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.

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.

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

In the field of e-commerce, the datasets are typically considered as proprietary, meaning they are owned and controlled by individual organizations and are not often made publicly available due to privacy and business considerations. In spite of this, The UCI Machine Learning Repository, known for its extensive collection of datasets beneficial for machine learning and data mining research, has curated and made accessible a unique dataset. This dataset comprises actual transactional data spanning from the year 2010 to 2011. For those interested, the dataset is maintained and readily available on the UCI Machine Learning Repository's site under the title "Online Retail".

Content

The dataset is a transnational one, capturing every transaction made from December 1, 2010, through December 9, 2011, by a UK-based non-store online retail company. As an online retail entity, the company doesn't have a physical store presence, and its operations and sales are conducted purely online. The company's primary product offering includes unique gifts for all occasions. While the company serves a diverse range of customers, a significant number of its clientele includes wholesalers.

Acknowledgements

In collaboration with the UCI Machine Learning Repository, the dataset was provided and made available by Dr. Daqing Chen. Dr. Chen is the Director of the Public Analytics group at London South Bank University, UK. Any correspondence regarding this dataset can be sent to Dr. Chen at 'chend' at 'lsbu.ac.uk'. We are grateful to him for providing such an invaluable resource for researchers and data science enthusiasts.

The image used has been sourced from Canva

Inspiration

The rich and extensive data within this dataset opens the door for a multitude of potential analyses. It lends itself well to various methods and techniques in data science, including but not limited to time series analysis, clustering, and classification. By exploring this dataset, one could derive key insights into customer behavior, transaction trends, and product performance, providing ample opportunities for deep and insightful explorations.

In 2024, retail e-commerce sales in the United States reached an estimated **** billion U.S. dollars, roughly double the sales value reached in 2019. E-commerce's growth trajectory Driven by the escalating integration of technology into daily life, e-commerce has witnessed a remarkable surge in popularity. Projections indicate a significant uptick in e-commerce users in the United States, rising from *** million in 2025 to over *** million by 2029. As of 2023, apparel and accessories ranked as the most sought-after e-commerce product category, comprising over ** percent of all retail sales in the U.S. This trend persists despite inflationary pressures, positioning this category among the e-commerce segments experiencing the most significant year-on-year price changes. M-commerce users demographic While the demand for the convenience of purchasing from the palm of one's hand is also rapidly increasing, various demographic factors influence mobile commerce usage. There's a higher proportion of male online shoppers than females, with a split of ** percent versus ** percent. Age is another determinant. Younger consumers exhibit a greater inclination towards m-commerce, with ** percent of mobile shoppers falling within the ** to ** age bracket. Furthermore, income levels also shape mobile shopping habits, with individuals earning less than ****** U.S. dollars annually showing the highest propensity for mobile-based purchases.

This table contains 3 series, with data for years 2016 - 2017 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Sales (3 items: Retail trade; Electronic shopping and mail-order houses; Retail E-commerce sales).

In the second quarter of 2025, the share of e-commerce in total U.S. retail sales stood at **** percent, up from the previous quarter. From January to March 2025, retail e-commerce sales in the United States hit over *** billion U.S. dollars, the highest quarterly revenue in history. How e-commerce measures up in total U.S. retail In 2024, the reported total value of retail e-commerce sales in the United States amounted to over ****trillion U.S. dollars—impressive, but the figure pales compared to the total annual retail trade value of ******trillion U.S. dollars. Rising e-commerce segments Online shopping is popular among all age groups, though digital purchases are most common among Millennial internet users. In 2022, around ** percent of Millennials purchased items via the internet. Mobile commerce is also growing in popularity, as consumers increasingly rely on their smartphones and mobile apps for shopping activities. In the fourth quarter of 2022, m-commerce spending made up ** percent of the overall online spending in the United States.

Retail Trade, e-commerce sales, Canada, by industries based on North American Industry Classification System (NAICS), monthly.

