📊 Retail Data Analysis Project
Due to GitHub file size limitations, the Power BI (.pbix) file is hosted on Google Drive.
Download the PBIX file here: https://drive.google.com/your-link-here
After downloading, open it using Power BI Desktop.
- Project Overview
This project focuses on analyzing retail data to uncover meaningful insights about customers, products, sales, and marketing campaigns. The dashboard helps businesses make data-driven decisions by identifying patterns, trends, and opportunities for growth. The goal of this project is to transform raw transactional data into interactive visualizations and actionable insights using Power BI.
- Purpose of the Project
To analyze customer demographics and purchasing behavior
To evaluate campaign effectiveness and coupon usage
To identify top-performing products and categories
To understand sales trends and seasonal patterns
To discover product associations using market basket analysis
- Tech Stack
Power BI – Data visualization and dashboard creation
DAX (Data Analysis Expressions) – Calculations and KPIs
Power Query – Data cleaning and transformation
Excel / CSV – Data storage format
- Data Source
Dataset sourced from Kaggle Retail Dataset Contains information related to: Customer demographics Transactions and sales Products and categories Campaign and coupon data
- Features & Highlights
📌 A. Customer Demographic Analysis:
Analyzed household distribution based on different demographic factors
Identified high-value customer segments
📌 B. Campaign & Coupon Analysis:
Measured coupon redemption rates
Compared performance across different campaigns
Identified the most effective campaign types
📌 C. Product Analysis:
Identified top-selling products and categories
Compared total sales across departments
Analyzed product contribution to overall revenue
📌 D. Transaction Analysis:
Evaluated total sales, discounts, and transaction volume
Identified trends in coupon discounts
Measured sales contribution by different factors
📌 E. Sales & Revenue Insights:
Analyzed overall revenue performance
Identified peak sales periods
Compared weekly sales variations
📌 F. Time Series Analysis:
Explored sales trends over time
Analyzed seasonality and cyclic patterns
Studied correlation between sales and coupon redemptions
📌 G. Market Basket Analysis:
Discovered frequently purchased product pairs
Identified cross-selling opportunities
Generated insights for product recommendations
📌 H. Interactive Dashboard
Page navigation for multiple analysis sections
Dynamic filtering using slicers
User-friendly layout for easy insights
- Screenshots
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