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 is estimated to reach just below *** billion British pounds in 2021. In 2022, the figure decreased to *** billion British pounds. 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.

Internet sales in Great Britain by store type, month and year.

In March 2025, the share of total retail sales of textile products, clothes, and shoes made online in Great Britain amounted to ** percent. A peak was reached in February 2021, when online sales reached about ** percent of total retail sales of textile products, clothes, and shoes in Great Britain.

Online retail spending in the United Kingdom (UK) never ceased to increase from 2011 to 2021. In 2021, online retail sales amounted to approximately *** billion British pounds. In 2022, inflation contributed to a sharp decline of online retail sales plummeting **** percent. As a result, online retail sales had a value of *** billion British pounds that year. In 2023, online retail spending stood at *** billion British pounds.

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

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...

Goals :

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description).

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.

4. Geographical Analysis:

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

5. Sales Performance Dashboard:

To track the sales performance of the online retail company, a sales performance dashboard can be created. This dashboard can include key metrics such as total sales, sales by product category, sales by customer segment, and sales by geographical location. By visualizing the sales data in an interactive dashboard, it becomes easier to identify trends, patterns, and areas for improvement.

Research Ideas ****:

1. Inventory Management: By analyzing the quantity and frequency of product sales, retailers can effectively manage their stock and predict future demand. This would help ensure that popular items are always available while less popular items aren't overstocked.
2. Customer Segmentation: Data from different countries can be used to understand buying habits across different geographical locations. This will allow the retail company to tailor its marketing strategy for each specific region or country, leading to more effective advertising campaigns.
3. Sales Trend Analysis: With data spanning almost a year, temporal patterns in purchasing behavior can be identified, including seasonality and other trends (like an increase in sales during holidays). Techniques like time-series analysis could provide insights into peak shopping times or days of the week when sales are typically high.
4. Predictive Analysis for Cross-Selling & Upselling: Based on a customer's previous purchase history, predictive algorithms can be utilized to suggest related products that might interest the customer, enhancing upsell and cross-sell opportunities.
5. Detecting Fraud: Analysing sale returns (marked with 'c' in InvoiceNo) across customers or regions could help pinpoint fraudulent activities or operational issues leading to those returns
6. RFM Analysis: By using the RFM (Recency, Frequency, Monetary) segmentation technique, the online retail company can gain insights into customer behavior and tailor their marketing strategies accordingly.

**************Steps :**************

1. Data manipulation and cleaning from raw data using SQL language Google Big Query
2. Data filtering, grouping, and slicing
3. Data Visualization using Tableau
4. Data visualization analysis and result

In 2021, the United Kingdom (UK) recorded an estimated *** billion British pounds in e-commerce retail sales. Of this value, over ** percent was first-party sales, and the other ** percent was third-party. Online sales in both business models were forecast to keep growing in the European country in the coming years.

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.

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).

The UK e-commerce market, a significant player in the global landscape, exhibits robust growth potential. With a 2025 market size estimated at £280.55 million (based on the provided global value and adjusting for the UK's share of the global market—a reasonable assumption considering the UK's advanced digital infrastructure and consumer behavior), the sector is projected to maintain a strong Compound Annual Growth Rate (CAGR) of approximately 21.76%. This growth is fueled by several key drivers. Increased internet penetration and smartphone usage continue to expand the addressable market, providing convenient access to online shopping for a broader demographic. The rising popularity of online marketplaces like Amazon and eBay, coupled with the aggressive expansion of omnichannel strategies by traditional retailers, fosters competition and innovation, ultimately benefiting consumers with greater choice and value. Furthermore, evolving consumer preferences towards convenience, personalized experiences, and seamless delivery options further fuel this expansion. However, challenges remain. While the market enjoys high growth, potential restraints include concerns surrounding data privacy and security, increasing competition, and the fluctuating economic climate. Specifically, the segments driving growth include fashion and apparel, beauty and personal care, and consumer electronics. These sectors benefit from strong online presence, visual merchandising opportunities, and the ability to target specific demographics effectively. The B2B e-commerce sector is also anticipated to experience considerable growth, fueled by the increasing adoption of digital procurement solutions by businesses. Key players like Amazon, eBay, Asos, and others are aggressively vying for market share, utilizing advanced technologies and strategic partnerships to consolidate their positions and capture opportunities within the diverse segments of the UK e-commerce landscape. Geographical distribution within the UK itself displays strong regional variations, with London and other major urban centers exhibiting higher penetration rates compared to rural areas. This necessitates tailored strategies and targeted investment for sustained market expansion. Recent developments include: May 2024 - Metapack, a prominent player in e-commerce delivery technology, announced that Mountain Warehouse, a significant outdoor clothing company in the United Kingdom, extended its partnership with Metapack to enhance its delivery capabilities and support its growth strategy. Already utilizing Metapack Delivery Manager, Mountain Warehouse incorporated Metapack’s Delivery Options and Metapack Intelligence solutions into its operations. This investment in shipping infrastructure aims to deliver superior e-commerce experiences to customers across the United Kingdom and Canada., March 2024 - Kin + Carta, a global player in digital transformation consulting, introduced an advanced generative AI and large language model (LLM) tool for the UK retailer Matalan. This innovative tool enables Matalan to efficiently generate comprehensive product descriptions for new items as they are added to its online inventory.. Key drivers for this market are: Increase Developments of 5G Technology, Increased Adoption of Online Payments. Potential restraints include: Increase Developments of 5G Technology, Increased Adoption of Online Payments. Notable trends are: Innovations in 5G Technology is Driving the Market Growth.

