As of early 2023, approximately ** percent of consumers in the United States said they would prefer to shop mostly online rather than in-store, making it the country with highest online shopping preference. In contrast, more shoppers preferred visiting physical stores in countries such as Austria, Finland, and New Zealand.

As of June 2025, about ***** million people in China had purchased goods online. This represented a penetration rate of **** percent.E-commerce in ChinaThe past decade has seen rapid growth in the demand for online shopping opportunities in China. The number of online shoppers in China has been increasing exponentially from below ** million in 2006 to over *** million users a decade later, enabling this enormous spurt of China’s e-commerce sector. By 2022, digital buyer penetration rate in China has edged close to ** percent. China has been the world’s second-largest e-tailing market after the U.S. in recent years. As of 2023, the gross merchandise volume of online shopping in China had amounted to around ***** trillion yuan. By 2025, the volume of B2C e-commerce sales in China was expected to surpass *** trillion U.S. dollars. The largest B2C e-commerce retailer in China with regard to gross merchandise volume (GMV) had been Tmall. The B2C online retail platform operated by Alibaba Group had generated a transaction volume of about *** trillion yuan in 2020. The GMV of the leading C2C online retail platform taobao.com, also operated by Alibaba group, had reached almost *** trillion yuan that year.

This dataset is having data of customers who buys clothes online. The store offers in-store style and clothing advice sessions. Customers come in to the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want.

The company is trying to decide whether to focus their efforts on their mobile app experience or their website.

84.5% (10

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.

This dataset contains longitudinal purchases data from 5027 Amazon.com users in the US, spanning 2018 through 2022: amazon-purchases.csv It also includes demographic data and other consumer level variables for each user with data in the dataset. These consumer level variables were collected through an online survey and are included in survey.csv fields.csv describes the columns in the survey.csv file, where fields/survey columns correspond to survey questions. The dataset also contains the survey instrument used to collect the data. More details about the survey questions and possible responses, and the format in which they were presented can be found by viewing the survey instrument. A 'Survey ResponseID' column is present in both the amazon-purchases.csv and survey.csv files. It links a user's survey responses to their Amazon.com purchases. The 'Survey ResponseID' was randomly generated at the time of data collection. amazon-purchases.csv Each row in this file corresponds to an Amazon order. Each such row has the following columns: Survey ResponseID Order date Shipping address state Purchase price per unit Quantity ASIN/ISBN (Product Code) Title Category The data were exported by the Amazon users from Amazon.com and shared by users with their informed consent. PII and other information not listed above were stripped from the data. This processing occurred on users' machines before sharing with researchers.

Percentage of individuals who shopped online and percentage of online shoppers by type of good and service purchased over the Internet during the past 12 months.

Graph and download economic data for E-Commerce Retail Sales (ECOMSA) from Q4 1999 to Q2 2025 about e-commerce, retail trade, sales, retail, and USA.

The dataset "isoc_ec_ibhv" has been discontinued since 08/02/2024.

Source:

Mariusz Šapczyński, Cracow University of Economics, Poland, lapczynm '@' uek.krakow.pl Sylwester Białowąs, Poznan University of Economics and Business, Poland, sylwester.bialowas '@' ue.poznan.pl

Data Set Information:

The dataset contains information on clickstream from online store offering clothing for pregnant women. Data are from five months of 2008 and include, among others, product category, location of the photo on the page, country of origin of the IP address and product price in US dollars.

Attribute Information:

The dataset contains 14 variables described in a separate file (See 'Data set description')

Relevant Papers:

N/A

Citation Request:

If you use this dataset, please cite:

Šapczyński M., Białowąs S. (2013) Discovering Patterns of Users' Behaviour in an E-shop - Comparison of Consumer Buying Behaviours in Poland and Other European Countries, “Studia Ekonomiczne†, nr 151, “La société de l'information : perspective européenne et globale : les usages et les risques d'Internet pour les citoyens et les consommateurs†, p. 144-153

Data description ìe-shop clothing 2008î

Variables:

1. YEAR (2008)

========================================================

2. MONTH -> from April (4) to August (8)

========================================================

3. DAY -> day number of the month

========================================================

4. ORDER -> sequence of clicks during one session

========================================================

5. COUNTRY -> variable indicating the country of origin of the IP address with the

following categories:

