Customer segmentation and cross-selling recommendation engine for the telecom industry : a machine learning approach

Abstract

In the telecommunication industry, personalized service delivery and effective crossselling strategies are vital for enhancing customer engagement and driving revenue growth. This research presents a data-driven approach to customer segmentation and personalized recommendation, aiming to promote the transition from Double-Play (Voice and Internet) to Triple-Play (Voice, Internet and IPTV) service adoption. The study applies unsupervised machine learning techniques, namely Gaussian Mixture Models (GMM) and K-Means clustering to segment customers based on demographic, billing, and service usage attributes. Multiple internal evaluation metrics, including the Silhouette Score, Calinski-Harabasz Index, Davies-Bouldin Index and the Elbow method are employed to determine the optimal clustering structure and validate the segmentation quality. To generate personalized service recommendations, a collaborative filtering approach based on Singular Value Decomposition (SVD) is utilized. The model predicts the most suitable IPTV package for Double-Play customers by learning from existing customer interaction patterns. The performance of the recommendation system is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The implementation leverages Python-based tools and libraries for data preprocessing, modeling and visualization. The research offers a practical and scalable framework for targeted marketing and intelligent service personalization, contributing to the advancement of data-driven strategies in the telecommunications domain.