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.
Problem Research and Data Collection
Exploratory Data Analysis
Model Development and Training
Data Analysis, Machine Learning, Teamwork, Problem Solving, Industrial Experience