1-Australia 2-Austria 3-Belgium 4-British Virgin Islands 5-Cayman Islands 6-Christmas Island 7-Croatia 8-Cyprus 9-Czech Republic 10-Denmark 11-Estonia 12-unidentified 13-Faroe Islands 14-Finland 15-France 16-Germany 17-Greece 18-Hungary 19-Iceland 20-India 21-Ireland 22-Italy 23-Latvia 24-Lithuania 25-Luxembourg 26-Mexico 27-Netherlands 28-Norway 29-Poland 30-Portugal 31-Romania 32-Russia 33-San Marino 34-Slovakia 35-Slovenia 36-Spain 37-Sweden 38-Switzerland 39-Ukraine 40-United Arab Emirates 41-United Kingdom 42-USA 43-biz (.biz) 44-com (.com) 45-int (.int) 46-net (.net) 47-org (*.org)

========================================================

6. SESSION ID -> variable indicating session id (short record)

========================================================

7. PAGE 1 (MAIN CATEGORY) -> concerns the main product category:

1-trousers 2-skirts 3-blouses 4-sale

========================================================

8. PAGE 2 (CLOTHING MODEL) -> contains information about the code for each product

(217 products)

========================================================

9. COLOUR -> colour of product

1-beige 2-black 3-blue 4-brown 5-burgundy 6-gray 7-green 8-navy blue 9-of many colors 10-olive 11-pink 12-red 13-violet 14-white

========================================================

10. LOCATION -> photo location on the page, the screen has been divided into six parts:

1-top left 2-top in the middle 3-top right 4-bottom left 5-bottom in the middle 6-bottom right

========================================================

11. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

========================================================

12. PRICE -> price in US dollars

========================================================

13. PRICE 2 -> variable informing whether the price of a particular product is higher than

the average price for the entire product category

1-yes 2-no

========================================================

14. PAGE -> page number within the e-store website (from 1 to 5)

++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Dataset from Info-communications Media Development Authority. For more information, visit https://data.gov.sg/datasets/d_79958154e6f9d9a30c5655ddd41d83c5/view

Online Retail E-Commerce Data Hey everyone! 👋

This dataset contains real e-commerce transaction data from 2009 to 2011. It comes from a UK-based online store that sells a variety of products. The data includes details like invoices, product codes, descriptions, prices, and even customer IDs.

What’s Inside? Each row represents a transaction, and the dataset has the following key columns: 🛒 Invoice – Unique order ID 📦 StockCode – Product code 📝 Description – Name of the product 📊 Quantity – Number of units sold ⏳ InvoiceDate – When the purchase happened 💰 Price – Unit price of the product 👤 Customer ID – Unique identifier for each customer 🌍 Country – Where the customer is from

Why is this dataset useful? This dataset is great for exploring: Customer Segmentation (Find high-value customers) Customer Lifetime Value (LTV) Analysis Sales & Revenue Trends Market Basket Analysis (Which products are bought together?) Predicting Churn & Retention Strategies

How Can You Use It? If you're into data science, machine learning, or business analytics, this dataset is perfect for hands-on projects. You can analyze customer behavior, predict sales, or even build recommendation systems.

Hope this dataset helps with your projects! Let me know if you find something interesting.

Dataset Card for Online Shoppers Purchasing Intention Dataset

  Dataset Summary

This dataset is a reupload of the Online Shoppers Purchasing Intention Dataset from the UCI Machine Learning Repository.

NOTE: The information below is from the original dataset description from UCI's website.

  Overview

Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples… See the full description on the dataset page: https://huggingface.co/datasets/jlh/uci-shopper.

Snapshot img

The online retail market share in the US is expected to increase to USD 460.13 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 11.64%.

The report extensively covers online retail market in the US segmentation by the following:

Product - Apparel, footwear, and accessories, consumer electronics and electricals, food and grocery, home furniture and furnishing, and others
Device - Smartphones and tablets and PCs

The US online retail market report offers information on several market vendors, including Amazon.com Inc., Apple Inc., Best Buy Co. Inc., Costco Wholesale Corp., eBay Inc., Kroger Co., Target Corp., The Home Depot Inc., Walmart Inc., and Wayfair Inc. among others.

This online retail market in the US research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches.

What will the Online Retail Market Size in the US be During the Forecast Period?

