Rigorous computation
Tested, versioned statistical engines.
AI-orchestrated Bayesian MMM
Measurement you can question, trust and own.
AI orchestrates the process using your business context. Tested, versioned statistical engines compute the results.
Assumptions, uncertainty and limits are explicit. Built with your team. Transferred to you. Not SaaS.
Use it in ChatGPT or Claude.Connected through MCP to the core MMM engine. No new dashboard.
Honest uncertainty
What the data supports—and what it cannot answer.
No new dashboard
ChatGPT or Claude, connected through MCP.
A system you own
Built with your team. Transferred to you.
Transparency
Measurement you can challenge is measurement you can trust.
Every result includes its evidence trail.
Assumptions
What went in
Priors and business context.
Evidence
What the data supports
Estimates, ranges and diagnostics.
Limits
What remains unresolved
Weak variation and correlated channels.
Next test
What could answer it
Lift, geo or holdout evidence.
Evidence boundaries
Where the data cannot answer, we say so—and tell you what could.
The channel can be separated.
Spend varies enough and independently enough. ROI is estimated with uncertainty, supported by diagnostics and checks on assumptions.
The channels move together.
The result is marked as uncertain, not presented as a confident channel ROI. This can be a limit of the data rather than a modelling failure.
Core modelling choices are versioned before final results are interpreted. Changes remain visible.
Question it. Trust it.
Ask any question about the model or its results.
Probe assumptions, compare alternative media plans or ask how any metric was calculated. If the evidence cannot answer, Transparent MMM says so rather than inventing a misleading explanation.
- Assumptions
- Alternative media plans
- Metric calculations
Search ROI is estimated at 1.8×–10.4×. That range is too wide for a confident decision. Why is it so wide, and how can we narrow it?
Why the range is wide
Search spend varies little and moves closely with Facebook Performance (correlation coefficient r = 0.78) and Instagram (r = 0.72). The model therefore cannot cleanly separate Search’s effect.
How to narrow it
Use a geo holdout test, a conversion-lift test or deliberately vary spend to create independent evidence. I can help you design the geo holdout or conversion-lift test.
Facebook Brand ROI is 2.6×—well below what we expected. Why?
What the evidence supports
Facebook Brand ROI is estimated at 2.0×–3.2×. The evidence is strong enough to override the initial prior of 6×–12×.
What the evidence cannot answer
This data does not show why ROI is lower. I cannot reliably infer a cause, so I will not invent one. Answering that requires a carefully designed experiment.
Ownership
Not SaaS. Built with you. Transferred to you.
- 01
Build
A measurement system configured around your business, data and decision process.
- 02
Transfer
The working system, code and configurations—with knowledge transfer to your team.
- 03
Advise
Ongoing support when you need it, as your questions and measurement needs evolve.
Private by architecture
Your data does not leave your control.
Runs inside your controlled environment. Nothing is pooled with another customer or locked inside a vendor portal.
Bring one budget decision
See what the data can answer—and what evidence to collect next.
Bring your channels, data and the decision you need to make. We’ll shape the demo around your context.
Transparent MMM by Thoughtful Robots