A Dual-Step Multi-Algorithm Approach for Churn Prediction in Pre-Paid Telecommunications Service Providers

Ali Tamaddoni Jahromi,Issar Akbari,Aram Akbarzadeh,Mehrad Moeini

doi:10.24212/2179-3565.2010v1i2a7

Ali Tamaddoni Jahromi, Issar Akbari + Show 2 more

Open Access

https://doi.org/10.24212/2179-3565.2010v1i2a7

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Abstract

Nowadays customer churn has become the main concern of companies which are active in different industries. Among all industries which suffer from this issue, telecommunications industry can be considered in the top of the list with approximate annual churn rate of 30%. Dealing with this problem, there exist different approaches via developing predictive models for customer churn but due to the nature of pre-paid mobile telephony market which is not contract-based, customer churn is not easily traceable and definable, thus constructing a predictive model would be of high complexity. Handling this issue, in this study, we developed a dual-step model building approach, which consists of clustering phase and classification phase. With this regard firstly, the customer base was divided into four clusters, based on their RFM related features, with the aim of extracting a logical definition of churn, and secondly, based on the churn definitions that were extracted in the first step, different algorithms were utilized with the intention of constructing predictive models for churn in our developed clusters. Evaluating and comparing the performance of the employed algorithms based on “gain measure”, we concluded that employing a multi-algorithm approach in the model constructing step, instead of single-algorithm one, can bring the maximum gain among the tested algorithms.

Highlights

Nowadays customer churn has become the main concern of firms in all industries (Neslin, Gupta, Kamakura, Lu, & Mason, 2006), and companies, regardless of the industry that they are active in, are dealing with this issue
Considering the churn rate of different industries, one can find that the telecommunications industry is one of the main targets of this hazard such that the churn rate in this industry ranges from 20 to 40 annually (Berson, Smith, & Therling, 1999; Madden, Savage, & Coble-Neal, 1999)
The study at your disposal aims at developing a predictive model for customer churn in pre-paid mobile telephony companies. With this regard the first step is to give a sensible definition for churn in such companies and afterwards construct the predictive model based on the extracted definition

Summary

Introduction

Nowadays customer churn has become the main concern of firms in all industries (Neslin, Gupta, Kamakura, Lu, & Mason, 2006), and companies, regardless of the industry that they are active in, are dealing with this issue. Targeted proactive programs have the potential advantages of having lower incentive costs, because the incentive may not have to be as high as when the customer has to be ‘‘bribed’’ not to leave at the last minute This system would be wasteful if churn prediction is inaccurate, because companies are wasting incentive money on customers who would have stayed anyway, this threat elucidates the need for an accurate model for churn prediction (Coussement & Van den Poel, 2008b; Neslin, Gupta, Kamakura, Lu, & Mason, 2006). This model has to be able to recognize the customers which tend to churn in close future. With this regard the first step is to give a sensible definition for churn in such companies and afterwards construct the predictive model based on the extracted definition

- Literature Review

Findings

- Methodology and Result