Download the Free Report Sample to Unlock the Online Retail Market Size in the US for the Forecast Period and Other Important Statistics

Online Retail Market in the US: Key Drivers, Trends, and Challenges

The growing seasonal and holiday sales is notably driving the online retail market growth in the US, although factors such as transportation and logistics may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the online retail industry in the US. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key US Online Retail Market Driver

The growing seasonal and holiday sales is one of the key drivers supporting the US online retail market growth. For instance, from November 1 to December 24, e-commerce sales in the US increased by 11% in 2021, when compared to a massive 47.2% growth in the holiday season of 2020. E-commerce sales made up 20.9 % of total retail sales in the holiday season of 2021, slightly higher than 20.6 percent in 2020. Thanksgiving, Black Friday, and Cyber Monday are the days that see a high amount of online shopping. Apparel, footwear and accessories, consumer electronics, computer hardware, and toys are the largest gaining product categories during the holiday season. Consumers in the US spent $204.5 billion online in November and December 2021, up 8.6% over the same period in 2020. Such exciting sales and offers are driving the market growth.

Key US Online Retail Market Trend

Omni-channel retailing is one of the key US online retail market trends fueling the market growth. It is rapidly becoming the norm for many retailers in the US. It offers consumers the option to shop online and pick up the merchandise from the store nearest to their location on the same day. Retailers are observing a high web influence on their in-store sales. For instance, Best Buy is integrating its offline and online stores to boost revenues. As a part of its omnichannel strategy, the retailer is utilizing physical stores as distribution centers for online purchases. According to Best Buy, 40% of its online shoppers prefer picking up their purchases from physical stores. Best Buy also challenges online and discount retailers with its match-to-price strategy, claiming to offer gadgets at or below the price offered by competitors. Such strategies are expected to boost market growth during the forecast period.

Key US Online Retail Market Challenge

Transportation and logistics are some of the factors hindering the US online retail market growth. Product procurement or sourcing, shipment of ordered items, and delivery to customers are the three major processes where the intervention of transportation and logistics come into the picture. All these processes require a high investment of both time and money, which challenges the efficiency and effectiveness of retailers and their costing strategies. The higher cost incurred from transportation and logistics reduces the margin of retailers, and most of the time, retailers are unable to break even. Between rising fuel prices, driver shortages, as well as a governmental and societal push for increased digitization and sustainability, transport and logistics will continue to be under a lot of pressure. Such factors will negatively impact the market growth during the forecast period.

This online retail market in the US analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2022-2026.

Who are the Major Online Retail Market Vendors in the US?

The report analyzes the market’s competitive landscape and offers information on sever

E-commerce adoption in Israel shows significant variation across age groups, with the 25-44 year demographic the most prolific shoppers. In 2023, 69.2 percent of this age group engaged in online shopping in the previous three months, highlighting the strong digital commerce presence among younger adults. This trend aligns with broader internet usage patterns in the country, where Israelis spend considerable time online for various activities. Demographic profile of e-commerce shoppers The data reveals a clear generational divide in e-commerce participation. Purchase frequency decreased with age, with those 75 and older showing the lowest participation at just under 20 percent. Other factors associated with higher shopping engagement were income level and religious and ethnic background. For example, among adults belonging to higher income brackets, almost two-thirds were found to have completed a recent online purchase. Internet usage and e-commerce potential The robust e-commerce engagement among younger adults correlates with overall internet usage in Israel. Israelis spend an average of seven hours and 25 minutes online daily, with mobile devices accounting for a significant portion of this time. This extensive internet usage provides an important factor in e-commerce growth, particularly as users become more comfortable with online transactions across various platforms. High internet usage in the market demonstrated the potential for targeted marketing and product discovery in the e-commerce space.

This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.

Columns:

• Order ID: Unique identifier for each sales order.
• Date:Date of the sales transaction.
• Category:Broad category of the product sold (e.g., Electronics, Home Appliances, Clothing, Books, Beauty Products, Sports).
• Product Name:Specific name or model of the product sold.
• Quantity:Number of units of the product sold in the transaction.
• Unit Price:Price of one unit of the product.
• Total Price: Total revenue generated from the sales transaction (Quantity * Unit Price).
• Region:Geographic region where the transaction occurred (e.g., North America, Europe, Asia).
• Payment Method: Method used for payment (e.g., Credit Card, PayPal, Debit Card).

