A first-order Markov chain with removal effects—the math behind advanced attribution.

A Markov chain is a system that moves between states, where the probability of the next state depends only on the current state—not on how you got there. This is the Markov property (memorylessness).

Transition Matrix
Every Markov chain is defined by a transition matrix. Each row represents a “from” state, each column a “to” state. The values are probabilities, so every row sums to 1.0.

In attribution, the states are marketing channels (Display, Search, Email, etc.) plus two absorbing states: Conversion and No Conversion. Once you enter an absorbing state, you stay there forever—the journey is over.

Absorbing States
An absorbing Markov chain has at least one state you can never leave. Conversion and No Conversion are absorbing: once a customer converts (or drops off), the journey ends. The math question becomes: what is the probability of being absorbed into Conversion vs. No Conversion?

Removal effects are the key insight. For each channel, we ask: “What happens to the overall conversion rate if we remove this channel entirely?”

If removing Search drops conversions from 70% to 30%, Search has a large removal effect. If removing Display only drops conversions from 70% to 65%, Display is less critical. We normalize these effects so they sum to 100%, giving each channel its attribution share.

The Formula
Removal Effect(channel) = (Baseline conversion − Conversion without channel) / Baseline conversion

Attribution(channel) = Removal Effect(channel) / Sum of all removal effects

Unlike last-click or first-click models, Markov chain attribution captures interdependencies between channels. A channel that rarely gets the last click but frequently assists conversions will still receive appropriate credit.

Why This Matters
Traditional attribution models are deterministic rules. Markov chains are probabilistic—they model the actual paths customers take and compute how much each channel contributes to the probability of conversion. This makes budget decisions less arbitrary and more defensible.

1 Customer Journeys

Enter customer journeys using the format: Channel → Channel → Conversion ($value) or No Conversion

Channels: D (Display), F (Facebook), S (Search), E (Email)