An end-to-end Business Analytics Capstone Project that transforms raw customer transaction data into actionable business insights through SQL, Python, Power BI, and Generative AI.
Businesses generate massive volumes of customer transaction data but often struggle to convert it into meaningful business insights. This project develops a Customer Intelligence System that segments customers, identifies churn risks, visualizes key business metrics, and generates AI-powered customer summaries to support data-driven decision-making.
Organizations collect large amounts of customer transaction data but face challenges in:
- Identifying high-value customers
- Detecting customer churn early
- Understanding customer purchasing behavior
- Making insights accessible to non-technical stakeholders
Traditional dashboards require analytical expertise, limiting business adoption.
Develop an end-to-end Customer Intelligence System capable of:
- Cleaning and preprocessing raw transactional data
- Performing customer behavior analysis
- Segmenting customers using RFM Analysis
- Identifying churn-risk customers
- Building interactive Power BI dashboards
- Creating a GenAI-powered natural language insight generator
- Cleaned transactional datasets
- Removed null values and duplicates
- Standardized data types
- Created revenue calculations
- Joined multiple tables for analysis-ready data
- Performed Exploratory Data Analysis (EDA)
- Analyzed customer purchasing trends
- Identified seasonal revenue patterns
- Evaluated product and country performance
Implemented RFM Analysis to classify customers into:
- 🏆 Champion
- ❤️ Loyal
⚠️ At Risk- ❌ Lost
- 🌱 New Customers
- Defined churn based on customer inactivity
- Identified high-risk customer groups
- Highlighted Recency as the strongest churn indicator
Designed a 3-page Power BI Dashboard featuring:
- Executive Business Overview
- Customer Intelligence Dashboard
- Product & Operations Dashboard
Built a GenAI-powered feature that converts customer analytics into simple business summaries, including:
- Customer Segment
- Purchase Behavior
- Churn Risk
- Marketing Recommendations
- Identified that 20% of customers generate the majority of revenue (Pareto Principle).
- Successfully segmented customers into actionable business categories.
- Detected high churn probability among inactive customer groups.
- Built an interactive Power BI dashboard for executive reporting.
- Simplified complex analytics through AI-generated customer summaries.
- Delivered an end-to-end analytics pipeline from raw data to business recommendations.
| Category | Tools |
|---|---|
| Database | SQL Server |
| Programming | Python |
| Libraries | Pandas, Matplotlib, Seaborn |
| Visualization | Power BI |
| AI | Generative AI |
| Analytics | RFM Analysis, Customer Segmentation, Churn Analysis |
- ✅ SQL Data Cleaning & Transformation
- ✅ Exploratory Data Analysis (EDA)
- ✅ Customer Segmentation using RFM
- ✅ Customer Churn Identification
- ✅ Interactive Power BI Dashboard
- ✅ AI-Powered Customer Summary Generator
- ✅ Business Recommendation Engine
- Implement loyalty programs for Champion customers.
- Launch targeted campaigns for At-Risk customers.
- Optimize product strategy by promoting high-performing products.
- Improve customer engagement through personalized recommendations.
- Leverage AI-generated insights for faster business reporting.
- Real-time data integration
- Machine Learning-based churn prediction
- Personalized recommendation engine
- AI chatbot for customer insights
- Automated business reporting
Team 3
- Abhay Singh
- Nilesh Mishra
- Abhinaw Tripathi
- Harender