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.