📊 Customer Churn Telecom Insights with Excel

A comprehensive Excel-based analysis of customer churn in the telecom sector, showcasing data preparation, exploratory analysis, visualization, and dashboarding. This project demonstrates how Excel can be used not just for spreadsheets, but for powerful business intelligence and storytelling.

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🧠 Project Objective

To analyze customer churn patterns using Excel and uncover actionable insights that help telecom companies improve retention, identify high-risk segments, and optimize customer experience.

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📁 Dataset Overview

The dataset includes:

• Customer Demographics: Gender, Senior Citizen status, Age
• Subscription Details: Contract type, Payment method, Tenure
• Service Usage: Internet service, Streaming, Tech support, Monthly charges
• Churn Labels: Whether the customer left and why

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🛠️ Workflow Summary

1. 🔄 Data Aggregation & Preparation

• Cleaned raw customer data for consistency
• Created calculated columns for tenure buckets, age groups, and usage categories
• Aggregated data using pivot tables for segment-level analysis

2. 🔍 Data Exploration & Checks

• Checked for missing values and outliers
• Validated churn distribution across key variables
• Used conditional formatting to highlight churn-prone segments

3. 📈 Data Analysis & Visualization

• Built pivot tables to analyze churn by:
  • Contract type
  • Payment method
  • Tenure and age buckets
  • Service usage patterns
• Created charts to visualize:
  • Churn rate by customer type
  • Monthly charges vs. churn
  • Senior citizen impact on churn

4. 📊 Dashboarding

• Designed an interactive Excel dashboard with:
  • KPI cards (Total Customers, Churn Rate, Avg Tenure)
  • Slicers for contract type, payment method, and tenure
  • Dynamic charts updating based on filters
  • Clean layout for executive-level presentation

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💡 Key Business Insights

• Customers on month-to-month contracts exhibit the highest churn rates, highlighting the need for loyalty programs or longer-term engagement strategies.
• Users who pay via electronic checks are more likely to churn, suggesting potential friction in the payment experience.
• Long-tenure customers tend to remain loyal, making them ideal candidates for upselling and premium service offerings.
• Elevated monthly charges, especially among short-tenure users, are strongly associated with churn—indicating a mismatch between perceived value and cost.
• Surprisingly, unlimited data plan users consuming less than 10 GB show higher churn likelihood, possibly due to underutilization or misaligned plan benefits.
• A significant portion of churn is driven by competitor influence, underscoring the importance of competitive benchmarking and customer satisfaction initiatives.

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📂 Files Included

• Churn_Analysis_Excel_Dashboard.xlsx: Final dashboard with slicers and charts
• Customer Pivots on Customer data.pdf: Pivot-based analysis summary
• Churn Analysis on Customer Aggregated data.pdf: Aggregated insights
• Churn Analysis Dashboard.pdf: Dashboard walkthrough

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🚀 How to Use

1. Download the Excel workbook
2. Open Churn_Analysis_Excel_Dashboard.xlsx
3. Use slicers to filter by contract type, tenure, and payment method
4. Explore dynamic charts and KPIs to uncover churn patterns

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🔗 Related Projects

• Power BI Version – Interactive dashboard using Power BI
• Tableau Version – Visual storytelling with Tableau
• ANN Churn Prediction – Predictive modeling using Artificial Neural Networks and Streamlit

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📬 Contact

Feel free to connect or reach out for collaboration, feedback, or opportunities:

• GitHub Issues for suggestions
• LinkedIn: https://www.linkedin.com/in/arvind-kumar-560885231/

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