Case Study - Analyzing Databel Customer Churn In Excel 

 Data Analysis Flow:

1. Data Check

2. Data Exploration

3. Analyze and Visualize Data

4. Dashboarding

5. Communicating Insights

Key Insights Uncovered

• Churn Rate: Over 1,750 customers churned from a total of 6,687, resulting in a churn rate of 27%.

• Top Reasons for Churn:

  • Competitors offered better devices 📱

  • Competitors made better offers 💼

  • Competitors provided faster download speeds 🌐

  • Competitors offered more data 📊

• Data Usage: Customers consuming the least data were the most likely to churn at 35%.

• Age Group Insights:

  • Customers aged 79-88 had the highest churn rate of 43.8% despite being the smallest group.

  • Seniors (age 65+) had a churn rate of 38% compared to the under 30 group, which had the lowest churn rate of 23%.

To present these findings, I built a detailed dashboard using PivotTables and calculated columns/fields, showcasing the most relevant insights to drive action.

💡 Key takeaway:

Understanding the customer segments most prone to churn and their reasons can help businesses craft targeted retention strategies and stay competitive!

I’m excited to apply these skills in future projects to help businesses make data-driven decisions. If you're looking for someone passionate about transforming data into actionable insights, let's connect! #DataAnalytics #CustomerChurn #Excel #DataVisualization #Telecom #PivotTables #Dashboard #ChurnReduction