article

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

Authors:

Sang Chan ParkAuthors Info & Claims

Computers and Industrial Engineering, Volume 43, Issue 4

Pages 801 - 820

https://doi.org/10.1016/S0360-8352(02)00141-9

Published: 02 September 2002 Publication History

Abstract

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.

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

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cover image Computers and Industrial Engineering

Computers and Industrial Engineering Volume 43, Issue 4

September 2002

203 pages

ISSN:0360-8352

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Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 02 September 2002

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Cited By

Bai TZou LZhao WDu PLiu WNie JWen JPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)CTRecProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331199(675-684)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331199
García DNebot ÀVellido A(2017)Intelligent data analysis approaches to churn as a business problemKnowledge and Information Systems10.1007/s10115-016-0995-z51:3(719-774)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10115-016-0995-z
Yao ZSarlin PEklund TBack B(2014)Combining visual customer segmentation and response modelingNeural Computing and Applications10.1007/s00521-013-1454-325:1(123-134)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.1007/s00521-013-1454-3
Mazumdar BBasak SModanwal N(2011)Multi-Agent Negotiation Paradigm for Agent Selection in B2C E-CommerceInternational Journal of Agent Technologies and Systems10.5555/2815760.28157633:2(33-52)Online publication date: 1-Apr-2011
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Mazumdar BMishra R(2010)Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining MethodsInternational Journal of Intelligent Information Technologies10.4018/jiit.20101001046:4(46-70)Online publication date: 1-Oct-2010
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Mazumdar BMishra R(2010)Customer Orientation Based Multi-Agent Negotiation for B2C e-CommerceInternational Journal of Agent Technologies and Systems10.4018/jats.20100401032:2(24-48)Online publication date: 1-Apr-2010
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Piton TBlanchard JBriand HGuillet FCheung DSong IChu WHu XLin J(2009)Domain driven data mining to improve promotional campaign ROI and select marketing channelsProceedings of the 18th ACM conference on Information and knowledge management10.1145/1645953.1646088(1057-1066)Online publication date: 2-Nov-2009
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