Integration of Machine Learning Techniques to Evaluate Dynamic Customer Segmentation Analysis for Mobile Customers

TL;DR

This paper explores the use of machine learning, specifically the C.5 algorithm within naive Bayesian models, to improve customer segmentation in the telecom industry for better marketing and churn reduction.

Contribution

It provides a detailed analysis and experimental evaluation of the C.5 algorithm for behavioral customer segmentation based on billing and socio-demographic data.

Findings

Effective segmentation of telecom customers achieved

Improved targeting for marketing strategies

Potential reduction in customer churn

Abstract

The telecommunications industry is highly competitive, which means that the mobile providers need a business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal level of cost in marketing activities. Machine learning applications can be used to provide guidance on marketing strategies. Furthermore, data mining techniques can be used in the process of customer segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling according to their billing and socio-demographic aspects. Results have been experimentally implemented.