How pavangowda547/customer-retention works
This repository presents a customer-retention analysis: customer data is examined for churn patterns, summarized into metrics and visualizations, and used to inform retention recommendations. The README describes data cleaning, trend analysis, and dashboards, and lists Python, SQL, Excel, and Power BI/Tableau as technologies. The tree includes a dataset workbook, analysis notebook, SQL file, and Power BI report. No source excerpts were provided, so links between these artifacts are shown only where documented or marked unverified.
Main entry points: customer_retention.ipynb Retention trends · Churn Retention.pbix Retention dashboard · customer_retention.sql Metrics and aggregation · Churn_Dataset.xlsx Customer dataset
Components 7
Customer data
- Customer dataset
Churn_Dataset.xlsx
Retention analysis
- Data cleaning
customer_retention.ipynb - Retention trends
customer_retention.ipynb - Metrics and aggregation
customer_retention.sql
Insights and reporting
- Retention recommendations
README.md - Retention dashboard
Churn Retention.pbix
Other components
- Business analyst
Connections 7
- Customer dataset → Data cleaning: provides data
- Data cleaning → Retention trends: prepares analysis
README.md - Retention trends → Metrics and aggregation: summarizes metrics
- Retention trends → Retention dashboard: visualizes findings
- Metrics and aggregation → Retention dashboard: supports reporting
- Retention dashboard → Business analyst: presents insights
- Retention trends → Retention recommendations: informs strategy
README.md