Performance-based variable selection for scoring models

Edward C. Malthouse*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

The performance of a direct marketing scoring model at a particular mailing depth, d, is usually measured by the total amount of revenue generated by sending an offer to the customers with the 100d% largest scores (predicted values). Commonly used variable selection algorithms optimize some function of model fit (squared difference between training and predicted values). This article (1) discusses issues involved in selecting a mailing depth, d, and (2) proposes a variable selection algorithm that optimizes the performance as the primary objective. The relationship between fit and performance is discussed. The performance-based algorithm is compared with fit-based algorithms using two real direct marketing data sets. These experimental results indicate that performance-based variable selection is 3-4% better than corresponding fit-based models, on average, when the mailing depth is between 20% and 40%.

Original languageEnglish (US)
Pages (from-to)37-50
Number of pages14
JournalJournal of Interactive Marketing
Volume16
Issue number4
DOIs
StatePublished - Jan 1 2002

ASJC Scopus subject areas

  • Business and International Management
  • Marketing

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