Online retail in the United Kingdom has been gaining ground in the past decade. With the onset of the coronavirus (COVID-19) crisis, the value of online retail sales in the United Kingdom peaked at around 129.5 billion British pounds in 2021. In 2022, the figure decreased to ***** billion British pounds. However, they then went through another recovery and achieved around ***** billion British pounds in 2024. What ranks high in UK e-commerce? In the United Kingdom, clothing and household goods were the most popular retail items consumers purchased through the internet in 2020. Data published by the Office for National Statistics (UK) showed that other leisure activities and services such as booking holiday accommodations, travel arrangements and event tickets were other areas consumers depended on the internet to buy. German e-commerce market The UK might have the highest share of online sales in retail trade, but other European countries such as Germany and France have had impressive track records over the years as well. According to the forecasts provided by German E-commerce and Distance Selling Trade Association (bevh), the market volume of Germany’s e-commerce sector was projected to see over ** billion euros in 2021.

Context

Typically e-commerce datasets are proprietary and consequently hard to find among publicly available data. However, The UCI Machine Learning Repository has made this dataset containing actual transactions from 2010 and 2011. The dataset is maintained on their site, where it can be found by the title "Online Retail".

Content

"This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers."

Acknowledgements

Per the UCI Machine Learning Repository, this data was made available by Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Image from stocksnap.io.

Inspiration

Analyses for this dataset could include time series, clustering, classification and more.

In March 2025, the value of internet sales as a percentage of total retail sales in Great Britain amounted to 26.3 percent. This was a slight increase compared with the previous month, when online retail sales accounted for 25.9 percent of total retail sales.

Use of information and communication technology (ICT) and e-commerce activity by UK businesses. Annual data on e-commerce sales and how businesses are using the internet.

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

What does the online shopper want? The answer for UK consumers was price comparisons, according to results of a consumer survey conducted each year from 2018 to 2020. In each of these years, those surveyed predominantly cited price comparison and greater variety as the two leading motivations to do their shopping online. Most popular items purchased online The latest reports published by the Office for National Statistics (UK) also suggested that, for both British men and women alike, clothing and sports goods were the number one items that shoppers purchased online in 2020. Ranking second that year, deliveries from restaurants, fast-food chains or catering services were ordered by approximately a ***** of consumers in Great Britain. UK: Europe’s leading e-commerce market UK’s online shoppers drive the sovereign country’s retail sector. Currently, the share of online sales as a proportion to all retail trade makes the United Kingdom the leading e-commerce market in Europe. Forecasts indicate that the total value of online sales in the UK will have reached approximately ********* British pounds in 2020.

Context

E-commerce has become a new channel to support businesses development. Through e-commerce, businesses can get access and establish a wider market presence by providing cheaper and more efficient distribution channels for their products or services. E-commerce has also changed the way people shop and consume products and services. Many people are turning to their computers or smart devices to order goods, which can easily be delivered to their homes.

Content

This is a sales transaction data set of UK-based e-commerce (online retail) for one year. This London-based shop has been selling gifts and homewares for adults and children through the website since 2007. Their customers come from all over the world and usually make direct purchases for themselves. There are also small businesses that buy in bulk and sell to other customers through retail outlet channels.

The data set contains 500K rows and 8 columns. The following is the description of each column. 1. TransactionNo (categorical): a six-digit unique number that defines each transaction. The letter “C” in the code indicates a cancellation. 2. Date (numeric): the date when each transaction was generated. 3. ProductNo (categorical): a five or six-digit unique character used to identify a specific product. 4. Product (categorical): product/item name. 5. Price (numeric): the price of each product per unit in pound sterling (£). 6. Quantity (numeric): the quantity of each product per transaction. Negative values related to cancelled transactions. 7. CustomerNo (categorical): a five-digit unique number that defines each customer. 8. Country (categorical): name of the country where the customer resides.

