Retail Sales Insights Dataset

Columns

• Invoice ID: A distinct identifier for each individual transaction.
• Branch: The specific supermarket branch where the transaction occurred (e.g., A, B, C).
• City: The city location of the supermarket branch (e.g., Yangon, Mandalay).
• Customer Type: Denotes whether the customer is a "Member" or a "Normal" shopper.
• Gender: The customer's gender, providing demographic insight.
• Product Line: The category of the product purchased (e.g., Groceries, Clothing, Fashion accessories, Food and beverages).
• Unit Price: The price per single unit of the product.
• Quantity: The number of units bought within a transaction.
• Tax (5%): The tax amount applied, calculated as 5% of the total before tax.
• Total: The full amount paid, which includes the applied tax.
• Date/Time: The exact date and time of purchase, useful for identifying peak shopping periods.
• Payment: The method of payment used for the transaction (ee.g., Cash, Credit Card).
• COGS (Cost of Goods Sold): The production cost associated with the goods that were sold.
• Gross Margin Percentage: The percentage difference between the sales revenue and the cost of goods sold.
• Gross Income: The income remaining after deducting the cost of goods sold.
• Rating: A customer satisfaction score, typically presented on a scale of 1 to 5.

Distribution

Usage

• Analysing sales trends and fluctuations over time.
• Investigating customer behaviour and purchasing patterns.
• Evaluating revenue performance across different branches or product lines.
• Developing and optimising promotional strategies.
• Refining product placement and stocking strategies.
• Gaining insights into purchasing trends and customer preferences.
• Formulating targeted strategies to boost revenue growth.
• Enhancing customer satisfaction initiatives.

Coverage

• Geographic Scope: The data originates from supermarket branches located in cities such as Yangon and Mandalay, encompassing branches A, B, and C.
• Time Range: The dataset includes specific dates and times of purchase, which can be utilised to identify peak hours and analyse time-based trends. A specific overall time range is not specified.
• Demographic Scope: Customer demographics include gender, with an equal distribution of 50% Female and 50% Male customers. Customer types are split evenly between 50% Members and 50% Normal shoppers.
• Data Availability: All listed key fields exhibit 100% validity and 0% missing data across the 1000 records.

License

Who Can Use It

• Business Analysts seeking to understand retail performance and market dynamics.
• Data Scientists developing predictive models for sales forecasting or customer segmentation.
• Marketing Professionals aiming to tailor promotions based on customer behaviour.
• Retail Managers looking to optimise store operations, product offerings, and customer service.
• Academics and Researchers studying consumer trends or retail economics.
• Anyone interested in extracting actionable insights for revenue enhancement and customer experience improvement.

Dataset Name Suggestions

• Supermarket Transaction Analysis
• Retail Sales Insights Dataset
• Customer Purchase Dynamics
• Grocery Retail Data
• Market Basket Analytics

Attributes