Subgroup evaluation and decision support for a direct mailing marketing problem

PA Flach, D Gamberger

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

In this work we use ROC (Receiver Operating Characteristic) analysis to evaluate customer subgroups detected by different machine learning approaches in a marketing database. A direct mailing model with a marginal cost per mailing and an average expected profit per new customer has been assumed. In order to identify optimal mailing strategies for different marketing situations, we introduce the normalised profit curve, which extends the ROC curve by not only identifying the optimal subgroup in a given context, but also indicating the expected profit. In this sense, the analysis presents a link between data mining and decision support.
Translated title of the contributionSubgroup evaluation and decision support for a direct mailing marketing problem
Original languageEnglish
Title of host publicationUnknown
EditorsChristophe Giraud-Carrier, Nada Lavrac, Steve Moyle
PublisherECML/PKDD'01 workshop notes
Pages45 - 56
Number of pages11
Publication statusPublished - Sep 2001

Bibliographical note

Conference Proceedings/Title of Journal: Integrating Aspects of Data Mining, Decision Support and Meta-Learning

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