According to the source, the online share of retail sales in the United Kingdom (UK) was estimated to be **** percent by the end of 2022. This figure is down from 2021, where online sales accounted for **** percent of the online retail industry.

A series of retail sales data for Great Britain in value and volume terms, seasonally and non-seasonally adjusted.

InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice Date and time. Numeric, the day and time when each transaction was generated. UnitPrice: Unit price. Numeric, Product price per unit in sterling. CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal, the name of the country where each customer resides.

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.

The report covers UK Online Retail Debit Card Payment Market, Total Number of Retail Categories UK Online Retail Market.t, UK Online Retail Pet Care Market, UK Online Retail Consumer Electronics Market.

Dataset Description: Online Retail Dataset

Overview:😊 The Online Retail dataset contains transactional data for a UK-based online retail company. The dataset includes details of orders made from various countries between 2010 and 2011. It is useful for exploring purchase behaviors, sales patterns, and customer segmentation.

Attributes: InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country

Potential Uses:

Sales Analysis:✅ Analyzing sales trends over time, identifying best-selling products, and understanding sales performance across different regions.

Customer Segmentation:✅ Segmenting customers based on purchasing behavior, frequency, and monetary value to tailor marketing strategies.

Inventory Management:✅ Monitoring stock levels and predicting future inventory needs based on sales patterns.

Market Basket Analysis:✅ Identifying products that are frequently bought together to improve cross-selling strategies. 🎯

License:

Please specify the appropriate license (e.g., Apache 2.0 or MIT) when uploading the dataset to ensure clear usage guidelines for other users.

In March 2025, internet sales accounted for 26.8 percent of all retail sales in Great Britain. Over the considered period, food online sales did not go over nine percent of total retail sales.

The online revenue of next.co.uk amounted to US$2,418.2m in 2024. Discover eCommerce insights, including sales development, shopping cart size, and many more.

The E-Commerce and Online Auctions industry has undergone considerable change over the years, switching from focusing on mail orders and direct TV and telephone sales to e-commerce, driven by the expansion and accessibility of internet services. The increasingly integrated nature of the internet and smartphones in everyday life has been pivotal in developing mobile shopping apps and driving growth. The growth of the internet has also supported online retailers’ reach; e-tailers get strong benefits from using social media platforms like Facebook, Instagram and TikTok to build traffic effectively at low costs. Similarly, combining with tools like Google Ads allows businesses to place targeted adverts based on keyword searches, helping boost product visibility in search engines and potentially leading to higher sales. Over the five years through 2025-26, e-commerce revenue is expected to expand at a compound annual rate of 2.3% to reach £64.9 billion. E-tailers have taken off by leveraging cost advantages and offering competitive prices to an increasingly price-conscious consumer base. Expanding value-added services like monthly finance options and flexible and fast delivery methods have contributed to double-digit growth in some years. However, there have still been challenges. The cost-of-living crisis dented sales volumes in 2022-23 and contrained growth in 2023-24. However, rebounding disposable incomes will help to return the industry to growth, with revenue projected to expand by 0.8 % in 2025-26. Over the five years through 2030-31, revenue is expected to climb at a compound annual rate of 1.4% to reach £69.5 billion. The rapid growth of m-commerce is set to continue, driven by increasing smartphone use and better internet access, especially as the 2025 Spring Statement committed £1.2 billion into improving broadband speed and 5G network coverage through Project Gigabit. Businesses that adapt quickly to customers' preferences for convenient mobile shopping are likely to be the most competitive. Retailers could also look to improve their logistical options, including through the use of drones to reach more customers in remote areas. Lower wage costs from greater automation and AI are likely to support profitability.