This project analyzes a customer churn dataset using Power BI. The focus is on data cleaning, exploratory data analysis (EDA), and generating key insights into customer behavior and churn trends.
🔹 Total Customers: 6,687 (👨 3,379 male | 👩 3,301 female). Among them, 1,984 are new joiners within the year.
🔹 Churn Rate: 📉 26.8%
🔹 Top Payment Methods: 💳 Direct Debit > Credit Card > Paper Check
🔹 Age & Churn: ⏳ Highest churn between 40-50 years old.
🔹 New Joiners & Churn: 🚀 49% of churned customers are new joiners.
🔹 Contract Type & Churn: 📝 Most churned customers were on Month-to-Month contracts.
🔹 State with Highest Churn: 🌎 California (CA) - 63.24%
🔹 Top Churn Reasons:
1️⃣ Competitors offering better deals 🏆
2️⃣ Competitors providing better devices 📱
3️⃣ Negative support experiences 📞❌
🔹 Pricing & Churn: 📊 Higher monthly charges = lower churn rate.
✔️ Retention Offers: 🎁 Provide special offers & loyalty perks to new joiners.
✔️ Contract Adjustments: 🔄 Encourage annual/biennial contracts instead of Month-to-Month plans.
✔️ Customer Support Improvement: 📢 Invest in training & monitoring to enhance support quality.
✔️ Payment Optimization: 💰 Promote digital payments and simplify checkout processes.
By implementing these strategies, we can enhance customer loyalty and reduce churn effectively. 🚀📈
Yousef Sebai
📧 Email: yousef.sebai011@gmail.com
🌍 Location: Cairo, Egypt
🔗 LinkedIn: linkedin.com/in/yousef-sebai (Add your actual link!)
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