CustomerInsight Analytics is a full-stack customer analytics platform that helps analyze customer behavior, identify churn risk, and generate actionable retention insights from uploaded CSV datasets.
I built this project to practice end-to-end application development by combining data processing, analytics, backend APIs, and interactive dashboards in a business-focused use case.
The platform allows users to upload customer datasets, automatically calculates churn risk scores, segments customers into meaningful groups, and presents insights through charts and analytics dashboards.
Customer retention is one of the most important business challenges across industries. Companies need to identify customers who are likely to leave and take proactive measures to improve retention.
I wanted to build a project that simulates how analytics teams and product teams can use customer data to generate insights, prioritize retention efforts, and make data-driven decisions.
CustomerInsight Analytics can be used by business teams and analytics teams to better understand customer behavior and identify customers who may be at risk of leaving.
The platform allows users to upload customer datasets and automatically generates insights such as churn risk scores, customer segments, contract distribution, and retention recommendations.
This type of application can help teams:
- Identify customers with high churn risk
- Prioritize retention efforts for at-risk customers
- Understand customer segments and engagement patterns
- Analyze contract and payment trends
- Monitor customer metrics through interactive dashboards
- Generate data-driven insights from uploaded customer datasets
- Support decision-making using customer analytics and visualizations
The application is designed as an end-to-end analytics platform that combines data processing, business intelligence, and dashboard reporting in a single interface.
- Upload customer CSV datasets
- Automatic customer data analysis
- Customer churn risk scoring
- Customer segmentation
- Customer retention recommendations
- Customer analytics dashboard
- Customer intelligence table
- Interactive charts and visualizations
- Search and filter functionality
- FastAPI Swagger API documentation
- React
- Axios
- Recharts
- JavaScript
- CSS
- FastAPI
- Python
- Pandas
- Pydantic
- VS Code
- Git
- GitHub
- Swagger UI
- User uploads a customer CSV file.
- Backend validates the dataset.
- Customer records are processed using analytics rules.
- Churn risk scores are calculated.
- Customers are segmented into different categories.
- Retention recommendations are generated.
- Dashboard metrics and visualizations are updated dynamically.
| Method | Endpoint | Description |
|---|---|---|
| GET | / | Backend health check |
| GET | /sample-data | Load sample customer dataset |
| POST | /upload-csv | Upload and analyze customer CSV |
| GET | /analytics | Retrieve customer analytics |
Screenshots are available in the screenshots folder.
Included screenshots:
- Empty dashboard
- CSV upload flow
- Analytics dashboard
- High-risk retention queue
- Customer intelligence table
- Swagger API documentation
- Project structure
- Final dashboard view
While building CustomerInsight Analytics, I worked through:
- CSV file processing
- Data validation and transformation
- Business analytics workflows
- Customer segmentation strategies
- Risk scoring techniques
- Dashboard development
- Frontend-backend integration
- Data visualization techniques
- Building business-oriented applications
- Authentication and role management
- Database integration
- Real machine learning models for churn prediction
- Downloadable analytics reports
- Email campaign recommendations
- Historical analytics tracking
- Cloud deployment and monitoring
Chaitanya Naga Eeda