Abstract

India, the second largest in telecommunication industry facing acute rise in mobile churn. Churn rate is very high in prepaid segment when compared with postpaid in India. Even though marketers are devoting huge investments on retention campaigning they could not arrest the churn rate. Many attractive promotional schemes and packages were offered to retain the prepaid customers as the cost of acquisition is very expensive. They were all found to be ineffective since churn rate is pungently alarming every day in prepaid scenario. Hence it is highly imperative to devise proactive retention strategies by percolating the operational churn factors and to design predictive model to stem out the churn rate in India. Thus this study focuses on the factors influencing churn in prepaid segment and conceptual predictive model using neural networks to enervate customer churn.

Highlights

  • The Indian telecommunications industry is one of the fastest proliferating sectors in the world

  • Customer churn happens to be the most challenging issue for mobile industry irrespective of its rapid growth. This in turn entangled with disloyalty and as the industry saturates it become imperative for the mobile operators to redesign service plans with new offerings to enhance customer loyalty

  • Knowledge of churn rate will enable the mobile operators to design and implement strategies to achieve a higher rate of customer retention

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Summary

Introduction

The Indian telecommunications industry is one of the fastest proliferating sectors in the world. Customer churn happens to be the most challenging issue for mobile industry irrespective of its rapid growth This in turn entangled with disloyalty and as the industry saturates it become imperative for the mobile operators to redesign service plans with new offerings to enhance customer loyalty. Prepaid subscribers represent the overwhelming customer majority for many mobile operators across the world. Rafi kretchmer, (2009), pointed out the reasons of customers spending, especially if they are on a tight budget This explains the prevalence of prepaid services in emerging economies. A multi-stage research procedure utilizing such real-world data is proposed It allows the identification of significant churn factors, the segmentation of customers, and the establishing of a rule model of the phenomenon for each customer segment. Junxiang Lu (1995), explained about predicting customer churn in the telecommunications industry, applying survival analysis to predict customer churn using data mining techniques

Problem Focus
Justification for Study
Data Collection
Sampling Framework
Sample size determination
Validity
Normality
Statistical Tool
Artificial Neural Network Model
Multilayer Perceptron Network Model
Model Output Using Testing Sample
Standardized Identity Sum of squares
Conclusions and Recommendations
Campaign management
Social media marketing
Summary
Findings
10. Usefulness of Study

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