📊 Telecom Customer Churn Analysis

This project analyzes customer churn behavior for a telecom company using Power BI, Excel, Power Query, and DAX.
The goal of this project is to help the business understand:

• Why customers leave
• Who is more likely to churn
• Which customer segments are at risk
• What actions can reduce churn

This is a complete end-to-end data analytics project — from data cleaning to dashboard building to insight generation.

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📊 Dashboard – Page 1

📈 Dashboard – Page 2 (Behavior & Insights)

🎬 Project Walkthrough Video

📌 1. Problem Statement

A telecom company is experiencing a high percentage of customer churn.
The management wants to understand:

• What drives customers to leave?
• Which locations and demographics have the highest churn?
• How do bill amount, satisfaction score, and tenure influence churn?
• What actions can reduce churn and improve retention?

This project aims to build an interactive Power BI dashboard to answer these questions clearly.

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❓ 2. Key Business Questions

The client wanted answers to these:

1. Which customer age groups churn the most?
2. What are the top reasons for churn?
3. Which locations have the highest churn?
4. How does churn vary across age groups, payment methods, and service types?
5. What is the average tenure and satisfaction score of churned customers?
6. Do prepaid or postpaid users churn more?
7. Which payment channels are linked to higher churn?

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📂 3. Dataset Description

The dataset contains nearly 6,000 rows of data with the following columns:

• CustomerID — Unique customer identifier
• Churn_Status — Yes / No
• Age_Group — 18–25, 26–35, 36–50, 50+, Unknown
• Gender — Male, Female, Other
• Telecom_Circle — Customer location (state)
• Service_Type — Prepaid / Postpaid
• Payment_Method — UPI, Credit Card, Own App, Retail Store.
• Monthly_Charges — Monthly bill amount (numeric)
• Total_Charges — Lifetime bill amount (numeric)
• Tenure_in_Months — Months customer stayed (numeric)
• Customer_Satisfaction_Score — 1–5 rating
• Churn_Reason — Reason for leaving (if churned)

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🧼 4. Data Cleaning (Excel + Power Query)

I cleaned the dataset using Excel and Power Query.
Below are the steps I followed:

• Removed leading & trailing spaces using TRIM() / Power Query Trim + Clean
• Handled missing values:
  • Payment_Method → filled with UPI (most common)
  • Age_Group → Unknown
  • Gender → Other
  • Customer_Satisfaction_Score → filled with 3
  • Churn_Reason → filled with other reasons if not mrntioned
  • Telecom_Circle → ** if not mentioned Unknown**
  • Dependents → No
• Fixed numeric issues:
  • Removed " Rs" text from numeric columns
  • Converted Monthly_Charges, Total_Charges, Tenure_in_Months to numeric
  • Recalculated Total_Charges = Monthly_Charges * Tenure_in_Months
• Standardized categories for Service_Type, Payment_Method, Contract_Type
• Removed duplicates and invalid rows
• Loaded the final clean dataset into Power BI

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🧮 5. DAX Measures Used

Below are the key DAX measures used in the Power BI report:

Total Customers

Total Customers = COUNTROWS('Telecom Data')

Total Churned Customers

Total Churned Customers =
CALCULATE(
  COUNTROWS('Telecom Data'),
  'Telecom Data'[Churn_Status] = "Yes"
)

Churn Rate (%)

Churn Rate = DIVIDE([Total Churned Customers], [Total Customers], 0)

Avg Tenure of Churned Customers

Avg Tenure of Churned Customers =
CALCULATE(
  AVERAGE('Telecom Data'[Tenure_in_Months]),
  'Telecom Data'[Churn_Status] = "Yes"
)

Avg Monthly Charges of Churned Customers

Avg Monthly Charges of Churned Customers =
CALCULATE(
  AVERAGE('Telecom Data'[Monthly_Charges]),
  'Telecom Data'[Churn_Status] = "Yes"
)

Avg Satisfaction Score (Churned Only)

Avg Satisfaction Score of Churned Customers =
CALCULATE(
  AVERAGE('Telecom Data'[Customer_Satisfaction_Score]),
  'Telecom Data'[Churn_Status] = "Yes"
)

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📊 6. Dashboard Pages & Visuals

Page 1 — Telecom Customer Churn Overview

Answers:

• What is the overall churn rate?
• Which locations have the most churn?
• Do certain age groups churn more?
• Which payment methods and service types show higher churn?

Visuals:

• KPI Cards: Total Customers, Total Churned, Churn Rate, Avg Monthly Charges, Avg Tenure
• Donut: Churn Status breakdown
• Bar: Churn by Telecom_Circle
• Column: Churn by Age_Group
• Bar: Churn by Payment_Method
• Pie: Churn by Service_Type
• Slicers: Telecom_Circle, Age_Group, Payment_Method, Service_Type

Page 2 — Customer Behavior & Churn Insights

Explores reasons and behaviors driving churn.

Visuals:

• KPI Cards: Avg Tenure (Churned), Avg Satisfaction (Churned), Avg Monthly Charges (Churned)
• Column: Tenure groups vs Churn count
• Column: Customer_Satisfaction_Score vs Churn count
• Column/Bar: Monthly_Charges bins vs Churn count
• Bar: Churn_Reason (sorted by count)
• Slicers: Telecom_Circle, Age_Group, Payment_Method, Service_Type

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🔍 7. Key Insights

• Churn Rate ≈ 20.6% — Opportunity to improve retention.
• Age groups 26–35 and 36–50 show higher churn — target with retention offers.
• Top churn reasons: Network Issues, High Price, Wrong Recharge Issues, Poor Customer Service, Competitor Offers.
• Certain circles (Gujarat, UP East, Delhi) show higher churn — investigate network quality and competitor activity.
• Prepaid users show different churn patterns than postpaid; target onboarding and first-month offers.
• UPI / Own App users churn more than cash/retail-store users.
• Churned customers have lower avg satisfaction (~2.8) and shorter tenure — focus on early engagement.

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Thank you for checking out my project. Feel free to reach out if you want collaboration.