9.3
Attribution Models
Rules that assign credit for a conversion across the touchpoints that preceded it.
No single originator; data-driven attribution uses Shapley value (1953), Markov-chain from Anderl et al. (2016)
What it does
Distributes conversion credit across touchpoints: last click, linear, time decay, data-driven.
When it breaks
Observational, not causal. Benchmarked against experiments it systematically inflates cheap and search channels. It cannot substitute for an incrementality test, and it constantly is.
Case
Lewis, Rao and Reiley ran a placebo-ad experiment on Yahoo!: users shown an unrelated charity ad were compared with unexposed users, and the observational method attributed a large effect to an ad that could not have caused anything. The cause is activity bias: active people both see ads and convert. Last-click and most multi-touch models cannot remove it.
Lewis, Rao & Reiley · correlated online behaviours ↗In the wild
Unchecked · not part of the tier
What people have written about this tool in the last twelve months. A machine found these and nobody has read them. Everything above this line was checked by hand.
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