Multi-attribute scoring method for mobile telecommunication subscribers

Abstract The measurement of customer value has become a key issue for developing and maintaining successful customer relationship. To measure the value of each customer, it is necessary to consider not only the direct monetary term but also the potential customer value. In this paper, we propose a multi-attribute customer evaluation procedure for mobile telecommunication subscribers. At first, customers are clustered using a single linkage clustering method, then each segment is evaluated based on data envelopment analysis (DEA), which is frequently applied for multi-attribute evaluation problem. Finally, neural network model is used to evaluate each individual customer's value based on DEA results. The proposed method is applied to the mobile telecommunication subscriber data. It is expected that telecommunication companies can focus on target marketing to maximize the overall profit based on the efficiency score of customers.

[1]  Peter C. Verhoef,et al.  The commercial use of segmentation and predictive modeling techniques for database marketing in the Netherlands , 2003, Decis. Support Syst..

[2]  Rajiv D. Banker,et al.  Efficiency Analysis for Exogenously Fixed Inputs and Outputs , 1986, Oper. Res..

[3]  Euiho Suh,et al.  Customer list segmentation using the combined response model , 1999 .

[4]  Han Kook Hong,et al.  Evaluating the efficiency of system integration projects using data envelopment analysis (DEA) and machine learning , 1999 .

[5]  F. Dwyer,et al.  Developing Buyer-Seller Relationships: , 1987 .

[6]  Chih-Ping Wei,et al.  Turning telecommunications call details to churn prediction: a data mining approach , 2002, Expert Syst. Appl..

[7]  Francis J. Mulhern,et al.  Customer Profitability Analysis: Measurement, Concentration, and Research Directions , 1999 .

[8]  R. Mojena,et al.  Hierarchical Grouping Methods and Stopping Rules: An Evaluation , 1977, Comput. J..

[9]  Roy W Ralston,et al.  The effects of customer service, branding, and price on the perceived value of local telephone service , 2003 .

[10]  Katherine N. Lemon,et al.  The Customer Pyramid: Creating and Serving Profitable Customers , 2001 .

[11]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[12]  Wolfgang Ulaga.  Customer Value in Business Markets An Agenda for Inquiry , 2001 .

[13]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

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

[15]  William W. Cooper,et al.  Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through , 1981 .

[16]  Jagdip Singh,et al.  Boundary role ambiguity: Facets, determinants, and impacts. , 1993 .

[17]  R. J. Kuo,et al.  Cluster analysis in industrial market segmentation through artificial neural network , 2002 .

[18]  Zilla Sinuany-Stern,et al.  Academic departments efficiency via DEA , 1994, Comput. Oper. Res..

[19]  G. W. Milligan,et al.  An examination of procedures for determining the number of clusters in a data set , 1985 .

[20]  Peter C. Verhoef,et al.  Predicting customer potential value an application in the insurance industry , 2001, Decis. Support Syst..

[21]  T. J. Euverman,et al.  Modelling Customer Retention with Statistical Techniques, Rough Data Models and Genetic Programming , 1999 .

[22]  Wesley J. Johnston,et al.  Customer Profitability: Prospective vs. Retrospective Approaches in a Business-to-Business Setting☆ , 2001 .

[23]  Abraham Charnes,et al.  A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the U.S. air forces , 1984, Ann. Oper. Res..