Ecommerce Purchases Dataset This dataset contains various information about purchases from an ecommerce website.

This dataset contains 12,500+ dataset was constructed to reflect realistic patterns observed in UK online retail platforms collected between January 2022 and July 2024. It includes session-level metrics, purchase outcomes, and user characteristics consistent with typical industry analytics. It was compiled as part of Project22–2024, a market research initiative focused on understanding online consumer behaviour, digital conversion patterns, and purchasing intent across UK regions.

Each record represents a single user session, capturing a wide range of behavioral signals (session activity, product interaction, exit patterns) alongside contextual variables (device type, region, payment preference, GDPR consent).

This dataset is ideal for research in e-commerce analytics, machine learning, behavioral segmentation, and marketing strategy.

The value of purchases of Italian consumers on e-commerce platforms increased almost ******* from 2018 to 2022. In fact, the value of online purchases amounted to **** billion euros in 2018 and is forecast to reach **** billion euros in 2023. What do people like to buy online? Among the most popular products and services to buy online, Italians seem to prefer travel-related purchases, books and music, and clothing.

 Mobile e-commerce on the rise  

The preferred device for online shopping in Italy is a desktop computer. In fact, online purchases finalized via desktop account for ** percent of the total online purchases, while smartphones and tablets account for ** and * percent respectively. However, despite desktop devices still being preferred for online shopping, mobile devices are catching up fast. For example, purchases via smartphone increased ** percent from 2015 to 2019.

 E-commerce is growing in Italy  

E-commerce turnover in Italy reached **** billion euros in 2019, increasing by almost ** percent compared to the previous year. Moreover, the number of e-commerce users is also quite impressive and is expected to increase further in the following years. These figures are evidence that e-commerce has become more widespread in Italy, even though it is not as widespread as in other European countries such as the UK.

Dataset

This dataset was created by Lakshmi Sayyapureddy

Released under CC0: Public Domain

Contents

Most payments for e-commerce purchases were made digitally in Poland in 2019. The most commonly used payment methods were: bank transfer (** percent), credit card (** percent), and e-wallet (** percent). In 2019 ** percent of e-commerce purchases were paid in cash.

Graph and download economic data for E-Commerce Retail Sales as a Percent of Total Sales (ECOMPCTSA) from Q4 1999 to Q3 2025 about e-commerce, retail trade, sales, retail, percent, and USA.

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

According to a 2024 survey, goods for internal usage accounted for around ********* of B2B buyers' last online purchases in Denmark. The second most popular category of goods purchased by B2B buyers was industrial goods (** percent).

E-Commerce Transactions: AOV: Hobbies & Leisure: Models data was reported at 214.007 USD in 13 Feb 2026. This records a decrease from the previous number of 215.832 USD for 12 Feb 2026. E-Commerce Transactions: AOV: Hobbies & Leisure: Models data is updated daily, averaging 166.565 USD from Dec 2018 (Median) to 13 Feb 2026, with 2554 observations. The data reached an all-time high of 415.405 USD in 26 Aug 2022 and a record low of 79.290 USD in 05 Sep 2020. E-Commerce Transactions: AOV: Hobbies & Leisure: Models data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s United States – Table US.GI.EC: E-Commerce Transactions: by Category.

In 2024, global retail e-commerce sales reached an estimated ************ U.S. dollars. Projections indicate a ** percent growth in this figure over the coming years, with expectations to come close to ************** dollars by 2028. World players Among the key players on the world stage, the American marketplace giant Amazon holds the title of the largest e-commerce player globally, with a gross merchandise value of nearly *********** U.S. dollars in 2024. Amazon was also the most valuable retail brand globally, followed by mostly American competitors such as Walmart and the Home Depot. Leading e-tailing regions E-commerce is a dormant channel globally, but nowhere has it been as successful as in Asia. In 2024, the e-commerce revenue in that continent alone was measured at nearly ************ U.S. dollars, outperforming the Americas and Europe. That year, the up-and-coming e-commerce markets also centered around Asia. The Philippines and India stood out as the swiftest-growing e-commerce markets based on online sales, anticipating a growth rate surpassing ** percent.

About

This file contains purchase data from December 2018 to December 2021 (3 years) from a medium sized jewelry online store.

Each row in the file represents a purchased product. Several products from the same order/purchase are listed in separate lines and joined by order_id field.

Data collected by Open CDP project. Feel free to use open source customer data platform.

More datasets

Checkout another datasets:

1. https://www.kaggle.com/mkechinov/ecommerce-behavior-data-from-multi-category-store
2. https://www.kaggle.com/mkechinov/ecommerce-purchase-history-from-electronics-store
3. https://www.kaggle.com/mkechinov/ecommerce-events-history-in-cosmetics-shop
4. https://www.kaggle.com/mkechinov/ecommerce-purchase-history-from-jewelry-store - you're reading it right now
5. https://www.kaggle.com/mkechinov/ecommerce-events-history-in-electronics-store
6. [NEW] https://www.kaggle.com/datasets/mkechinov/direct-messaging

Many thanks

Thanks to REES46 Marketing Platform for this dataset.