Insights:

• 1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.
• 2. Explore the popularity of different product categories across regions.
• 3. Investigate the impact of payment methods on sales volume or revenue.
• 4. Identify top-selling products within each category to optimize inventory and marketing strategies.
• 5. Evaluate the performance of specific products or categories in different regions to tailor marketing campaigns accordingly.

Revolutionize Customer Engagement with Our Comprehensive Ecommerce Data

Our Ecommerce Data is designed to elevate your customer engagement strategies, providing you with unparalleled insights and precision targeting capabilities. With over 61 million global contacts, this dataset goes beyond conventional data, offering a unique blend of shopping cart links, business emails, phone numbers, and LinkedIn profiles. This comprehensive approach ensures that your marketing strategies are not just effective but also highly personalized, enabling you to connect with your audience on a deeper level.

What Makes Our Ecommerce Data Stand Out?

• Unique Features for Enhanced Targeting
  Our Ecommerce Data is distinguished by its depth and precision. Unlike many other datasets, it includes shopping cart links—a rare and valuable feature that provides you with direct insights into consumer behavior and purchasing intent. This information allows you to tailor your marketing efforts with unprecedented accuracy. Additionally, the integration of business emails, phone numbers, and LinkedIn profiles adds multiple layers to traditional contact data, enriching your understanding of clients and enabling more personalized engagement.

• Robust and Reliable Data Sourcing
  We pride ourselves on our dual-sourcing strategy that ensures the highest levels of data accuracy and relevance:

  • Real-Time Information from 10 Active Publication Sites: Our databases are continuously updated with the latest information, sourced from ten active publication sites that provide real-time data.
  • Dedicated Contact Discovery Team: Complementing our automated sources, our dedicated Contact Discovery Team conducts thorough research and investigations, ensuring that every piece of data is accurate and reliable. This two-pronged approach guarantees that our Ecommerce Data is both up-to-date and relevant, providing you with a solid foundation for your business strategies.

  Primary Use Cases Across Industries

Our Ecommerce Data is versatile and can be leveraged across various industries for multiple applications: - Precision Targeting in Marketing: Create personalized marketing campaigns based on detailed shopping cart activities, ensuring that your outreach resonates with individual customer preferences. - Sales Enrichment: Sales teams can benefit from enriched client profiles that include comprehensive contact information, enabling them to connect with key decision-makers more effectively. - Market Research and Analytics: Research and analytics departments can use this data for in-depth market studies and trend analyses, gaining valuable insights into consumer behavior and market dynamics.

Global Coverage for Comprehensive Engagement

Our Ecommerce Data spans across the globe, providing you with extensive reach and the ability to engage with customers in diverse regions: - North America: United States, Canada, Mexico - Europe: United Kingdom, Germany, France, Italy, Spain, Netherlands, Sweden, and more - Asia: China, Japan, India, South Korea, Singapore, Malaysia, and more - South America: Brazil, Argentina, Chile, Colombia, and more - Africa: South Africa, Nigeria, Kenya, Egypt, and more - Australia and Oceania: Australia, New Zealand - Middle East: United Arab Emirates, Saudi Arabia, Israel, Qatar, and more

Comprehensive Employee and Revenue Size Information

Our dataset also includes detailed information on: - Employee Size: Whether you’re targeting small businesses or large corporations, our data covers all employee sizes, from startups to global enterprises. - Revenue Size: Gain insights into companies across various revenue brackets, enabling you to segment the market more effectively and target your efforts where they will have the most impact.

Seamless Integration into Broader Data Offerings

Our Ecommerce Data is not just a standalone product; it is a critical piece of our broader data ecosystem. It seamlessly integrates with our comprehensive suite of business and consumer datasets, offering you a holistic approach to data-driven decision-making: - Tailored Packages: Choose customized data packages that meet your specific business needs, combining Ecommerce Data with other relevant datasets for a complete view of your market. - Holistic Insights: Whether you are looking for industry-specific details or a broader market overview, our integrated data solutions provide you with the insights necessary to stay ahead of the competition and make informed business decisions.

Elevate Your Business Decisions with Our Ecommerce Data

In essence, our Ecommerce Data is more than just a collection of contacts—it’s a strategic tool designed to give you a competitive edge in understanding and engaging your target audience. By leveraging the power of this comprehensive dataset, you can elevate your business decisions, enhance customer interactions, and navigate the digital landscape with confidence and insight.

