Customer Segmentation and the Effectiveness of Personalized Service Offers in the GSM Sector in Pakistan Using Clustering Techniques

Abstract

In an intensely competitive GSM telecom industry, customer retention and provision of customized services are vital. This research employs K-Means clustering and Principal Component Analysis (PCA) to classify mobile subscribers in Pakistan according to their usage of voice, SMS, and data services. On the basis of anonymized customers' data from a leading telecom operator, trends were examined with regard to demographic and location variables. The clusters that were produced showed clear patterns of user behavior, which were matched with appropriate commercial propositions to enable personalized delivery of services. The findings emphasize the constraints on generalized offers and illustrate how segmentation using data can drive both customer satisfaction and business performance. The methodology that has been used can be applied by telecom companies in other emerging economies to enable focused marketing and product planning.