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