Feel free to checkout https://www.kaggle.com/code/hasan23/eda-on-online-retail-dataset-top-sales-trends

This dataset provides a curated subset of the anonymized Google Analytics event data for three months of the Google Merchandise Store. The full dataset is available as a BigQuery Public Dataset.

The data includes information on items sold in the store and how much money was spent by users over time. It is both comprehensive enough to invite real analysis yet simple enough to facilitate teaching.

Original Data

Acknowledgement

Foto von Arthur Osipyan auf Unsplash

Snapshot img

The e-commerce technology market share is expected to increase by USD 10.57 billion from 2020 to 2025, and the market’s growth momentum will accelerate at a CAGR of 19.07%.

This e-commerce technology market research report provides valuable insights on the post-COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers e-commerce technology market segmentation by application (B2C and B2B) and geography (North America, APAC, Europe, South America, and MEA). The e-commerce technology market report also offers information on several market vendors, including Adobe Inc., BigCommerce Holdings Inc., commercetools GmbH, HCL Technologies Ltd., Open Text Corp., Oracle Corp., Pitney Bowes Inc., Salesforce.com Inc., SAP SE, and Shopify Inc. among others.

What will the E-Commerce Technology Market Size be During the Forecast Period?

Download Report Sample to Unlock the e-Commerce Technology Market Size for the Forecast Period and Other Important Statistics

E-Commerce Technology Market: Key Drivers, Trends, and Challenges

The increasing e-commerce sales are notably driving the e-commerce technology market growth, although factors such as growing concerns over data privacy and security may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic's impact on the e-commerce technology industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key E-Commerce Technology Market Driver

One of the key factors driving the e-commerce technology market is increasing e-commerce sales. The e-commerce industry is progressing quickly, owing to various factors, such as the growing tech-savvy population, increasing Internet penetration, and the rising use of smartphones. The demand for globally manufactured products is also fueling growth by generating cross-border e-commerce sales. Furthermore, the presence of various multiple payment options, such as credit and debit cards, Internet banking, electronic wallets, and cash-on-delivery (COD), has led to a paradigm shift in the purchasing patterns of people from brick-and-mortar stores to online shopping. Also, e-commerce platforms not only enable consumers to buy goods easily as they do not have the physical barriers involved in offline stores but also help them in making better and more informed decisions, as consumers can view multiple user reviews on the website before purchasing a product. The growth of the e-commerce sector directly impacts the e-commerce technology market. All these factors have increased the demand for e-commerce software and services from end-users. Hence, the growth of the e-commerce industry will boost the growth of the global e-commerce technology market during the forecast period.

Key E-Commerce Technology Market Trend

The rising focus on developing headless CMS is another factor supporting the e-commerce technology market growth in the forecast period. The increasing number of touchpoints for customers, such as IoT devices, smartphones, and progressive web apps, is making it difficult for legacy e-commerce websites to manage demand from customers. Even though most retailers have not embraced the IoT, more customers are exploring new product information through devices, such as IoT-enabled speakers, smart voice assistance, and in-store interfaces. To resolve this issue and provide a more effective user experience, vendors are offering a headless e-commerce architecture. Headless e-commerce architecture is a back-end-only content management system (CMS). Furthermore, vendors are offering headless CMS solutions to simplify e-commerce applications and provide flexible software packaging for their clients. For instance, Magento, a subsidiary of Adobe Inc., offers GraphQL, a flexible and performant application programming interface (API), which allows users to build custom front ends, including headless storefronts, advanced web applications (PWA), and mobile apps. Such developments are expected to provide high growth opportunities for market vendors during the forecast period.

Key E-Commerce Technology Market Challenge

Growing concerns over data privacy and security will be a major challenge for the e-commerce technology market during the forecast period. Data privacy and security risks are the major barriers to the adoption of e-commerce technology. Hackers are constantly trying to search for vulnerabilities and loopholes in e-commerce infrastructure. Although e-commerce players, vendors, and end-user organizations try to adopt proactive prevention plans to counter security breaches within their systems, the rise in the number of e-commerce website hacking and ransomware attacks has resulted in financial and data loss for companies. In addition, public c