Retail Sales Business Analysis Project
Overview This project performs an end-to-end business analysis on a retail sales dataset and provides both data-driven insights and an interactive Power BI dashboard. It is designed to help business analysts and decision-makers identify sales trends, customer segments, and opportunities for growth.
Dataset File: retail_sales_dataset.csv Rows: 1,000 Columns: Transaction ID, Date, Customer ID, Gender, Age, Product Category, Quantity, Price per Unit, Total Amount
Business Questions Addressed
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What are the overall sales trends (monthly/weekly)?
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Which product categories and customer segments drive the most revenue?
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How does customer age and gender affect sales?
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Who are the top customers based on RFM (Recency, Frequency, Monetary) analysis?
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What are the key correlations among sales features?
Analysis Workflow
Data Cleaning & Preparation:
Date conversion
Feature engineering (Month, Quarter, Week, Age Bucket)
KPI Calculation:
Total Revenue, Transactions, Unique Customers, Units Sold
Average Order Value (AOV), Items per Transaction
Trend Analysis:
Monthly and weekly revenue trends
Category & Demographics:
Revenue by category, gender, and age groups
RFM Customer Segmentation:
Recency, Frequency, Monetary scoring
Correlation Analysis:
Key Insights & KPIs
Total Revenue: (calculated from dataset)
Transactions: (calculated from dataset)
Unique Customers: (calculated from dataset)
Average Order Value: (calculated from dataset)
Units Sold: (calculated from dataset)
Deliverables
Business Analysis Report: Retail_Business_Analysis_Report.md
KPI Summary (CSV): kpis_summary.csv
Charts (PNG):
Monthly Revenue Trend
Weekly Revenue Trend
Revenue by Category
Revenue by Gender
Age Distribution
Revenue by Age Bucket
Correlation Matrix
Power BI Dashboard: Retail_Sales_Dashboard.pbix (interactive visual exploration)
How to Use
Open Retail_Sales_Dashboard.pbix in Power BI Desktop.
Explore interactive dashboards for filtering by category, date, and customer segments.
Refer to the Markdown report for detailed KPI explanations and actionable recommendations.
Future Enhancements
Integrate predictive modeling (sales forecasting)
Automate data refresh from live sources
Add customer lifetime value (CLV) predictions
Deploy as a web-based interactive dashboard
Tools Used
Power BI: Interactive dashboard creation
Markdown: Reporting and documentation
Author: D Rajeev
Created on 2025-08-21
This project serves as a blueprint for business analysts to turn raw retail sales data into actionable insights