There is a small percentage of order cancellation in the data set. Most of these cancellations were due to out-of-stock conditions on some products. Under this situation, customers tend to cancel an order as they want all products delivered all at once.

Inspiration

Information is a main asset of businesses nowadays. The success of a business in a competitive environment depends on its ability to acquire, store, and utilize information. Data is one of the main sources of information. Therefore, data analysis is an important activity for acquiring new and useful information. Analyze this dataset and try to answer the following questions. 1. How was the sales trend over the months? 2. What are the most frequently purchased products? 3. How many products does the customer purchase in each transaction? 4. What are the most profitable segment customers? 5. Based on your findings, what strategy could you recommend to the business to gain more profit?

Photo by CardMapr on Unsplash

Context

In the field of e-commerce, the datasets are typically considered as proprietary, meaning they are owned and controlled by individual organizations and are not often made publicly available due to privacy and business considerations. In spite of this, The UCI Machine Learning Repository, known for its extensive collection of datasets beneficial for machine learning and data mining research, has curated and made accessible a unique dataset. This dataset comprises actual transactional data spanning from the year 2010 to 2011. For those interested, the dataset is maintained and readily available on the UCI Machine Learning Repository's site under the title "Online Retail".

Content

The dataset is a transnational one, capturing every transaction made from December 1, 2010, through December 9, 2011, by a UK-based non-store online retail company. As an online retail entity, the company doesn't have a physical store presence, and its operations and sales are conducted purely online. The company's primary product offering includes unique gifts for all occasions. While the company serves a diverse range of customers, a significant number of its clientele includes wholesalers.

Acknowledgements

In collaboration with the UCI Machine Learning Repository, the dataset was provided and made available by Dr. Daqing Chen. Dr. Chen is the Director of the Public Analytics group at London South Bank University, UK. Any correspondence regarding this dataset can be sent to Dr. Chen at 'chend' at 'lsbu.ac.uk'. We are grateful to him for providing such an invaluable resource for researchers and data science enthusiasts.

The image used has been sourced from Canva

Inspiration

The rich and extensive data within this dataset opens the door for a multitude of potential analyses. It lends itself well to various methods and techniques in data science, including but not limited to time series analysis, clustering, and classification. By exploring this dataset, one could derive key insights into customer behavior, transaction trends, and product performance, providing ample opportunities for deep and insightful explorations.

When asked about "Attitudes towards online shopping", most UK respondents pick ******************************************************************************* as an answer. ** percent did so in our online survey in 2025. Looking to gain valuable insights about customers of online shops across the globe? Check out our reports about consumers of online shops worldwide. These reports offer the readers a comprehensive overview of customers of eCommerce brands: who they are; what they like; what they think; and how to reach them.

Descriptions and categories of the Internet Sales Index and their percentage of all retailing for Great Britain.

The pie chart showcases the distribution of app/software spending by store category in United Kingdom, providing insights into how eCommerce stores allocate their resources on the app or software they utilize. Among the store categories, Apparel exhibits the highest spending, with a total expenditure of $63.97M units representing 23.91% of the overall spending. Following closely behind is Beauty & Fitness with a spend of $40.58M units, comprising 15.17% of the total. Home & Garden also contributes significantly with a spend of $32.14M units, accounting for 12.01% of the overall app/software spending. This data sheds light on the investment patterns of eCommerce stores within each category, reflecting their priorities and resource allocation towards app or software solutions.

Context

This is a transactional data set containing the transactions for a UK-based non-store online retailer in various categories. The company mainly sells unique all-occasion gifts with mostly wholesales customers.

UK E-Commerce Market is Segmented by Business Model (B2C, B2B), Device Type (Smartphone / Mobile, Desktop and Laptop, and More), Payment Method (Credit / Debit Cards, Digital Wallets, and More), B2C Product Category (Beauty and Personal Care, Consumer Electronics, Fashion and Apparel, Food and Beverages, and More). The Market Forecasts are Provided in Terms of Value (USD).

