The use of frontier estimation in direct marketing

Abstract A common problem in direct marketing is to identify which physicians are the best prospects for an intervention that would encourage them to prescribe a drug. The standard procedure is to measure how far their prescribing behavior falls short of the level predicted by a regression line. We suggest that a better approach is to determine how far they fall short of “best practice,” as measured by a frontier line. We discuss ways of measuring the frontier and apply the techniques to both simulated data and a live data set. The results show that frontier estimation is particularly valuable when the data are heteroscedastic, a relatively common situation.

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