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WIRTSCHAFTSINFORMATIK

, Volume 52, Issue 2, pp 79–93 | Cite as

Unterstützung kundenbezogener Entscheidungsprobleme

Eine Analyse zum Potenzial moderner Klassifikationsverfahren
  • Stefan LessmannEmail author
  • Stefan Voß
Aufsatz
  • 517 Downloads

Zusammenfassung

Die Klassifikation repräsentiert ein wichtiges Instrument zur Unterstützung kundenbezogener Planungs- und Entscheidungsprobleme. Hierzu zählen z. B. die Prognose von Abwanderungswahrscheinlichkeiten im Vertragskundengeschäft oder die Abgrenzung einer geeigneten Zielgruppe für Marketingkampagnen. Während die Entwicklung neuer Klassifikationsverfahren ein populäres Forschungsfeld repräsentiert, werden entsprechende Neuerungen in der betrieblichen Praxis bisher nur selten eingesetzt. Diese Divergenz zwischen wissenschaftlichen und praktischen Interessen lässt sich z. T. dadurch erklären, dass das Potenzial moderner Klassifikationsverfahren in diesem Anwendungskontext noch nicht hinreichend geklärt ist. Die vorliegende Arbeit möchte einen Beitrag zur Schließung dieser Erkenntnislücke liefern. Hierzu wird eine empirische Studie durchgeführt, in deren Rahmen eine große Zahl etablierter wie neuer Klassifikationsverfahren verglichen wird. Eine Bewertung erfolgt anhand der Kosten bzw. Erträge, welche sich aus dem Einsatz einer bestimmten Methode in einer konkreten Entscheidungssituation ergeben. Die Untersuchung zeigt, dass eine stärkere Berücksichtigung moderner Methoden durchaus empfohlen werden kann und diese unter verschiedenen Bedingungen einen ökonomischen Mehrwert bieten.

Schlüsselwörter

Data Mining Kundenbeziehungsmanagement Entscheidungsunterstützung Klassifikation 

Customer-Centric Decision Support

Abstract

Classification analysis is an important tool to support decision making in customer-centric applications like, e.g., proactively identifying churners or selecting responsive customers for direct-marketing campaigns. Whereas the development of novel algorithms is a popular avenue for research, corresponding advancements are rarely adopted in corporate practice. This lack of diffusion may be explained by a high degree of uncertainty regarding the superiority of novel classifiers over well established counterparts in customer-centric settings. To overcome this obstacle, an empirical study is undertaken to assess the ability of several novel as well as traditional classifiers to form accurate predictions and effectively support decision making. The results provide strong evidence for the appropriateness of novel methods and indicate that they offer economic benefits under a variety of conditions. Therefore, an increase in use of respective procedures can be recommended.

Keywords

Data mining Customer relationship management Decision support Classification models 

Supplementary material

11576_2010_216_MOESM1_ESM.pdf (87 kb)
Modellselektion. (PDF 87 KB)
11576_2010_216_MOESM2_ESM.pdf (76 kb)
Vorgehensweise zur monetären Bewertung alternativer Klassifikatoren. (PDF 78 KB)

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Copyright information

© Gabler Verlag 2010

Authors and Affiliations

  1. 1.Institut für WirtschaftsinformatikUniversität HamburgHamburgDeutschland

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