Bayesian foundations
Models are built on proven open-source approaches (Robyn and Lightweight MMM) rather than a black box, so results can be interrogated and defended in a client meeting.
How it works
Triple M automates the three stages that used to need a data science retainer: pulling the data together, modelling it properly, and translating it into action.
HOW IT WORKS
Automated pipelines pull all your scattered ad platform data into one secure warehouse. Zero manual uploads.
Our AI agent uses proven Bayesian statistics (Robyn & Lightweight MMM) to calculate carryover, saturation, and where to spend your next marketing pound.
No complex charts or black-box formulas. Get clear, conversational answers on where to shift budget for maximum growth.
Illustrative recommendation
“Where should I invest more budget?”
Increase Google Ads by £4,200 and decrease Meta by £3,100. This could drive approximately 17% more revenue at a lower CPA.
Illustrative example. Not a real prediction.
The method
Models are built on proven open-source approaches (Robyn and Lightweight MMM) rather than a black box, so results can be interrogated and defended in a client meeting.
We model advertising adstock and diminishing returns, so you can see the point at which extra spend on a channel stops paying for itself.
Modelling runs on aggregated weekly data. No cookies, no individual-level tracking and no reliance on signals that are disappearing.
Every recommendation is shown with its modelled uncertainty. Where the data cannot support a conclusion, Triple M says so.
Data sources on the Triple M roadmap
Integration coverage shown is indicative of our launch roadmap.
Comparison
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