PREPAID TELECOM CUSTOMERS SEGMENTATION USING THE K-MEAN ALGORITHM

Mihai Bacila (), Radulescu Adrian () and Marar Liviu Ioan ()
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Radulescu Adrian: Business Logic Systems Ltd,
Marar Liviu Ioan: Business Logic Systems Ltd,

Annals of Faculty of Economics, 2012, vol. 1, issue 1, 1112-1118

Abstract: The scope of relationship marketing is to retain customers and win their loyalty. This can be achieved if the companiesâ€(tm) products and services are developed and sold considering customersâ€(tm) demands. Fulfilling customersâ€(tm) demands, taken as the starting point of relationship marketing, can be obtained by acknowledging that the customersâ€(tm) needs and wishes are heterogeneous. The segmentation of the customersâ€(tm) base allows operators to overcome this because it illustrates the whole heterogeneous market as the sum of smaller homogeneous markets. The concept of segmentation relies on the high probability of persons grouped into segments based on common demands and behaviours to have a similar response to marketing strategies. This article focuses on the segmentation of a telecom customer base according to specific and noticeable criteria of a certain service. Although the segmentation concept is widely approached in professional literature, articles on the segmentation of a telecom customer base are very scarce, due to the strategic nature of this information. Market segmentation is carried out based on how customers spent their money on credit recharging, on making calls, on sending SMS and on Internet navigation. The method used for customer segmentation is the K-mean cluster analysis. To assess the internal cohesion of the clusters we employed the average sum of squares error indicator, and to determine the differences among the clusters we used the ANOVA and the post-hoc Tukey tests. The analyses revealed seven customer segments with different features and behaviours. The results enable the telecom company to conceive marketing strategies and planning which lead to better understanding of its customersâ€(tm) needs and ultimately to a more efficient relationship with the subscribers and enhanced customer satisfaction. At the same time, the results enable the description and characterization of expenditure patterns for services that are continuously growing. Also, the study demonstrates this analysis model is efficient for a large customer base.

Keywords: market segmentation; profiling segments; telecommunication services; k-mean cluster; relationship marketing (search for similar items in EconPapers)
JEL-codes: M31 (search for similar items in EconPapers)
Date: 2012
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