ZARA UK Fashion Dataset offers an extensive collection of fashion product data from ZARA's UK online store, providing a detailed overview of available items. This dataset is valuable for analyzing the European fashion retail market, particularly in the UK, and includes fields such as product titles, URLs, SKUs, MPNs, brands, prices, currency, images, breadcrumbs, country, availability, unique IDs, and timestamps for when the data was scraped.

Key Features:

• Product Details: Includes title, URL, SKU (Stock Keeping Unit), MPN (Manufacturer Part Number), and brand for each product, helping to uniquely identify and differentiate items.
• Pricing Information: Features the price of each product along with the currency used (GBP) to understand the pricing strategies of ZARA in the UK market.
• Visual Data: High-quality images of each product, essential for visual merchandising analysis and online consumer behavior studies.
• Categorical Information: Breadcrumbs data provide context on the product's placement within ZARA's website structure, helping to analyze navigation and product hierarchy.
• Geographical Focus: Specific to the UK market, making it relevant for studies on British fashion retail and consumer trends.
• Availability Status: Includes real-time availability data, which is crucial for understanding stock levels, popular products, and restocking practices.
• Unique Identifiers: Each product is tagged with a uniq_id, ensuring data integrity and making it easier to track and analyze over time.
• Data Collection Timestamp: The scraped_at field records the exact time and date when the data was collected, aiding in time-based analysis of inventory and pricing.

Potential Use Cases:

• Market Research: Analyze UK and European fashion trends, consumer preferences, and competitive positioning within the fast fashion sector.
• E-commerce Analysis: Study ZARA's product placement, pricing, and availability to optimize online retail strategies.
• Stock Management: Use SKU and availability data to predict inventory needs and enhance supply chain efficiency.
• Brand Analysis: Examine the impact of brand identity on consumer choices and product performance in the UK market.
• Academic Research: Ideal for research projects focused on fashion retail, marketing strategies, and consumer behavior in Europe.

Data Sources:

The data is meticulously collected from ZARA's official UK website and other reliable retail databases, reflecting the latest product offerings and market dynamics specific to the UK and European fashion markets.

• ZARA US Retail Products Dataset: Explore over 10,000 product records from ZARA's USA online store, including titles, prices, images, and availability.
  
  
• Fashion Products Dataset from GAP.com: Access detailed product information from GAP's online store, featuring over 4,500 fashion items with attributes like price, brand, color, reviews, and images.
  
  
• Myntra Fashion Products Dataset: A comprehensive dataset from Myntra.com, offering over 12,000 fashion products with detailed attributes for in-depth analysis.

This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

To be noted that this dataset was taken from UCI.

CITATION Daqing Chen, Sai Liang Sain, and Kun Guo, Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197–208, 2012 (Published online before print: 27 August 2012. doi: 10.1057/dbm.2012.17).

Over the past pandemic year, a significant number of consumers in the United Kingdom (UK) have reported changes in their online shopping behavior. Specifically, in March 2020, about ** percent of UK shoppers said they had been shopping more online, compared to before the coronavirus (COVID-19) pandemic. By February 2021, however, this percentage had grown to approximately ** percent. By the same token, offline shopping has decreased over the analyzed period.

In United Kingdom, the estimated sales amount across various store categories provides key insights into the market's dynamics. Food & Drink, as a prominent category, generates significant sales, totaling $263.02B, which is 27.52% of the region's total sales in this sector. Home & Garden follows with robust sales figures, achieving $258.38B in sales and comprising 27.03% of the region's total. Health contributes a considerable amount to the regional market, with sales of $111.47B, accounting for 11.66% of the total sales in United Kingdom. This breakdown highlights the varying economic impacts of different categories within the region, showcasing the diversity and strengths of each sector.

During a late 2023 survey carried out among working-age consumers from the United Kingdom (UK), roughly ** percent of respondents stated they started their online shopping journey on search engines. Approximately ** percent said they started their journeys on marketplaces.

