Multi-touch attribution is an approach that splits credit for a sale across every touchpoint along the path. It has variants such as linear, time decay and U-shaped. Distributing credit instead of handing it to one channel brings channel comparison closer to reality; but because the choice of model is arbitrary, the result changes with the choice.
The linear model gives every touch an equal share, time decay weights the touches closest to the conversion, and the U-shaped model loads onto the first and last touch. There is no technical answer to which one is correct; it is chosen according to the length of the purchase cycle. In Turkey the real constraint is data: the path breaks when a user rejects cookies, returns on another device, or orders over WhatsApp. That is why multi-touch is used not for in-channel optimization but to ground the cross-channel budget argument in data.
The linear model gives every touch an equal share, time decay weights the touches closest to the conversion, and the U-shaped model loads onto the first and last touch. There is no technical answer to which one is correct; it is chosen according to the length of the purchase cycle. In Turkey the real constraint is data: the path breaks when a user rejects cookies, returns on another device, or orders over WhatsApp. That is why multi-touch is used not for in-channel optimization but to ground the cross-channel budget argument in data.
The linear model gives every touch an equal share, time decay weights the touches closest to the conversion, and the U-shaped model loads onto the first and last touch. There is no technical answer to which one is correct; it is chosen according to the length of the purchase cycle. In Turkey the real constraint is data…
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