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Historical E-Commerce Sales and Market Basket Analysis
Retail & Consumer Behavior
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About
Analysing global e-commerce activity through historical sales records offers a window into consumer behaviour and market dynamics during the early 2010s. By examining over half a million individual transactions, stakeholders can identify purchasing patterns, seasonal fluctuations, and product performance metrics. This resource provides a detailed account of retail operations, capturing essential variables such as unit pricing and geographical distribution to assist in developing robust business strategies and understanding market basket trends. The records allow for a thorough exploration of how specific stock items perform across different international regions, particularly focusing on the intersection of product demand and customer location.
Columns
- Invoice: A unique identifier for each transaction or bill, used to group items purchased together.
- StockCode: A specific alphanumeric code assigned to each unique product in the inventory for tracking purposes.
- Description: A textual label providing the name or a brief detail of the item sold.
- Quantity: The numerical count of items purchased per transaction, including negative values representing returns or cancellations.
- InvoiceDate: The precise date and time when the transaction was processed, covering a period between late 2009 and late 2010.
- Price: The cost per unit of the item, recorded in a numerical format.
- Customer ID: A unique numerical code assigned to individual shoppers to monitor loyalty and purchasing history.
- Country: The name of the nation where the customer resides or where the order was placed.
Distribution
The information is provided in a CSV file titled Retail 2009-10.csv with a file size of 44.91 MB. It contains approximately 525,000 valid records structured across 8 distinct columns. The collection maintains high integrity, with core fields such as Invoice, StockCode, and Price showing 100% validity, though approximately 21% of Customer ID entries and 1% of Description entries are missing. As a historical record of specific retail years, no future updates are planned for this collection.
Usage
This resource is ideal for performing market basket analysis to uncover product associations and cross-selling opportunities. It is well-suited for exploratory data analysis aimed at identifying peak sales periods, customer churn rates, and high-value product categories. Additionally, data scientists can utilise these records to build predictive models for customer lifetime value or to segment the user base based on geographical and purchasing characteristics. The presence of return data also allows for the study of reverse logistics and product dissatisfaction patterns.
Coverage
The geographic scope is predominantly focused on the United Kingdom, which represents 92% of the transactions, with EIRE accounting for 2% and the remaining 6% distributed across 38 other unique countries. Temporally, the records span a continuous period from 12 January 2009 through to 11 December 2010. The demographic focus is centered on retail customers, providing a substantial sample size for analysing e-commerce interactions and regional demand shifts over nearly two years.
License
CC0: Public Domain
Who Can Use It
E-commerce analysts can leverage these records to refine pricing strategies and inventory management based on historical performance. Marketing professionals might utilise the customer ID and country data to design targeted regional campaigns or loyalty programmes. Furthermore, students of data science and business intelligence can find this a valuable primary source for practicing intermediate-level data cleaning, visualisation, and categorical analysis on real-world transactional data.
Dataset Name Suggestions
- Online Retail Transactional Data (2009-2011)
- Historical E-Commerce Sales and Market Basket Analysis
- United Kingdom Retail Transactions and Customer Behaviour
- Global Online Store Sales and Product Performance Archive
- Decade-Old Retail Trends: Invoice and Stock Metrics
Attributes
Original Data Source: Historical E-Commerce Sales and Market Basket Analysis
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