Metodos De Segmentacion

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Expert Systems with Applications
Expert Systems with Applications 34 (2008) 2754–2762 www.elsevier.com/locate/eswa

Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer
Chu Chai Henry Chan
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E-Business Research Laboratory, Department of Industrial Engineering and Management, ChaoyangUniversity of Technology, No. 168, Jifong East Road, Wufong Township, Taichung County, Taiwan, ROC

Abstract Most marketers have difficulty in identifying the right customers to engage in successful campaigns. So far, customer segmentation is a popular method that is used for selecting appropriate customers for a launch campaign. Unfortunately, the link between customer segmentation and marketingcampaign is missing. Another problem is that database marketers generally use different models to conduct customer segmentation and customer targeting. This study presents a novel approach that combines customer targeting and customer segmentation for campaign strategies. This investigation identifies customer behavior using a recency, frequency and monetary (RFM) model and then uses a customer lifetime value (LTV) model to evaluate proposed segmented customers. Additionally, this work proposes using generic algorithm (GA) to select more appropriate customers for each campaign strategy. To demonstrate the efficiency of the proposed method, this work performs an empirical study of a Nissan automobile retailer to segment over 4000 customers. The experimental results demonstrate that the proposedmethod can more effectively target valuable customers than random selection. Ó 2007 Published by Elsevier Ltd.
Keywords: Customer segmentation; Generic algorithm; Customer lifetime value; Campaign management

1. Introduction For a successful business, engaging in an effective campaign is a key task for marketers. Traditionally, marketers must first identify market segmentation using a mathematicalmode and then implement an efficient campaign plan to target profitable customers (Fraley & Thearting, 1999). This process confronts considerable problems. First, most previous studies used various mathematical models to segment customers without considering the correlation between customer segmentation and a campaign. Previously, the link between customer segmentation and campaign activities wasmost manual or missing (Fraley & Thearting, 1999). From an academic perspective, the processes of customer segmentation should consider the constraints or dependent variables of campaign activities in

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Tel.: +886 423323000x4251; fax: +86 4 23742327. E-mail address: ccchan@cyut.edu.tw

attempting to increase the relevancy of both processes. For marketing researchers, segmentation should notthe end in itself, but rather a means to an end (Jonker, Piersma, & Poel, 2004). Following the notion proposed by Jonker, this work presents a conceptual model by counting the significant campaign dependent variables of customer targeting in customer segmentation. In this way, the processes of customer segmentation and targeting thus can be linked and solved together. The outcomes of customersegmentation of this study are more meaningful and useful for marketers than the others. Second, in most previous studies, the quality of a segmentation methodology is measured based on within-segment and inter-segment heterogeneity (Wedel & Kamakura, 2000). Realistically, marketers are concerned with and interested in maximizing the net value of targeted customers, rather than caring aboutwithin-segment homogeneity or targeting rate (Jonker et al., 2004; Kim, Street, Russell, & Menczer, 2005). To solve the core problem of marketers facing, this investigation applies a customer life time value

0957-4174/$ - see front matter Ó 2007 Published by Elsevier Ltd. doi:10.1016/j.eswa.2007.05.043

C.C.H. Chan / Expert Systems with Applications 34 (2008) 2754–2762

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