Turma 8

📈 Customer Churn Analysis part 1

🎯 Main goal: This project performs an in-depth exploratory data analysis (EDA) on a telecom company's customer dataset. The primary objective is to identify the key drivers of customer churn and transform these insights into actionable business strategies to improve customer retention.

📋 Objectives

• Clean and Prepare Data: Process raw, nested JSON data into a clean, usable format.
• Identify Churn Drivers: Analyze how different factors—such as tenure, contract type, services, and demographics—impact customer churn.
• Visualize Findings: Create clear and effective visualizations to communicate the main insights.
• Propose Strategies: Formulate data-driven recommendations for the business to reduce customer churn.

📊 Key Findings

The analysis successfully identified several key factors that strongly correlate with a customer's likelihood to churn:

• Overall 26% of clients has churned.

• Dropouts occur mostly in the firt 12 months.

• Contract & Tenure are Decisive: The churn rate is highest for customers on month-to-month contracts and within their first 12 months of service. Customer loyalty increases significantly with longer tenure.

• Payment Method Influence: Customers paying via Electronic Check show a notably higher tendency to churn compared to those using automatic payment methods.

• High-Risk Demographics: Senior citizens (65 years old or more) exhibit a significantly higher churn rate compared to younger customers.

💡 Strategic Recommendations

Based on the findings, the following actions are recommended to reduce customer churn:

• Encourage annual contracts:

  Offer discounts or benefits (a free month, higher connection speed, technical support, etc.) for customers on monthly plans to switch to one or two-year contracts.

• Improve new customer onboarding:

  Create a dedicated program for customers in their first 3 to 6 months to ensure they have a positive experience and understand the value of the services. Offer "Adhesion" service packages such as Technical Support and Online Security, especially for new customers or those on monthly plans.

• Targeted Retention for Seniors:

  Develop specific communication or retention campaigns to meet the needs of senior citizens.

🛠️ Technologies Used

Python version 3

Pandas: For data manipulation and analysis.

Matplotlib & Seaborn: For data visualization.

Colab: As the development environment.

Project versions:

🔗 Colab:

Colab