Supermarket Sales Dataset

This dataset provides a historical record of three months of sales data from three different supermarket branches, capturing competitive market trends in populous cities. It’s structured to facilitate predictive analytics in retail.

Dataset Attributes:

• Invoice ID: Unique identifier for each sales transaction.
• Branch: Identifier for each of the three supermarket branches (A, B, and C).
• City: Supermarket locations.
• Customer Type: Distinguishes between ‘Member’ and ‘Normal’ customers.
• Gender: Customer gender.
• Product Line: Categories include Electronic Accessories, Fashion Accessories, Food and Beverages, Health and Beauty, Home and Lifestyle, Sports and Travel.
• Unit Price: Cost per item in dollars.
• Quantity: Items purchased per transaction.
• Tax: Applied sales tax (5%).
• Total: Total transaction cost, including tax.
• Date: Date of transaction, from January 2019 to March 2019.
• Time: Time of transaction, from 10 AM to 9 PM.
• Payment Method: Includes Cash, Credit Card, and Ewallet.
• COGS: Cost of goods sold.
• Gross Margin Percentage: Percentage of gross margin.
• Gross Income: Revenue generated from sales.

This structured data supports analysis of buying patterns, customer behavior, and financial performance across different locations and times.

Applications of the Supermarket Sales Dataset

Sales Performance Analysis

Businesses can utilize this dataset to track and analyze sales performance. By examining the sales volume and revenue generated by different product lines, companies can identify high-performing products and areas needing improvement.

Customer Behavior Analysis

Understanding customer behavior is crucial for any business. This dataset provides valuable insights into customer preferences and buying habits, enabling businesses to tailor their marketing strategies effectively.

Inventory Management

Effective inventory management is vital for any supermarket. With detailed transaction records, businesses can predict demand and manage inventory levels more efficiently, reducing the risk of stockouts or overstocking.