An Intelligent Interactive Information Discovery Model for Churn Management in Telecommunications Industry

Customer churn is when a customer stops using products or services from a company. In order to maximize the revenue and profit, the companies harness a wide range of strategies to reduce customer churn rate. This study develops an artificial intelligence-powered interactive information discovery model that predicts a customer’s decision to churn through analyzing their behavioral data. This model uses artificial neural networks to predict churn probability for every customer based on their personal behavioral data, which includes features like gender, contract type, and service charges. The model is applied to a real -world customer churn problem from the telecommunications industry. The results indicate that contract type, service charges, and tenure are primary single feature drivers behind customer churn in this sector. They further illustrate the ability of the model in discovering and retrieving customers with a high churn probability and forming the foundation of an interactive information retrieval system for simulating the customer response to a set of alternative decision scenarios.

Keywords:

information discovery , churn , artificial intelligence , behavior analysis , telecommunications.