Markov Chain Attribution Models Are Transparent and Auditable

For transparent multi-touch attribution, a Markov chain model treats each marketing channel as a state and computes transition probabilities from actual user journey data. The primary benefit of this approach is auditability; stakeholders can trace the calculations step-by-step.

Calculate removal effects: how does conversion probability change when you remove each channel from the graph? The resulting attribution weights are mathematically derived from your data, fully explainable, and reproducible.

The transition matrix also reveals path patterns that flat attribution reports never surface — which channels feed each other, which channels are terminal dead-ends, and where the conversion-critical handoffs occur.

Implementation in BigQuery: build a user journey table with ordered channel touchpoints per user, adding start, conversion and null (no conversion) states. Compute the transition matrix by pairing each touchpoint with the next one — LEAD() over the ordered journey — counting each transition and dividing by the total number of transitions out of the origin state. For removal effects, recompute the conversion probability with each channel removed, redirecting its incoming transitions to the null state. The relative drop from the baseline is that channel's removal effect; normalise the removal effects so they sum to one to get attribution weights.

Present the channel graph to stakeholders — visualise it with nodes as channels and edges weighted by transition probability. It's the most honest picture of how your marketing channels interact, and it gives media buyers actionable intelligence about channel synergies that flat attribution tables can never reveal.