Customer Churn Analysis in Telecom Industry
Project information
- Category: Exploratory Data Analysis
- Industry: Telecommunications
- Data/Study based on: Kaggle
- Project date: 4 August, 2024
- Project URL: Github Link
- Skills: Python | Data Visualization | Statistical Analysis | Feature Engineering | Domain Research | Pandas | Matplotlib | Seaborn | Scikit-learn
Identified key drivers of customer churn in the telecom industry and proposed actionable strategies for retention.
Analyzed customer data to uncover a 25% churn rate, with short tenure and high monthly charges as critical predictors of churn.
Key Findings: Churn is driven by pricing concerns, competition, and dissatisfaction with customer support. Addressing these issues can significantly enhance retention and customer satisfaction.
Recommendations: Suggested early engagement strategies, pricing model revisions, and service improvements, such as incentivizing long-term contracts and enhancing customer support quality.