An intelligent system for customer targeting: a data mining approach

Kim, YongSeog; Street, W. Nick

doi:10.1016/s0167-9236(03)00008-3

Cited by 145 publications

(46 citation statements)

References 14 publications

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“…Dennis et al (2001) proposed customer knowledge management framework based on K-means. Kim and Street (2004) proposed a system for customer targeting based on ANN and genetic algorithm. Kim (2006) used logistic regression and ANN for feature selection to predict churn.…”

Section: Data Mining For Customer Relationship Managementmentioning

confidence: 99%

“…DT and ANN are data mining approaches that have been heavily used for classification and/or prediction in support of marketing decision marking, and have shown good performance (Chien and Chen 2008;Kim et al 2006;Yu et al 2005;Kim and Street 2004;Kim 2006). As an alternative to a single classifier approach, bagging has been considered in the recent years (Frosyniotis et al 2003;Kang and Doermann, 2003;Roli et al 2004).…”

Section: Vip Classificationmentioning

confidence: 99%

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CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns

Shim

Choi

Suh

2012

Expert Systems with Applications

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Section: Data Mining For Customer Relationship Managementmentioning

confidence: 99%

Section: Vip Classificationmentioning

confidence: 99%

CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns

Shim

Choi

Suh

2012

Expert Systems with Applications

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“…A GA based intelligent system approach was found to be efficiently used in customer targeting (Kim & Street, 2004). The back propagation neural network based application deployed in Cape Metropolitan Tourism data has been shown to track the changing behavior of tourists within and between segments (Bloom, 2005).…”

Section: Classification Framework -Ann Dimensionmentioning

confidence: 99%

Application of neural network in market segmentation: A review on recent trends

Chattopadhyay¹,

Dan²,

Majumdar³

et al. 2012

MSL

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Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.

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“…Data mining is the result of applying sophisticated modeling techniques from the diverse fields of statistics, artificial intelligence, and database management (Yuantao & Siqin, 2008;Han & Kambert, 2001). Data mining has been widely used to determine marketing trend (Kaefer et al, 2005), customer detection (Kim & Nick Street, 2004), fraud detection (Farvares & Sepehri, 2010), etc.…”

Section: Introductionmentioning

confidence: 99%

A hybrid grey based K-means and feature selection for bank evaluation

Emami¹,

Razi²

2014

10.5267/j.dsl

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Performance measurement plays essential role on improving the performance of business units and their efficiencies. During the past few years, there have been tremendous development in banking systems and the primary focus of many managers is to improve the quality of services for market retention. Performance measurement in banking industry is normally involved with various qualitative as well as quantitative criteria, which leads to the implementation of multiple criteria decision making techniques. This paper presents a hybrid grey relational analysis and K-means to cluster and measure the performance of banking system. The proposed study uses different criteria, clusters banks into various segments and ranks 43 different banks in city of Semnan, Iran.

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An intelligent system for customer targeting: a data mining approach

Cited by 145 publications

References 14 publications

CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns

CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns

Application of neural network in market segmentation: A review on recent trends

A hybrid grey based K-means and feature selection for bank evaluation

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