Know which dollar
actually moved revenue.

Attribution, media mix modeling, forecasting, and budget allocation, fit on your own data. It refreshes nightly and refits weekly. No vertical baked in.

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Illustrative

app.bayesin.com/models/… fits on your data

Modeled revenue

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Blended ROI

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Spend

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Forecast

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Modeled vs. forecastweekly · credible interval

Channel contribution

Paid search

Paid social

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The layout is real and the values are placeholders. In the product, every figure is read from your own uploaded data.

Rigor

It won't hand you a number it can't defend.

Each of these stops a fit and tells you why, instead of handing you a number that looks like an answer. See the method →

Contribution exceeds revenue

If the channels' modeled contribution adds up to more than the revenue you actually booked, the fit is claiming credit for money that does not exist. It is rejected.

Nothing attributed to media

When the model hands every dollar to baseline and none to media, it has learned nothing about your channels, usually because spend barely varied. Publishing it would only look like an answer.

The sampler has not converged

A Bayesian fit runs several chains that have to agree. When they do not, the posterior cannot be trusted, however confident any single figure looks. The fit is refused and the page says so.

Sources →

Every channel. Every ledger. One model.

Ad platforms, CRM, warehouse, and commerce data land in the same touchpoint schema.

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