Integration of Machine Learning Techniques to Evaluate Dynamic Customer Segmentation Analysis for Mobile Customers
This work addresses customer segmentation for telecommunications companies, but it is incremental as it applies existing methods to a specific domain.
The paper tackled customer segmentation for mobile providers by analyzing the C.5 algorithm within naive Bayesian modeling to segment customers based on billing and socio-demographic data, with results experimentally implemented but no concrete numbers provided.
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.