In 2024, convenience was the leading reason to spend more money online during Cyber Week than in the previous year. Prices being lower online was the second most common reason for U.S. Cyber Week shoppers.

Introduction

Online Shopping Statistics: Online shopping has revolutionized the retail industry, providing consumers with unparalleled convenience, a wide range of products, and easy access to services. Factors such as greater internet accessibility, the rise of mobile commerce, and shifting consumer preferences have contributed to the substantial growth of the e-commerce market.

Online shopping statistics offer key insights into market trends, consumer habits, demographic shifts, popular product categories, and the technologies driving the future of retail. Understanding these insights is essential for both businesses and consumers to successfully navigate the competitive online marketplace and keep up with emerging trends in digital shopping.

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.

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.

The number of users in the e-commerce market in the United States was modeled to stand at ************** users in 2024. Following a continuous upward trend, the number of users has risen by ************* users since 2017. Between 2024 and 2029, the number of users will rise by ************* users, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on eCommerce.

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.

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)

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

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3. DAY -> day number of the month

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4. ORDER -> sequence of clicks during one session

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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)

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

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

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

(217 products)

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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

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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

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11. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

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12. PRICE -> price in US dollars

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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)

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

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.

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.

Online store owners across different regions have varying preferences when investing in apps. Our data reveals that United States is the leading destination for online businesses using apps, spending an impressive $1.58B per month. Online business owners in United Kingdom and Canada are also passionate about leveraging apps, with monthly app expenditure of $267.55M and $204.96M respectively. Additionally, Australia and Germany contribute significantly as well, representing a combined 8.41% of global monthly app spending.

About

This data is from E-Commerce. I used postgreSQL for data cleaning. I transformed NULL values to 'Not defined' and orginal data have only category name column(which was 'category_code') and that was 'DOT' seperated value which show us the products class from wide to specific. So I split them with delimeter('.').

The orignal data have record with 5 months but I only used December of 2019. If you want more data you can visit the link above and use.

File structure

Acknowledgements

Many thanks Thanks to REES46 Marketing Platform for this dataset and Michael Kechinov

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 and Origin data provider

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Discover the latest eCommerce statistics in India for 2025, including store count by category and platform, estimated sales amount by platform and category, products sold by platform and category, and total app spend by platform and category. Gain valuable insights into the retail landscape in India, uncovering the distribution of stores across categories and platforms.

Mexico leads the eCommerce industry, achieving remarkable success with monthly sales of $5.49T. This solidifies its dominant market position, capturing a significant 20.01% share. Following closely behind is Nicaragua, which achieved monthly sales of $5.08T, accounting for 18.52% of the global eCommerce market. Ranking third among the top performers is United States, contributing 10.84% to the monthly eCommerce sales worldwide. Noteworthy mentions go to Italy, Canada and India, as they also hold substantial market shares.

The eCommerce industry develops at different stages in various regions. Among the platforms we monitor, United States stands out with the highest number of online stores, indicating the prosperity of its eCommerce economy. Additionally, both United Kingdom and Brazil have a strong presence of online shops, accounting for 6.10% and 4.87% of the global online store market.

In 2020, a total of over ** percent of consumers across the globe shopped online: reaching nearly ** percent each, the leading regions that year were South America and Asia. North America had the lowest share with just over ***** in **** consumers buying items on the internet. The online store that was used most frequently by shoppers worldwide was Amazon.com.

Favorite online stores in the U.S. As of November 2020, an estimated ** percent of U.S. consumers stated that their online shop of choice was Amazon, making it by far the favorite e-commerce shop among online shoppers. With less than ** percent, Walmart’s web shop ranked second. Both male and female consumers in the country had a clear preference for Amazon, however, certain online stores were more popular among specific genders. For instance, more men liked visiting eBay, while a higher percentage of women had a preference for Target.

Why do consumers like Amazon? There were various reasons why U.S. shoppers used Amazon to buy products in 2020, the leading reason being the fast and free shipping services provided. Other key factors consumers mentioned, included Amazon’s broad selection, the easy return process, and the platform having some of the lowest prices.

The pie chart showcases the distribution of app/software spending by store category in India, 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 $19.59M units representing 35.58% of the overall spending. Following closely behind is Beauty & Fitness with a spend of $7.33M units, comprising 13.32% of the total. Home & Garden also contributes significantly with a spend of $6.55M units, accounting for 11.89% 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.

Retail eCommerce sales surpassing a staggering $5.7 trillion globally in last year. This upward trajectory shows no signs of plateauing. As we unpack the latest statistics, from the dominance of platforms like WooCommerce and Shopify to the regional powerhouses of the United States and beyond, a picture emerges of a sector in constant evolution. This article dives into the heart of these statistics, offering a panoramic view of the eCommerce landscape today. We explore the dynamics of platform preference, regional market trends, and category-specific insights, providing a comprehensive snapshot of an industry that continues to reshape global retail.

The Poland E-Commerce Market Report is Segmented by Business Model (B2C, B2B), Device Type (Smartphone / Mobile, Desktop and Laptop, Other Device Types), Payment Method (Credit / Debit Cards, Digital Wallets, BNPL, Other Payment Method), 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).

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.

Introduction

Ecommerce Statistics: This app industry has seen remarkable growth, fueled by the widespread use of smartphones, improved internet connectivity, and a shift in consumer habits toward online shopping. With mobile devices becoming a primary tool for shopping and the rise of convenient payment solutions, eCommerce apps have become a vital component of the retail landscape.

As mobile commerce evolves, businesses are focusing on innovations like personalized shopping experiences, quicker transaction methods, and more efficient logistics to keep pace with consumer demands. This shift underscores the critical role eCommerce apps will play in the future of retail.