Skip to content

README.md

Latest commit

 

History

History
171 lines (129 loc) · 5.98 KB

README.md

File metadata and controls

171 lines (129 loc) · 5.98 KB

📊 Customer Segmentation Visualization & Advanced Analysis

This project was completed as part of a Business Analyst Internship at Saiket Systems.
It focuses on analyzing customer churn behavior in a telecommunications company using data analysis, visualization, and business insights to identify at-risk customers and recommend retention strategies.

Python Pandas NumPy Matplotlib Seaborn EDA Jupyter Power BI Status

🏢 Internship Details

  • Role: Business Analyst Intern
  • Organization: Saiket Systems
  • Project Type: Customer Churn Analysis & Segmentation
  • Tools Used: Python, Pandas, Matplotlib, Seaborn, Power BI

🎯 Project Objective

The primary objective of this project is to:

  • Analyze customer churn patterns
  • Segment customers based on tenure and behavior
  • Identify key churn drivers (contracts, payments, services, demographics)
  • Provide actionable business recommendations to reduce churn and improve customer retention

📁 Project Folder Structure

├── Customer Segmentation Analysis.ipynb
├── Customer_Segmentation_Churn_Dashboard.pbix
├── Customer_Segmentation_Churn_Dashboard.pdf
├── README.md
├── requirements.txt
├── Telco_Customer_Churn_Dataset.csv
└── images/
    ├── 01_Churn_vs_Non-Churn_Distribution.png
    ├── 02_Tenure_Distribution.png
    ├── 03_Monthly_Charges_vs_Churn.png
    ├── 04_Customer_Distribution_by_Tenure_Group.png
    ├── 05_Average_Monthly_Charges_by_Tenure_Group.png
    ├── 06_Churn_Rate_by_Tenure_Group.png
    ├── 07_Churn_by_Gender.png
    ├── 08_Churn_by_Senior_Citizen.png
    ├── 09_Churn_by_Contract_Type.png
    ├── 10_Churn_by_Payment_Method.png
    └── Customer_Segmentation_Churn_Dashboard.png

📊 Dataset Overview

  • Dataset: Telco Customer Churn Dataset
  • Target Variable: Churn (Yes / No)
  • Key Features:
    • Customer demographics (gender, senior citizen, dependents)
    • Account details (tenure, contract type, payment method)
    • Service usage (internet, phone, streaming, security)
    • Financial metrics (monthly charges, total charges)

🧩 Project Tasks & Methodology

✅ Task 1: Dataset Understanding

  • Loaded and explored the dataset using Pandas
  • Identified data types and missing values
  • Understood business context of each feature

✅ Task 2: Data Cleaning

  • Standardized column names
  • Handled missing values in TotalCharges
  • Removed duplicate records
  • Ensured data consistency for analysis

✅ Task 3: Exploratory Data Analysis (EDA)

  • Analyzed churn vs non-churn distribution
  • Studied tenure, monthly charges, and churn relationships
  • Used histograms, box plots, and count plots

✅ Task 4: Customer Segmentation Visualization

  • Segmented customers into tenure groups:
    • 0–12 Months
    • 13–36 Months
    • 37+ Months
  • Visualized customer distribution and average charges
  • Highlighted lifecycle-based revenue patterns

✅ Task 5: Advanced Analysis

  • Analyzed churn by:
    • Contract type
    • Payment method
    • Gender
    • Senior citizen status
  • Identified high-risk customer segments
  • Connected insights to business actions

📈 Sample Visualizations

Churn Distribution

Churn Distribution

Customer Distribution by Tenure Group

Tenure Distribution

Average Monthly Charges by Tenure

Avg Charges

Churn by Contract Type

Contract Churn

📊 Power BI Dashboard

An interactive Power BI dashboard was created to visualize churn trends and customer segmentation.

Dashboard Highlights:

  • KPI cards for total customers, churn rate, and average monthly charges
  • Tenure-based customer segmentation
  • Churn analysis by contract, payment method, and services
  • Interactive slicers for dynamic exploration

📁 Files:

  • Customer_Segmentation_Churn_Dashboard.pbix
  • Customer_Segmentation_Churn_Dashboard.pdf

Power BI Dashboard

🧠 Key Business Insights

  • Customers on month-to-month contracts have the highest churn
  • New customers (0–12 months) are at greater churn risk
  • Electronic check payment method shows higher churn
  • Long-term contracts significantly improve customer retention
  • High monthly charges correlate with churn if value perception is low

💡 Business Recommendations

  • Encourage customers to switch to long-term contracts using incentives
  • Improve onboarding experience for new customers
  • Promote automatic payment methods
  • Offer loyalty benefits to high-value customers

🚀 Skills Demonstrated

  • Business Analysis & Data Interpretation
  • Customer Segmentation
  • Exploratory Data Analysis (EDA)
  • Data Visualization (Python & Power BI)
  • Insight-driven decision making
  • Dashboard design & storytelling

🧑‍💻 Author

👤 Harsh Belekar
📍 Data Analyst | Python | SQL | Power BI | Excel | Data Visualization
📬 LinkedIn | 🔗GitHub

📧 harshbelekar74@gmail.com

⭐ If you found this project helpful, feel free to star the repo and connect with me for collaboration!