What Is Markov Chain Attribution?
Markov chain attribution models the user journey as a Markov process: each touchpoint is a state, transitions between touchpoints have probabilities estimated from observed data, and conversion is an absorbing state. Channel credit is computed by the removal effect: simulate what would happen to the conversion rate if a given channel's state were removed from the chain. The drop in conversion rate is that channel's attributed credit.
Like Shapley, Markov chain attribution is an algorithmic alternative to rules-based MTA. The two methods often produce similar channel-level credit allocations and are sometimes used interchangeably in vendor messaging.
Why Markov Chain Attribution Matters
Markov chains capture sequence effects more naturally than Shapley. The order in which touchpoints appear matters in Markov; Shapley averages across orderings. For categories where touchpoint sequence is meaningful (search before retargeting before checkout), Markov can produce more interpretable credit allocations.
The method is widely used in enterprise attribution platforms and is the basis for several open-source implementations (the ChannelAttribution R package being the most common). It is a credible algorithmic alternative when Shapley's computational cost is prohibitive.
How Markov Chain Attribution Works
Estimate transition probabilities from observed user journeys: probability of moving from state A to state B, including conversion as a terminal state. Compute the baseline conversion rate of the chain. For each channel, remove it from the chain (or equivalently set its transition probabilities to redirect to the next state), recompute the conversion rate, and attribute the drop to that channel as its removal effect. Normalize across channels to get credit shares.
Limitations for AI Search
Like Shapley, Markov chain attribution operates only on the touchpoints in the observed journey data. AI assistant interactions are not in that data, so they receive zero credit by construction. The method is more sophisticated than rules-based MTA for tracked channels and does nothing for untracked channels.
The same caveat as Shapley applies: vendors sometimes present Markov-based attribution as if it addresses modern attribution gaps. It addresses within-tracked-channel rigor; it does not address the AI search blind spot. MMM remains the framework that can value AI search.
How Presenc AI Helps
Presenc AI provides the AI visibility signal that complements Markov chain attribution: use Markov for within-channel tactical optimization in paid digital, feed AI visibility into MMM for strategic cross-channel allocation, and reconcile. The two methods together close the gap that Markov alone cannot.