Using datasets in your works, books, education materials

You can use this dataset for free. Just mention the source of it: link to this page and link to REES46 Marketing Platform.

CE Vision USA is the premier merchant attributable data set tracking consumer spend on credit and debit cards. Private investors and corporate clients use CE Vision to track market share, uncover customer insights, compare shopping patterns by demo and geo, and analyze market dynamics.

E-Commerce Transactions: AOV: E-Commerce & Shopping: E-Commerce & Shopping data was reported at 59.871 USD in 01 Feb 2026. This records a decrease from the previous number of 60.997 USD for 25 Jan 2026. E-Commerce Transactions: AOV: E-Commerce & Shopping: E-Commerce & Shopping data is updated daily, averaging 141.964 USD from Dec 2018 (Median) to 01 Feb 2026, with 2327 observations. The data reached an all-time high of 287.259 USD in 14 Jul 2021 and a record low of 36.379 USD in 13 Dec 2025. E-Commerce Transactions: AOV: E-Commerce & Shopping: E-Commerce & Shopping data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s United States – Table US.GI.EC: E-Commerce Transactions: by Category.

E-Commerce Transactions: AOV: E-Commerce & Shopping: Marketplace data was reported at 186.880 USD in 04 Jan 2026. This records a decrease from the previous number of 191.920 USD for 28 Dec 2025. E-Commerce Transactions: AOV: E-Commerce & Shopping: Marketplace data is updated daily, averaging 242.385 USD from Dec 2018 (Median) to 04 Jan 2026, with 2100 observations. The data reached an all-time high of 541.608 USD in 28 Jan 2023 and a record low of 56.360 USD in 07 Nov 2021. E-Commerce Transactions: AOV: E-Commerce & Shopping: Marketplace data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s United States – Table US.GI.EC: E-Commerce Transactions: by Category.

The "Ecommerce Purchases" dataset encompasses a variety of information related to online transactions. Key columns include:

1. Address: The customer's address for shipping and billing.
2. Lot: A unique identifier for the purchase lot.
3. AM or PM: Denotes whether the purchase was made in the morning or evening.
4. Browser Info: Information about the web browser used during the transaction.
5. Company: The company associated with the purchase.
6. Credit Card: The credit card number used for the transaction.
7. CC Exp Date: The expiration date of the credit card.
8. CC Security Code: The security code associated with the credit card.
9. CC Provider: The provider or issuer of the credit card.
10. Email: The customer's email address.
11. Job: The customer's occupation or job title.
12. IP Address: The IP address associated with the transaction.
13. Language: The language preference of the customer.
14. Purchase Price: The amount spent on the purchase.

This dataset provides a comprehensive view of ecommerce transactions, enabling analysis of customer demographics, purchase patterns, and other valuable insights for business intelligence and research.

In 2021, credit and debit cards accounted for over ** percent of online purchases in Guatemala. The remaining percentage of e-commerce transactions in the Central American country involved bank transfers, e-wallets, and other payment types.

As of July 2019, the purchase conversion rate on China's e-commerce platforms varied from **** percent to ** percent. The top e-commerce influencers could generate a ** percent purchase conversion rate, much higher than social and traditional e-commerce providers in the Chinese market.

E-Commerce Transactions: AOV: E-Commerce & Shopping data was reported at 91.984 USD in 13 Feb 2026. This records an increase from the previous number of 88.065 USD for 12 Feb 2026. E-Commerce Transactions: AOV: E-Commerce & Shopping data is updated daily, averaging 114.444 USD from Dec 2018 (Median) to 13 Feb 2026, with 2582 observations. The data reached an all-time high of 284.323 USD in 14 Jul 2021 and a record low of 30.085 USD in 03 Dec 2025. E-Commerce Transactions: AOV: E-Commerce & Shopping data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s United States – Table US.GI.EC: E-Commerce Transactions: by Category.

Dataset

This dataset was created by Mauyon Friday Senapon

Contents

Internet sales have played an increasingly significant role in retailing. In 2025, e-commerce accounted for over ***percent of retail sales worldwide. Forecasts indicate that by 2030, the online segment will make up ***percent of total global retail sales. Retail e-commerce Online shopping has grown steadily in popularity in recent years. In 2024, global e-commerce sales amounted to over ************ U.S. dollars, a figure expected to approach * trillion U.S. dollars by 2030. Digital development boomed during the COVID-19 pandemic, generating unprecedented e-commerce growth in various economies across the globe. This trend correlates strongly with the constantly improving online access, especially in "mobile-first" online communities, which have long struggled with traditional commercial fixed broadband connections due to financial or infrastructure constraints but enjoy the advantages of cheap mobile broadband connections. M-commerce on the rise The order share of online shopping via smartphones and tablets now outperforms traditional e-commerce via desktop computers. As such, e-retailers around the world have caught up in mobile e-commerce sales. Online shopping via smartphones is particularly prominent in Asia. By the end of 2023, South Korea was the top digital market based on the percentage of the population that had purchased something by phone, with nearly ** percent having made a weekly mobile purchase. Malaysia, UAE, and Turkey completed the top of the ranking.