ExactOne delivers unparalleled consumer transaction insights to help investors and corporate clients uncover market opportunities, analyze trends, and drive better decisions.

Dataset Highlights - Source: Debit and credit card transactions from 600K+ active users and 2M accounts connected via Open Banking. Scale: Covers 250M+ annual transactions, mapped to 1,800+ merchants and 330+ tickers. Historical Depth: Over 6 years of transaction data. Flexibility: Analyse transactions by merchant/ticker, category/industry, or timeframe (daily, weekly, monthly, or quarterly).

ExactOne data offers visibility into key consumer industries, including: Airlines - Regional / Budget Airlines - Cargo Airlines - Full Service Autos - OEMs Communication Services - Cable & Satellite Communication Services - Integrated Telecommunications Communication Services - Wireless Telecom Consumer - Services Consumer - Health & Fitness Consumer Staples - Household Supplies Energy - Utilities Energy - Integrated Oil & Gas Financial Services - Insurance Grocers - Traditional Hotels - C-corp Industrial - Tools And Hardware Internet - E-commerce Internet - B2B Services Internet - Ride Hailing & Delivery Leisure - Online Gambling Media - Digital Subscription Real Estate - Brokerage Restaurants - Quick Service Restaurants - Fast Casual Restaurants - Pubs Restaurants - Specialty Retail - Softlines Retail - Mass Merchants Retail - European Luxury Retail - Specialty Retail - Sports & Athletics Retail - Footwear Retail - Dept Stores Retail - Luxury Retail - Convenience Stores Retail - Hardlines Technology - Enterprise Software Technology - Electronics & Appliances Technology - Computer Hardware Utilities - Water Utilities

Use Cases

For Private Equity & Venture Capital Firms: - Deal Sourcing: Identify high-growth opportunities. - Due Diligence: Leverage transaction data to evaluate investment potential. - Portfolio Monitoring: Track performance post-investment with real-time data.

For Consumer Insights & Strategy Teams: - Market Dynamics: Compare sales trends, average transaction size, and customer loyalty. - Competitive Analysis: Benchmark market share and identify emerging competitors. - E-commerce vs. Brick & Mortar Trends: Assess channel performance and strategic opportunities. - Demographic & Geographic Insights: Uncover growth drivers by demo and geo segments.

For Investor Relations Teams: - Shareholder Insights: Monitor brand performance relative to competitors. - Real-Time Intelligence: Analyse sales and market dynamics for public and private companies. - M&A Opportunities: Evaluate market share and growth potential for strategic investments.

Key Benefits of ExactOne - Understand Market Share: Benchmark against competitors and uncover emerging players. - Analyse Customer Loyalty: Evaluate repeat purchase behavior and retention rates. - Track Growth Trends: Identify key drivers of sales by geography, demographic, and channel. - Granular Insights: Drill into transaction-level data or aggregated summaries for in-depth analysis.

With ExactOne, investors and corporate leaders gain actionable, real-time insights into consumer behaviour and market dynamics, enabling smarter decisions and sustained growth.

The main driver for online shopping activity in the United Kingdom was free delivery, with ** percent of digital buyers in the country reporting so. Another driver to purchase products online was having no hidden fees. The rise of e-commerce in the UK with fashion leading the way Already a part of daily life for millions of consumers in the UK, the popularity of online shopping only increased considerably after the COVID-19 pandemic. In the United Kingdom, e-commerce generated *** billion U.S. dollars in revenue in 2023; revenue figures are forecast to grow in the coming years, reaching a total of *** billion by 2029. The leading e-commerce industry was the fashion segment, accounting for a revenue of around ** billion U.S. dollars. Tesco leads UK's e-commerce food sector Aside from fashion, the food sector also stands out when analyzing e-commerce in the United Kingdom. Food e-commerce generated ***** billion U.S. dollars in revenue in 2023. Tesco, the popular British grocery store which started its online activities in the 2000s, was the leading online grocery store in the country in 2023, generating net sales of around **** billion U.S. dollars.