Data mining in market segmentation and tariff policy design: A telecommunication case

Abstract

A method for data mining by using K-means clustering analysis is employed to group consumers into segments by collecting historical data accumulated in business support systems of a telecommunication company. The study led to the identification of seven segments, each with a diverse combination of the segmentation variables. Findings provide implications for strategic choices to telecom operators. Validation of tariff policy, which is designed according to the target customer group, proves that successful marketing policy relies heavily on the accurate assignment of segment membership. © 2009 IEEE.