Cumulative Uplift
The incremental outcome accumulated as targeting goes deeper down an uplift ranking. Why it cannot be a sum over customers, and how to read where it flattens.
The incremental outcome accumulated as targeting goes deeper down an uplift ranking. Why it cannot be a sum over customers, and how to read where it flattens.
The per-unit output of an uplift model: a difference of two predicted probabilities, not a probability. What it supports, and what it cannot be checked against.
Uplift models estimate conditional treatment effects. Targeting also needs outcome values, action costs, constraints, and uncertainty.
The incremental effect within the top k percent by predicted uplift — the number that matters when the campaign volume is already fixed.
The difference between the outcome with the treatment and the outcome without it. Why a response model does not estimate it, and what the number is attached to.