What does it cost a model to delete a channel from every customer journey?

What does it cost a model to delete a channel from every customer journey?

A precise question that data-driven attribution can answer: if a touchpoint vanished, how many conversions would the path have lost?

The method. Markov-chain attribution treats the customer journey as a sequence of states with transition probabilities — the chance of moving from "saw display" to "clicked search" to "converted." A Markov model is one where the next step depends only on the current state. Its key output is the "removal effect": you delete a channel from the graph, recompute the probability of reaching conversion, and the drop is that channel's credit.

A documented application. In a frequently published e-commerce walkthrough using the R ChannelAttribution package on a real path dataset, last-click assigned a large share of credit to paid search and almost none to early display and social. The Markov removal-effect reallocation cut paid search's credit by roughly 25-30% and roughly doubled the credit assigned to upper-funnel display — because removing display from the graph measurably lowered the conversion probability of paths that began there.

The nuance. Removal effect captures structural importance within observed journeys, not causal lift. A channel can be load-bearing in the graph yet still non-incremental if those journeys would have completed anyway. Markov fixes last-click's order bias; it does not replace an experiment.

Bottom line for practitioners: use Markov removal effects to rebalance credit away from the last click and surface assist channels last-click hides — then validate the upper-funnel channels it elevates with a holdout before committing budget. The reallocation is a better map; it is still not the territory.