confident result. Few are accurate.
Tools that ingest every platform and show a confident live dashboard are easy to believe and easy to be wrong. The only way to trust a model is to test it on data it has not seen. Stella shows out-of-sample MAPE, R², and VIF on every model, with forward and back testing, so you know it predicts before you bet budget on it.
First, a model that actually predicts.
Stella fits a Bayesian media mix model to your data and validates it until it earns trust. You see the numbers that prove it, not a black box. Configure saturation and ad-stock for longer lead cycles, and calibrate with your Stella holdouts to make it more accurate still.
Saturation curves per channel.
An accurate model tells you the spend level where returns flatten, so you can see which channels still have headroom and which are past saturation. That is what turns a model into a decision.
Then reallocate to maximize incremental revenue.
A model is only worth as much as the decisions it drives. Stella's budget optimizer moves spend from saturated channels into the ones with headroom, so the same budget returns more. Most teams never use this half. The outcome is the point, not the report.
Built for real data science, not a dashboard.
Spend or impressions
Model on spend or impressions, and add as many control columns as you need: promotions, pricing, weather, seasonality.
Calibrate with holdouts
Use your Stella holdout results as priors so the model is anchored to causal truth, not just correlation.
Forward and back testing
Validate the model on held-out periods so you trust it before you act, and watch the validity metrics, not just the iROAS.
Hear it from the brands we run measurement for.
Media mix modeling is included in every plan, free and up.
Have us run it, or run it yourself.
Most brands want measurement done for them. That's our consultancy, and it's where most clients start. Not ready for done-for-you? The same platform is self-serve, from $6,000 down to free. There's an option for everyone.
$10k isn't for everyone. There's a plan for every budget.
Prefer to run it yourself? Same platform, self-operated: the full loop at $6,000, always-on incrementality from $3,000, attribution and surveys from $750, or start completely free. Walk down to the plan that fits.
- ✓Two media mix models and two holdout analyses every month
- ✓Upload a spreadsheet, get a real model back. No connectors to set up.
- ✓The same models we run for enterprise brands, not spreadsheet math
- ✓Ask questions about your results in plain English
- ✓Multi-Touch Attribution: see every touchpoint that leads to a sale
- ✓Ask buyers how they found you, right after they check out
- ✓Connect up to 5 sources (Shopify, Amazon, Meta, Google, TikTok) and stop pulling reports by hand
- ✓Run as many holdout tests and media mix models as you want, on data you upload
- ✓Ask questions about your results in plain English
- ✓Everything in Starter
- ✓Always-On Incrementality: a live view of how incremental every campaign is, updated daily, with no test to run
- ✓Holdouts and media mix models run straight off your connected data, region by region. Up to 7 sources, no more uploads.
- ✓See where the next dollar should go, and where a channel is already saturated
- ✓Ask your data questions from Claude, ChatGPT and your other AI tools
- ✓Everything in Professional
- ✓We send incremental conversions back to Meta, Google, TikTok and more, so their algorithms optimize for real profit instead of last click
- ✓All of your Stella data delivered into your own warehouse
- ✓Unlimited data sources
Media mix modeling questions, answered.
What makes a media mix model accurate?
A model is only as good as its predictions on data it has not seen. Stella reports out-of-sample MAPE, R², and VIF on every model, and supports forward and back testing, so you can confirm the model predicts before you reallocate budget on it. A confident-looking result is not the same as an accurate one.
What are the two parts of MMM as a service?
First, build a model that accurately predicts what makes revenue go up or down. Second, use that model to reallocate budget toward what is actually incremental. Many teams stop after part one and never act on it. The whole point is the outcome, so Stella includes a budget optimizer to turn the model into decisions.
Why do live-dashboard tools give a different number?
Tools that ingest every platform and show a confident live dashboard are easy to believe and often inaccurate, because they rarely show the validity metrics or confidence bands. Any MMM will output a confident result. Ask for the out-of-sample numbers. If they are not shown, the confidence is not evidence.
Can I model on spend or impressions?
Both. Model on spend or on impressions, add as many control columns as you need (promotions, pricing, weather, seasonality), and tune saturation and ad-stock for longer lead cycles.
How do I calibrate the model with holdouts?
If you have run holdouts in Stella, you can calibrate the MMM with those results as priors. Calibrated models are more accurate and more stable, and the calibration keeps the model honest about what is truly incremental.
What should I look for in the results?
A low out-of-sample MAPE, a high out-of-sample R², and a low VIF across channels. Those tell you the model predicts and that channels are not collinear. Stella surfaces all of them on every model.
A real data-science tool. Or we run it for you.
Start free and build a model you can trust. If running it is more than your team wants to take on, a PhD data scientist will run it for you from $10,000/mo.