Customer's time-variant purchase behavior and corresponding marketing strategies: an online retailer's case

The traditional customer relationship management (CRM) studies are mainly focused on CRM in a specific point of time. The static CRM and derived knowledge of customer behavior could help marketers to redirect marketing resources for profit gain at the given point in time. However, as time goes on, the static knowledge becomes obsolete. Therefore, application of CRM to an online retailer should be done dynamically in time. Though the concept of buying-behavior-based CRM was advanced several decades ago, virtually little application of the dynamic CRM has been reported to date.In this paper, we propose a dynamic CRM model utilizing data mining and a monitoring agent system to extract longitudinal knowledge from the customer data and to analyze customer behavior patterns over time for the retailer. Furthermore, we show that longitudinal CRM could be usefully applied to solve several managerial problems, which any retailer may face.

[1]  Paul D. Berger,et al.  Direct Marketing Management , 1989 .

[2]  Robert A. Peterson,et al.  Customer Base Analysis: An Industrial Purchase Process Application , 1994 .

[3]  Peter E. Rossi,et al.  The Value of Purchase History Data in Target Marketing , 1996 .

[4]  Thomas G. Dietterich,et al.  Readings in Machine Learning , 1991 .

[5]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[6]  Wagner A. Kamakura,et al.  Understanding Brand Competition Using Micro and Macro Scanner Data , 1994 .

[7]  Don Peppers,et al.  The One to One Future: Building Relationships One Customer at a Time (Будущее персонализации: построение взаимоотношений с одним клиентом) , 1993 .

[8]  J. Peppard.  Customer Relationship Management (CRM) in Financial Services , 2000 .

[9]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[10]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[11]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[12]  Frederick S. Hillier,et al.  Introduction of Operations Research , 1967 .

[13]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[14]  John Riedl,et al.  Ganging up on Information Overload , 1998, Computer.

[15]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[16]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[17]  Philip S. Yu,et al.  Horting hatches an egg: a new graph-theoretic approach to collaborative filtering , 1999, KDD '99.

[18]  D. Peppers,et al.  Enterprise One to One: Tools for Competing in the Interactive Age , 1996 .

[19]  Sung Ho Ha,et al.  Application of data mining tools to hotel data mart on the Intranet for database marketing , 1998 .

[20]  D. Peppers,et al.  Is your company ready for one-to-one marketing? , 1999, Harvard business review.