To establish online shoppers' markets and rules for dynamic CRM systems: an empirical case study in Taiwan

Purpose – The purpose of this paper is to establish customers’ markets and rules of dynamic customer relationship management (CRM) systems for online retailers.Design/methodology/approach – This research proposes a procedure to discover customers’ markets and rules, which adopts the recency, frequency, monetary value (RFM) variables, transaction records, and socioeconomic data of the online shoppers to be the research variables. The research methods aim at the supervised apriori algorithm, C5.0 decision tree algorithm, and RFM model.Findings – This research discovered eight RFM markets and six rules of online retailers.Practical implications – The proposed framework and research results can help retailer managers to retain and expand high value markets via their dynamic CRM and POS systems.Originality/value – This research uses data mining technologies to extract high value markets and rules for marketing plans. The research variables are easy to obtain via retailers’ systems. The found customer values, R...

[1]  J. Miglautsch Thoughts on RFM scoring , 2000 .

[2]  Shu-Hsien Liao,et al.  Ontology-based data mining approach implemented for sport marketing , 2009, Expert Syst. Appl..

[3]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[4]  Irini Rigopoulou,et al.  Virtual Store Layout Effects on Consumer Behaviour: Applying an Environmental Psychology Approach in the Online Travel Industry , 2011, Internet Res..

[5]  Eldon Y. Li,et al.  The effect of channel quality inconsistency on the association between e-service quality and customer relationships , 2011, Internet Res..

[6]  Arthur Middleton Hughes,et al.  Strategic database marketing , 2005 .

[7]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[8]  John A. McCarty,et al.  SEGMENTATION APPROACHES IN DATA MINING: A COMPARISON OF RFM, CHAID, AND LOGISTIC REGRESSION , 2007 .

[9]  Sulin Pang,et al.  C5.0 Classification Algorithm and Application on Individual Credit Evaluation of Banks , 2009 .

[10]  Young-Gul Kim,et al.  A framework of dynamic CRM: linking marketing with information strategy , 2003, Bus. Process. Manag. J..

[11]  K. Perreault,et al.  Research Design: Qualitative, Quantitative, and Mixed Methods Approaches , 2011 .

[12]  Wen-Yu Chiang Establishment and application of fuzzy decision rules: an empirical case of the air passenger market in Taiwan , 2011 .

[13]  Yen-Liang Chen,et al.  Discovering recency, frequency, and monetary (RFM) sequential patterns from customers' purchasing data , 2009, Electron. Commer. Res. Appl..

[14]  Wen-Yu Chiang,et al.  To mine association rules of customer values via a data mining procedure with improved model: An empirical case study , 2011, Expert Syst. Appl..

[15]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[16]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[17]  G. Linoff,et al.  Mining the Web: Transforming Customer Data into Customer Value , 2002 .

[18]  Mevlut Ture,et al.  Using Kaplan-Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4.5 and ID3) in determining recurrence-free survival of breast cancer patients , 2009, Expert Syst. Appl..

[19]  Cataldo Zuccaro Classification and prediction in customer scoring , 2010 .

[20]  Hsiao-Ping Tsai,et al.  Group RFM analysis as a novel framework to discover better customer consumption behavior , 2011, Expert Syst. Appl..

[21]  Yang-Chieh Chin,et al.  Comparing consumer complaint responses to online and offline environment , 2011, Internet Res..

[22]  Mohammad Jafar Tarokh,et al.  Estimating customer future value of different customer segments based on adapted RFM model in retail banking context , 2011, WCIT.

[23]  Ali Buldu,et al.  Data mining application on students’ data , 2010 .

[24]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

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

[26]  A. K. Pujari,et al.  Data Mining Techniques , 2006 .