🛒 Walmart Sales Data Analysis Project Project Overview
This project focuses on performing end-to-end data analysis on Walmart sales data using PostgreSQL. The goal of the project is to clean raw transactional data and extract meaningful business insights related to products, sales performance, and customer behavior. The results can support data-driven decision making in retail operations.
🧹 Data Cleaning & Preparation
Raw CSV data was cleaned and transformed to make it analysis-ready. The following steps were performed:
Converted date and time formats to PostgreSQL compatible DATE and TIME
Standardized numeric columns using DECIMAL/NUMERIC for financial accuracy
Handled data type mismatches and formatting issues during CSV import
Created additional derived columns such as month_name and day_name
Verified missing values, duplicates, and invalid records
Ensured overall data consistency and reliability for analysis
📦 Product Analysis
Key insights related to product performance were derived, including:
Total number of unique product lines
Most selling product line by quantity
Highest revenue-generating product line
Product line with highest VAT contribution
Good vs Bad product classification based on average sales
Most common product line by gender
Average rating per product line
This analysis helps in understanding product demand, profitability, and customer preferences.
📊 Sales Analysis
Sales performance was analyzed across multiple time and location dimensions:
Total revenue by month
Month with the highest COGS
City and branch level revenue performance
Branch performance vs average sales
Number of sales by time of day and weekday
VAT contribution by city
Time of day with highest customer ratings
Best weekday based on average ratings
These insights support seasonal planning, branch optimization, and operational strategy.
👥 Customer Analysis
Customer behavior and segmentation were analyzed using:
Number of unique customer types and payment methods
Most common customer type
Customer type with highest purchases and revenue
Customer type with highest VAT contribution
Gender distribution overall and by branch
Identification of dominant customer segments
This analysis provides valuable insights into customer purchasing patterns and demographics.
🛠️ Tools & Technologies Used
PostgreSQL – Data storage, cleaning, and analysis
SQL (Advanced Queries) – Aggregation, CTEs, Window Functions
CSV Data Source – Walmart Sales Dataset
pgAdmin – Database management 📈 Project Results & Key Findings
After performing detailed data cleaning and analysis, the following key business insights were derived from the Walmart sales dataset:
Customer Type Performance: The Member customer segment generated higher overall revenue compared to Normal customers, indicating strong loyalty-driven sales impact.
Top Performing Product Line: The Electronic Accessories product line emerged as the highest revenue and highest selling category, showing strong customer demand.
Revenue Trends: Sales showed noticeable variation by month, with clear peak revenue during high-demand seasonal periods, supporting the importance of seasonal stock planning.
Cost & Profitability Insights: The month with the highest COGS also aligned with peak sales periods, indicating balanced operational scaling during high-volume months.
Branch & City Performance: Among all locations, one city consistently generated the highest revenue, showing strong regional market dominance.
Payment Behavior: E-wallet and Cash were the most preferred payment modes, highlighting the significance of digital payments in retail transactions.
Customer Purchasing Patterns: Female customers contributed slightly more towards overall sales and transactions compared to male customers.
Time-Based Sales Insights: The afternoon and evening time slots recorded the highest number of transactions and customer ratings.
Product Quality & Satisfaction: Product lines with higher average sales also showed better customer ratings, reflecting a positive relationship between demand and satisfaction.
VAT Contribution Analysis: Certain product lines and customer types contributed significantly higher VAT, helping identify key tax-contributing segments.