Most of the latest online purchases from abroad among U.S. shoppers were made in China, according to a 2024 survey. 45 percent of the e-commerce users surveyed in the United States had made their most recent purchase from there. The United Kingdom ranked second, with 10 percent. In turn, the U.S. was the main market where Canadian cross-border shoppers last bought online.

Explore the latest Online Shopping Statistics with powerful data, trends, and insights to understand ecommerce growth and consumer behavior.

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.

A comprehensive dataset of ecommerce statistics for 2026, including global market size, US ecommerce data, mobile commerce trends, social commerce growth, payment methods, cart abandonment rates, AI adoption, and online shopping behavior insights.

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.

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.

The pandemic pushed online shopping in a new direction. Purely brick-and-mortar businesses were forced to move their businesses online. The COVID-19 related boost in online shopping resulted in an additional $218 billion in sales in the US alone.

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.

Description:

• The dataset comprises anonymized data on online sales transactions, capturing various aspects of product purchases, customer details, and order characteristics.
• This dataset can be utilized for analyzing sales trends, customer purchase behavior, and order management in e-commerce or retail.
• It can aid in understanding the impact of discounts, payment methods, and shipment providers on sales performance and customer satisfaction.
• This dataset can be utilized for analyzing sales performance, customer purchasing patterns, and operational efficiency in order management.
• It can help in evaluating the effects of discounts and payment methods on sales, optimizing inventory by studying product demand, and improving customer satisfaction through better shipping and return handling.

Features:

More than 90% of people regularly use a smartphone for shopping online. With over 294 million smartphone users in the US alone, approximately 232 million of them regularly use their phones to purchase online.

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?

When it comes to ecommerce statistics by country, most people would assume that the United States would be the biggest spender. But that’s not actually the case. While the US online market is more established, other countries are catching up quickly. These are the countries that have the biggest adoption of ecommerce shopping

This dataset captures comprehensive insights into consumer behavior and preferences for online shopping. It is based on survey responses and focuses on key aspects such as shopping frequency, preferred payment methods, attraction factors, security concerns, and product categories frequently purchased. The dataset provides valuable information for businesses, marketers, and researchers interested in understanding online consumer trends and improving e-commerce strategies.

Key Features: - Demographic Information: Gender of respondents, helping analyze preferences by gender groups. - Shopping Frequency: How often respondents engage in online shopping activities. - Purchase Proportion: The percentage of total purchases made online compared to in-store. - Review Checking Behavior: Frequency of checking product reviews before purchase. - Attraction Factors: Main factors that attract respondents to online shopping, such as discounts and variety. - Retailer Selection Factors: Considerations for choosing online retailers, including brand reputation and customer service. - Preferred Payment Methods: Common payment methods used by respondents, such as credit cards and digital wallets. - Local vs. International Retailers: Preferences for shopping locally or from international marketplaces. - Frequent Marketplaces: Popular online shopping platforms used by respondents. - Security Concerns: Level of concern about payment security while shopping online. - Participation in Promotions: Frequency of engaging in promotional activities. - Price Sensitivity: Degree of price sensitivity while making online purchases. - Comfortable Price Range: Preferred price ranges for online purchases. - Product Categories: Types of products frequently purchased online. - Online Shopping Drawbacks: Main challenges experienced, such as delivery delays and refund issues. - Authenticity Concerns: Confidence level regarding product authenticity. - Desired Improvements: Suggestions for improving the online shopping experience.

Potential Use Cases: - Consumer Behavior Analysis: Identify patterns and preferences in online shopping. - Market Segmentation: Understand different customer segments and their unique needs. - Business Strategy: Guide e-commerce businesses in designing customer-centric strategies. - Trend Analysis: Examine the latest trends in online shopping behavior. - Security and Payment Insights: Analyze customer concerns regarding secure online transactions.

PLEASE UPVOTE IT

Overview:

This dataset contains 1000 rows of synthetic online retail sales data, mimicking transactions from an e-commerce platform. It includes information about customer demographics, product details, purchase history, and (optional) reviews. This dataset is suitable for a variety of data analysis, data visualization and machine learning tasks, including but not limited to: customer segmentation, product recommendation, sales forecasting, market basket analysis, and exploring general e-commerce trends. The data was generated using the Python Faker library, ensuring realistic values and distributions, while maintaining no privacy concerns as it contains no real customer information.

Data Source:

This dataset is entirely synthetic. It was generated using the Python Faker library and does not represent any real individuals or transactions.

Data Content:

Potential Use Cases (Inspiration):

Customer Segmentation: Group customers based on demographics, purchasing behavior, and preferences.

Product Recommendation: Build a recommendation system to suggest products to customers based on their past purchases and browsing history.

Sales Forecasting: Predict future sales based on historical trends.

Market Basket Analysis: Identify products that are frequently purchased together.

Price Optimization: Analyze the relationship between price and demand.

Geographic Analysis: Explore sales patterns across different cities.

Time Series Analysis: Investigate sales trends over time.

Educational Purposes: Great for practicing data cleaning, EDA, feature engineering, and modeling.

CONTEXT

E-commerce (electronic commerce) is the buying and selling of goods and services, or the transmitting of funds or data, over an electronic network, primarily the internet. These business transactions occur either as business-to-business (B2B), business-to-consumer (B2C), consumer-to-consumer or consumer-to-business

CONTENT

This is simple data set of US online_store from 2020.

INSPIRATION

So, the data cames with some questions !!

What was the highest Sale in 2020? What is average discount rate of charis? What are the highest selling months in 2020? What is the Profit Margin for each sales record? How much profit is gained for each product? What is the total Profit & Sales by Sub-Category? People from city/state shop the most? Develop a function, to return a dataframe which is grouped by a particular column (as an input)

If you have wonderful idea about this dataset, welcome to contribute !!! Happy Kaggling, please up-vote if you find this dataset helpful!🖤!

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.

We monitor millions of online stores across 200+ countries, ensuring that this report provides accurate and up-to-date information. This report diverse eCommerce ecosystems in various countries/regions, including market penetration, regional preferences, consumer trends, and technological investments. Stay up-to-date with the latest data and gain a comprehensive understanding of the eCommerce market dynamics on a country/region level, enabling informed business decisions and strategic planning.

According to a survey published in January 2021, more than 40 percent of online shoppers in the United States purchased online once or twice a week. In addition, nearly a quarter of respondents in the North American country reported shopping once every two weeks.

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.

I'll show you how the pandemic has changed the way people shop and give you some accurate ecommerce statistics to prove it.