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https://github.com/shubham200137/customer-churn-analysis
In this case study, we analyze customer churn for a telecom company serving Southern California. The company faces increased competition and wants to retain customers by understanding the reasons for churn. Our objectives include improving service quality, identifying churn factors, pinpointing attractive services, and retaining high LTV customers.
https://github.com/shubham200137/customer-churn-analysis
data-analysis data-visualization numpy-python pandas-python sqlite tableau
Last synced: 7 days ago
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In this case study, we analyze customer churn for a telecom company serving Southern California. The company faces increased competition and wants to retain customers by understanding the reasons for churn. Our objectives include improving service quality, identifying churn factors, pinpointing attractive services, and retaining high LTV customers.
- Host: GitHub
- URL: https://github.com/shubham200137/customer-churn-analysis
- Owner: Shubham200137
- Created: 2023-09-21T15:02:26.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-21T15:56:19.000Z (over 1 year ago)
- Last Synced: 2024-11-10T21:14:15.648Z (2 months ago)
- Topics: data-analysis, data-visualization, numpy-python, pandas-python, sqlite, tableau
- Language: Jupyter Notebook
- Homepage:
- Size: 3.12 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Case Study: Churn Analysis for a Telecom Company
## Project Overview
In this case study, we are tasked with analyzing customer churn for a telecom company in Southern California. The company provides home phone and internet services, as well as various other services, and faces the challenge of losing customers to a new competitor. The objectives are as follows:
1. Service Performance Analysis: Identify underperforming services to improve customer satisfaction.
2. Churn Factors Identification: Determine variables affecting customer churn using existing account data.
3. Successful Service Identification: Identify services with high customer satisfaction for attracting new customers.
4. High-Value Customer Identification: Identify high-value customers for premium membership retention.
## Key Metrics:
1. Customer Churn Rate: The percentage of customers who have discontinued their subscription.
2. Customer Lifetime Value (CLV): The net profit associated with a customer over a fixed period.
## Approach:
1. Database Creation: Create a database, define schema, primary/foreign keys, and relationships, and apply normalization rules.
2. Data Refinement: Make necessary schema modifications.
3. Insights Generation: Analyze data using SQL to identify factors affecting CLV and churn.
4. Data Visualization: Utilize Tableau for data exploration, dashboard creation, and storytelling.
5. Python Integration: Connect Python to the database for advanced analysis.
## Tools Used:
- SQLite: Lightweight, portable, and easily integrated with Python and Tableau.
- Python: For running queries and complex calculations.
- Tableau: For data exploration, visualization, and storytelling.
## Data Source:
The data used for the analysis can be accessed from Below links:
- [Intro to the Case Study](https://cdn.upgrad.com/uploads/production/a1cfb9b7-8f9c-4578-aff1-d80d2363d375/Session+1+Files.zip)
- [Analyzing Churn and LTV Using SQL and Python](https://cdn.upgrad.com/uploads/production/99be7345-0b61-46d7-867c-b83e18905ad8/Session+2+files.zip)
- [Creating a Tableau Story](https://cdn.upgrad.com/uploads/production/03361e80-2f6d-4400-a2d6-e9f0ff16d327/Session+4+Files.zip)