Superstore E-commerce Data

Columns

• Row ID: A unique identifier for each record.
• Order ID: A unique identifier for each order, with 5,009 distinct order IDs.
• Order Date: The date when an order was placed, with entries ranging from 3rd January 2014 to 30th December 2017.
• Ship Date: The date an order was shipped, with entries from 7th January 2014 to 5th January 2018.
• Ship Mode: The method of shipment used, featuring four unique modes, with Standard Class being the most prevalent (60%).
• Customer ID: A unique identifier for each customer, with 793 distinct customer IDs.
• Customer Name: The name of the customer, corresponding to the 793 unique customer IDs.
• Segment: The customer's segment, categorised into three unique groups, predominantly Consumer (52%) and Corporate (30%).
• Country: The country where the transactions occurred, exclusively the United States.
• City: The city of the customer, covering 531 unique cities, including New York City (9%) and Los Angeles (7%).
• State: The state of the customer, spanning 49 unique states, with California (20%) and New York (11%) being the most frequent.
• Postal Code: The postal code for the customer's location.
• Region: The geographic region within the US, divided into four unique regions, notably West (32%) and East (28%).
• Product ID: A unique identifier for each product, with 1,862 distinct product IDs.
• Category: The main product category, with three unique categories, primarily Office Supplies (60%) and Furniture (21%).
• Sub-Category: The sub-category of the product, featuring 17 unique sub-categories, such as Binders (15%) and Paper (14%).
• Product Name: The specific name of the product, with 1,850 distinct product names.
• Sales: The sales amount for the item, ranging from £0.44 to £22,600, with an average of £230 per transaction.
• Quantity: The quantity of the product ordered, ranging from 1 to 14 units, with an average of 3.79 units.
• Discount: The discount applied to the product, ranging from 0 to 0.8, with an average discount of 0.16.
• Profit: The profit generated from the sale, which can range from a loss of £6,600 to a profit of £8,400, with an average profit of £28.7.

Distribution

Usage

Coverage

License

Who Can Use It

• Business Analysts for sales forecasting and performance evaluation.
• Data Scientists for developing predictive models for customer behaviour and market trends.
• Researchers studying retail dynamics and e-commerce growth.
• Students learning data analysis and visualisation techniques in a business context.
• Anyone interested in performing customer segmentation, cluster analysis, or sales and profit analysis.

Dataset Name Suggestions

• Superstore E-commerce Data
• Retail Sales and Customer Analytics
• US Superstore Transactions
• E-commerce Order Data

Attributes