Churn Prediction in Telecom Industry: Identifying High-Risk Customers and Key Indicators

The telecom industry faces significant challenges related to customer churn, where customers switch between service providers. It can cost 5-10 times more to acquire a new customer than to retain an existing one, and the telecom industry experiences an average churn rate of 15-22% per year. In Africa market, approximately 80% of revenue comes from the top 20% of customers, making it crucial to reduce churn among high-value customers. In this project, we will use customer-level data to build predictive models for identifying high-churn-risk customers and the main indicators of churn, ultimately aiming to reduce churn and retain valuable customers.

In Ghana, young data science enthusiasts lack the opportunity to work on real-world industry datasets that involve critical problems like churn prediction in the telecom industry. This creates a gap in their skill set and hinders their ability to gain practical experience, which is essential for success in the field. As a result, it is challenging for them to secure jobs in the industry and contribute to the development of the country's technology sector. Therefore, this project aims to provide a practical learning opportunity for young data science enthusiasts in Ghana by tackling the critical problem of churn prediction in the telecom industry.

The Omdena Accra Chapter team is working on a project to predict customer churn for high-value customers in the telecom industry. Our goal is to develop a predictive model that can accurately identify customers who are at risk of leaving so that corrective actions can be taken to retain them. We will focus on high-value customers and use a usage-based definition of churn to predict churn for these customers based on a certain metric.

Data Analysis, Machine Learning, Teamwork, Problem Solving, Industrial Experience