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Telecom Customer Churn Analysis

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Project Overview

This project presents an in-depth analysis of customer churn within the telecom industry. Using Excel's powerful data analysis tools, we've uncovered key insights to help reduce churn rates and improve customer retention strategies.

Key Findings

  • Total Customers: 6,687
  • Churned Customers: 1,796
  • Overall Churn Rate: 26.86%

Churn by Demographics

  • Significant variations in churn rates among seniors and customers under 30

Age Group Analysis

  • Highest churn rate observed in the 79-88 age group

Competitor Churn Analysis

  • Main reasons for switching to competitors:
    • Better device offers (37%)
    • More attractive data plans (37%)
    • Higher download speeds (14%)

Consumption-based Churn

  • Higher churn rates among users consuming less than 5GB of data

Geographic Churn Patterns

  • Highest churn rates:
    1. California (75%)
    2. Indiana (67%)
    3. New Hampshire (63%)

Methodology

  • Utilized Excel's Pivot Tables, Charts, and Data Analysis Toolpak
  • Created a comprehensive dashboard for visualizing churn patterns
  • Employed calculated columns and fields for deeper insights

Key Takeaways

  1. Target retention strategies towards the 79-88 age group
  2. Address pricing concerns and enhance service quality
  3. Develop competitive device and data plan offerings
  4. Focus on improving customer experience in high-churn states

Tools Used

  • Microsoft Excel
    • Pivot Tables
    • Charts and Visualizations
    • Calculated Columns and Fields
    • Data Analysis Toolpak

Dataset

The dataset for this project is available in the following Excel file:

Analyzing Customer Churn Dataset.xlsx

This dataset contains detailed information about customer churn in the telecom industry, including demographics, usage patterns, and reasons for churn.

Acknowledgements

This project was completed as part of DataCamp's Data Analyst with Excel track. Special thanks to DataCamp for providing the dataset and project structure.


This project was completed in July 2024 as part of a DataCamp course on data analysis with Excel.

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