The measurement methodology
Bayesian, transparent, and honest about what the model doesn’t know.
Priors bank
Every model Acera runs starts with priors — beliefs about likely channel effectiveness, drawn from a k=5 floor of past engagements in the same vertical. Before your data shapes anything, the model knows: in retail, paid search typically drives 30–40% of revenue. That belief updates the moment your data arrives. It shrinks as evidence accumulates. It never disappears entirely — because one quarter of data is never the whole story.
Priors are differentially private. Each prior contribution is noise-injected (ε=1.0) before entering the bank. Your data teaches the bank without being identifiable in it.
Opt-in: enabled by default. You can opt out at any time from Settings.
Credible intervals, not point estimates
Traditional MMM reports: “Paid search drove $2.7M.” Acera reports: “Paid search drove $2.7M (95% CI: $2.2M–$3.1M).” That range is not a caveat — it is the answer. The width of the interval tells you how confident the model is. A narrow interval means strong evidence. A wide interval means more data is needed before you act.
(95% CI: $2.2M–$3.1M)”
Model warm-start
When a new customer joins, the model does not start from zero. It starts from the priors bank for their vertical. The first month’s model is already calibrated to something reasonable. By month three, your own data dominates. By month six, you have a model built entirely on your evidence — with the priors bank as a distant prior that barely moves the needle.
Bell curve width represents credible interval. Narrower curve = stronger evidence from your data.
All customer data is stored in Sydney, Australia (AWS ap-southeast-2). Your data does not leave Australian jurisdiction. Full details on the Trust page
See credible intervals in a live result
The Auditor demo shows real credible intervals from a Bayesian MMM run — channel contributions with uncertainty ranges, not point estimates.
See a live Auditor result