End-to-End Retail Data Analytics Project | SQL · Power BI.tableau
Retail businesses struggle to understand customer behavior, manage inventory efficiently, and identify which products or regions are driving — or hurting — sales. Without clear analytics, decisions are made on gut feeling rather than data. This project builds a complete analytics solution to address these challenges.
To analyze retail sales, customer, and inventory data to:
- Identify top-performing products, regions, and customer segments
- Detect inventory inefficiencies and overstock/understock situations
- Uncover sales trends and seasonal patterns
- Provide actionable insights through interactive dashboards
| Tool | Purpose |
|---|---|
| SQL (MySQL) | Data extraction, transformation, and aggregation |
| Power BI | Executive-level interactive dashboard |
| Tableau | Detailed customer and inventory visualizations |
| Excel | Data cleaning and preprocessing |
- Sales Analysis — Revenue trends, top products, regional performance, YoY growth
- Customer Analytics — Segmentation, purchase frequency, customer lifetime value (CLV)
- Inventory Management — Stock levels, turnover rates, overstock and stockout detection
- Category Performance — Profitability by product category
- Seasonal Trends — Peak periods, promotional impact analysis
- Data Preparation — Cleaned and standardized raw retail data in Excel
- SQL Analysis — Wrote complex queries for sales aggregation, joins across tables, and KPI calculations
- Power BI Dashboard — Built a multi-page executive dashboard covering sales, customers, and inventory
- Tableau Visualizations — Created deep-dive views on customer behavior and stock patterns
- Insight Generation — Translated findings into business recommendations
- Top 20% of products contribute to the majority of total revenue (Pareto principle validated)
- Identified regions with consistently underperforming sales despite high inventory
- Customer segmentation revealed a high-value repeat-buyer group with specific buying patterns
- Inventory turnover analysis flagged slow-moving SKUs for promotional intervention
- Seasonal spikes confirmed, enabling better demand forecasting
RetailPulse-Analytics-Project/
│
├── data/ # Raw and cleaned retail datasets
├── sql_queries/ # SQL scripts for all analysis
├── powerbi_dashboard/ # Power BI .pbix file
├── tableau_workbooks/ # Tableau .twbx files
├── excel_files/ # Preprocessed data
└── README.md
RetailPulse gives retail decision-makers a 360° view of their business — from shelf to sale — helping them make faster, smarter, and more profitable decisions.
Vaishnavi Palamakula — Aspiring Business Analyst passionate about retail analytics and data-driven storytelling.
🔗 GitHub Profile | 💼 [LinkedIn]linkedin.com/in/vaishnavi-p-708073352