Customer acquisition and retention is a significant challenge in all industries. In the Telecom industry it affects profitability of the company if a customer churns before the company can earn back the investment it incurred in acquiring the customer. Therefore, it is very critical to identify the profitable customers and retain them.
With the telecom market becoming more competitive, determining the reasons of the customer leaving the service of the company is increasingly difficult. In this circumstance, it is even more difficult to predict the probability of the customer to leave in near future. It is increasingly challenging to devise a cost-effect incentive to target the right customer to convince him to stay with the company.
Predictive modeling of churn analysis and management aims at generating scores depicting the probability of the customers to churn out in future. This takes into consideration different aspects of customer's susceptibility to churn, including the history of people those who have churned in the past and build a data model that generates an easy-to-understand reference numbers (scores) assigned to each customers. These customers are then targeted with incentives to deter their cancellation. In other words, Churn analysis determines the probable reasons for a future cancellation depending on the past records which will help the companies to customize their offer. For example: if analysis reveals that many customers have churned from a particular area last month and further investigation has identified that there are frequent call drops (disruptions in service) in that exchange (or BTS area). It can be concluded that due to the technical inadequacy of that particular exchange, frequent call drops are experienced which has contributed to the customer dissatisfaction and their moving out of the company. So further technical solution for that exchange can prevent future potential churns.