Estimating campaign benefits and modeling lift

Abstract

In assessing the potential of data mining based marketing campaigns one needs to estimate the payoff of applying modeling to the problem of predicting behavior of some target population (e.g. attriters, people likely to buy product X, people likely to default on a loan, etc). This assessment has two components: a) the financial estimate of the campaign profitability, based on cost/benefit analysis and b) estimation of model accuracy in the targeted population using measures such as lift. We present a methodology for initial cost/benefit analysis and present surprising empirical results, based on actual business data from several domains, on achievable model accuracy. We conjecture that lift at T (where T is the target frequency) is usually about sqrt(1/T) for a good model. We also present formulae for estimating the entire lift curve and estimating expected profits.