The Problem
What Google tells you
Google Ads: 4.2× ROAS
Every platform reports its own attribution. Every platform inflates its own numbers. You are making budget decisions based on data that every channel has a financial incentive to distort.
What the model tells you
P(exceeding target) = 0.94
The Bayesian model produces a posterior distribution of expected returns for each channel: a probability-weighted estimate of what each pound is actually driving, independent of platform reporting.
What We Build
Bayesian Marketing Mix Model, built on your actual revenue and spend data
Posterior distribution reporting, probability of exceeding target by channel
Budget reallocation recommendations with documented confidence intervals
Stochastic optimisation, real-time budget adjustment under constraints
Channel attribution beyond platform-reported ROAS
Phase-Gate accountability, before and after revenue attribution audit
Frequently Asked
What is Bayesian Marketing Mix Modeling?
Bayesian Marketing Mix Modeling (Bayesian MMM) is a statistical framework that uses Bayesian inference to estimate the contribution of each marketing channel to business outcomes. Unlike platform-reported attribution (which every platform inflates in its own favour), Bayesian MMM produces a posterior distribution of expected returns for each channel: a probability-weighted estimate of what each pound or dollar is actually driving. The model updates as new data arrives, becoming more accurate over time.
How is this different from Google Analytics attribution?
Google Analytics attribution assigns credit to the last click, the first click, or a linear distribution across touchpoints, all of which are arbitrary rules, not statistical models. Bayesian MMM models the actual causal relationship between marketing spend and revenue using regression analysis, time-series data, and prior knowledge about channel behaviour. It accounts for factors that platform attribution ignores: seasonality, brand equity, competitor activity, and the lag between spend and conversion.
What does 98.1% accuracy mean in practice?
The 98.1% accuracy figure refers to the predictive accuracy of Bayesian models in forecasting customer behaviour across the channels in Zennith's client dataset. In practice, this means the model's predictions of which channels will exceed target ROAS are correct 98.1% of the time, compared to the industry average of roughly 60–70% for rule-based attribution models. This accuracy is what allows Zennith to make budget reallocation recommendations with documented confidence intervals rather than intuition.
What is a posterior distribution in marketing terms?
A posterior distribution is the model's output after combining prior knowledge (what we know about how this channel typically performs) with observed data (what we have actually measured). In marketing terms, it means the model gives each channel a probability of exceeding its target, not a single number, but a range with confidence levels. A channel with a posterior distribution showing 94% probability of exceeding target ROAS is a fundamentally different investment decision than one showing 45%.
Stop Guessing