Multi-Touch Attribution

Multi-touch splits credit across the touches in a journey using a rule: linear gives every touch an equal share, time-decay weights the touches nearest the conversion, and position-based models typically give the first and last touch a large fixed share with the middle splitting the remainder. Data-driven models derive the weights statistically instead of fixing them by hand, which requires far more conversions than most affiliate programs have.

It is common in analytics and rare in affiliate payouts, and the reason is arithmetic rather than philosophy. Splitting a 40 dollar commission four ways produces payments small enough to sit under every payout threshold in the program, and a single refund then has to be clawed back across four partners.

Where a program does run multi-touch, discovery content stops earning zero and starts earning a fraction — which is usually a large improvement on last-click for anyone producing reviews or tutorials.

The catch is predictability. Under last-click you know whether you won; under a weighted model your share depends on touches by other partners that you cannot see, which makes it much harder to judge whether a piece of content was worth making.

Multi-touch is most useful to a brand as a measurement lens even when payouts stay last-click. Running it in reporting alone shows which partners appear early in journeys that convert, and that is the list worth protecting when budget moves.

Paying on it is a bigger commitment: thresholds, clawbacks, partner statements and dispute handling all get materially harder, and partners generally dislike a model whose output they cannot reconstruct.