📊 Churn Analysis in Telecommunications

📕 Table of Contents

• Problem Statement
• Objective
• Tools Used
• Dataset Overview
• Key Findings
• Recommendations
• Project Presentation
• Project Learnings
• Installation and Usage
• Dashboard

❓ Problem Statement

Customer churn significantly impacts revenue and profitability in the telecommunications industry. Understanding churn factors and identifying at-risk customers is crucial for designing effective retention strategies.

🎯 Objective

Analyze customer data to identify patterns and factors associated with churn, develop actionable insights to reduce churn rates, enhance customer retention, and increase overall customer satisfaction and lifetime value.

🛠️ Tools Used

• Analytical & Visual: Microsoft Excel
  
• Presentation: Microsoft Power Point
  

📅 Dataset Overview

• Data source: Internet
• Time period: 2015-2018
• Data size: dataset_telecom_customer_churn (7043, 43)
• Key columns: Payment Method, Married, Gender, Contract, Number of Referrals, Total Revenue, Offer, Churn category, Churned
• Calculated columns: churn%, age_range, senior_citizen, cltv
• Data set Link

🔎 Key Findings

• Churn rate: 26.54%
• Average customer lifetime value: $2,853.93
• Total referrals: 13,747
• Total revenue: $21.37M
• Average tenure: 32.39 months
• Average monthly bill: $63.78

Additional findings include higher churn rates among senior citizens, non-married customers, and those without offers. Competitor-related churn accounts for nearly half of customer losses.

💡 Recommendations

1. Enhance retention strategies for high-churn segments
2. Promote secure and convenient payment methods
3. Expand promotional offers
4. Optimize offer-based strategies
5. Encourage longer-term contract commitments
6. Address disparities in offers for specific demographics
7. Enhance competitive positioning
8. Leverage active referral demographics

📌 Project Presentation

Slides

The detailed presentation slides for this project can be found here

🧠 Project Learnings

1. Data Loading and Transformations.
2. Pivot table analysis.
3. Power Query and DAX.
4. Data visualization.
5. Conditional and calculated column.
6. Importance of data quality.
7. Data storytelling.
8. Sharpened analytical and problem-solving abilities.
9. Strengthened strategic planning and presentation skill.
10. Enhanced communication skills.

💻 Installation and Usage

• Microsoft Excel

📈 Dashboard