When you need more than response models

Today’s sophisticated analytical approaches have proven to deliver increasingly better results. But does uplift modelling predict different things from non-uplift models?

It’s perhaps easier to say when they predict the same thing. This is usually when there is essentially no behaviour in the control group. For example, if a set of people purchase product X after a mailing, but no one purchases it without the mailing, and uplift model should predict the same thing as a conventional response model. Their predictions are most different when the variation in the change in behaviour opposite from the variation in the underlying behaviour.

For example, suppose the background purchase pattern (the one you see if you don’t do anything) is that mostly men by product X, but the effect of a marketing action is to make more women buy it, but fewer men, even though still more men than women buy when treated. In this case, uplift models will make radically different different predictions from “response” models.

A response model will concentrate on the fact that more men buy (when treated) that women; but an uplift model will recognize that women’s purchases are increased by the treatment whereas men’s is suppressed.

 

This entry was posted on Monday, July 6th, 2009 at 5:59 pm and is filed under For Analytics and Statistics Professionals. You can follow any responses to this entry through the RSS 2.0 feed.

 

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