A Bayesian model attributes revenue across every channel you spend on, accounting for the lag between spend and sale, diminishing returns at high spend, seasonality, price and promotions. It works from aggregate spend and revenue rather than user-level tracking, so it survives cookie loss and covers TV, print and sponsorships. The output is a contribution estimate per channel with a credible interval, and a budget scenario tool.
Add up the conversions each platform reports and you get more revenue than the company earned. Every dashboard is confident, and together they are arithmetically impossible.
Anything without a click — TV, outdoor, sponsorship, podcasts — shows nothing in the attribution tool. It gets cut first, and the effect shows up as an unexplained dip a quarter later.
Spend doubles on the channel with the best reported ROAS and revenue does not follow. Without a saturation curve, the answer only arrives after the money is spent.
Weekly spend by channel, revenue, price and promotion calendar, distribution changes, seasonality and major external events. Twelve months is the floor, twenty-four is where it becomes reliable.
Each channel gets a lag structure — how long the effect persists — and a diminishing-returns curve. Without both, a model will happily recommend infinite spend on the last thing that worked.
A Bayesian fit lets known constraints and prior tests inform the model, and produces a range rather than a single number. A point estimate for TV contribution is false precision, and we do not print one.
The model is checked on a held-out period and, wherever possible, against geo tests or blackout periods you have actually run. Agreement with a real experiment is worth more than a good fit.
A dashboard where the team shifts budget between channels and sees the modelled revenue effect with its uncertainty. Planning conversations move from opinion to a shared model with visible assumptions.
We build in your stack rather than moving you onto ours. The list below is what this solution most often connects to — other systems are a scoping question, not a blocker.
Attribution follows individual users and their clicks; MMM works on aggregate spend and revenue over time. It is unaffected by cookie loss and covers offline, which is exactly where attribution is blind.
We use cookies for analytics — to see which pages bring enquiries. Nothing else, and nothing before you agree. Cookie Policy