https://github.com/kalyan4636/end-end-churn-analysis-portfolio-project
Introduction to Churn Analysis
https://github.com/kalyan4636/end-end-churn-analysis-portfolio-project
Last synced: 29 days ago
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Introduction to Churn Analysis
- Host: GitHub
- URL: https://github.com/kalyan4636/end-end-churn-analysis-portfolio-project
- Owner: Kalyan4636
- Created: 2024-11-16T17:06:23.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-11-25T16:20:28.000Z (5 months ago)
- Last Synced: 2025-02-10T02:52:47.808Z (3 months ago)
- Size: 849 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# End-End-Churn-Analysis-Portfolio-Project
Introduction to Churn Analysis
In today’s competitive business environment, retaining customers is crucial for long-term success. Churn analysis is a key technique used to understand and reduce this customer attrition. It involves examining customer data to identify patterns and reasons behind customer departures. By using advanced data analytics and machine learning, businesses can predict which customers are at risk of leaving and understand the factors driving their decisions. This knowledge allows companies to take proactive steps to improve customer satisfaction and loyalty.
# Project Target
# Create an entire ETL process in a database & a Power BI dashboard to utilize the Customer Data and achieve below goals:
1 - Visualize & Analyse Customer Data at below levels
2 - Demographic
3 - Geographic
4 - Payment & Account Info
5 - Services
6 - Study Churner Profile & Identify Areas for Implementing Marketing Campaigns
7 - Identify a Method to Predict Future Churners
# STEP 1 – ETL Process in SQL Server
# STEP 2 – Power BI Transform
# STEP 3 – Power BI Measure
# STEP 4 – Power BI Visualization
# STEP 5 – Predict Customer Churn
# STEP 6 – Power BI Visualization of Predicted Data
That’s it, now you have a comprehensive Power BI dashboard with and Executive Summary to analyze historical data and also a Churn Prediction page to predict future churners.
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