E-Commerce Sales Dataset

Analyzing and Maximizing Online Business Performance

By ANil [source]

About this dataset

This dataset provides an in-depth look at the profitability of e-commerce sales. It contains data on a variety of sales channels, including Shiprocket and INCREFF, as well as financial information on related expenses and profits. The columns contain data such as SKU codes, design numbers, stock levels, product categories, sizes and colors. In addition to this we have included the MRPs across multiple stores like Ajio MRP , Amazon MRP , Amazon FBA MRP , Flipkart MRP , Limeroad MRP Myntra MRP and PaytmMRP along with other key parameters like amount paid by customer for the purchase , rate per piece for every individual transaction Also we have added transactional parameters like Date of sale months category fulfilledby B2b Status Qty Currency Gross amt . This is a must-have dataset for anyone trying to uncover the profitability of e-commerce sales in today's marketplace

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How to use the dataset

This dataset provides a comprehensive overview of e-commerce sales data from different channels covering a variety of products. Using this dataset, retailers and digital marketers can measure the performance of their campaigns more accurately and efficiently.

The following steps help users make the most out of this dataset: - Analyze the general sales trends by examining info such as month, category, currency, stock level, and customer for each sale. This will give you an idea about how your e-commerce business is performing in each channel.
- Review the Shiprocket and INCREF data to compare and analyze profitability via different fulfilment methods. This comparison would enable you to make better decisions towards maximizing profit while minimizing costs associated with each method’s referral fees and fulfillment rates.
- Compare prices between various channels such as Amazon FBA MRP, Myntra MRP, Ajio MRP etc using the corresponding columns for each store (Amazon MRP etc). You can judge which stores are offering more profitable margins without compromising on quality by analyzing these pricing points in combination with other information related to product sales (TP1/TP2 - cost per piece).
- Look at customer specific data such as TP 1/TP 2 combination wise Gross Amount or Rate info in terms price per piece or total gross amount generated by any SKU dispersed over multiple customers with relevant dates associated to track individual item performance relative to others within its category over time periods shortlisted/filtered appropriately.. Have an eye on items commonly utilized against offers or promotional discounts offered hence crafting strategies towards inventory optimization leading up-selling operations.?
- Finally Use Overall ‘Stock’ details along all the P & L Data including Yearly Expenses_IIGF information record for takeaways which might be aimed towards essential cost cutting measures like switching amongst delivery options carefully chosen out of Shiprocket & INCREFF leadings away from manual inspections catering savings under support personnel outsourcing structures.?

By employing a comprehensive understanding on how our internal subsidiaries perform globally unless attached respective audits may provide us remarkably lower operational costs servicing confidence; costing far lesser than being incurred taking into account entire pallet shipments tracking sheets representing current level supply chains efficiencies achieved internally., then one may finally scale profits exponentially increases cut down unseen losses followed up introducing newer marketing campaigns necessarily tailored according playing around multiple goods based spectrums due powerful backing suitable transportation boundaries set carefully

Research Ideas

• Analysing the difference in profitability between sales made through Shiprocket and INCREFF. This data can be used to see where the biggest profit margins lie, and strategize accordingly.
• Examining the Complete Cost structure of a product with all its components and their contribution towards revenue or profitability, i.e., TP 1 & 2, MRP Old & Final MRP Old together with Platform based MRP - Amazon, Myntra and Paytm etc., Currency based Profit Margin etc.
• Building a predictive model using Machine Learning by leveraging historical data to predict future sales volume and profits for e-commerce products across multiple categories/devices/platforms such as Amazon, Flipkart, Myntra etc as well providing m...

View quarterly updates and historical trends for US E-Commerce Sales as Percent of Retail Sales. from United States. Source: Census Bureau. Track economic…

In 2024, retail e-commerce in Canada was forecast to generate over **** billion U.S. dollars in revenues. According to estimates, this figure is projected to increase to *** billion U.S. dollars by 2029. Successful e-commerce segments in Canada Canada's booming e-commerce sector owes more than half of its retail sales to two particularly lucrative segments: fashion and electronics, which captured ** and ** percent of all e-commerce retail sales in Canada in 2022, respectively. With nearly ** billion U.S. dollars in e-commerce net sales, Amazon was the leading online store in Canada in 2021, outperforming competitors like Walmart and Costco by an impressive margin. Ranking second, walmart.ca generated around **** billion U.S. dollars in e-commerce net sales that year. Fashion leads the way Fashion proved to be a successful e-commerce segment in many countries around the world, and Canada was no exception, as the most popular online stores in the fashion segment raked in millions of dollars in e-commerce net sales, led by gapcanada.ca with *** million U.S. dollars in 2021. Like many other countries in the world, Canada's fashion e-commerce sector was also disrupted by a Chinese newcomer that catapulted itself to the centerstage of the fast fashion world: Shein was the most downloaded fashion and beauty shopping app in Canada in June 2022, with its ******* monthly downloads overshadowing those of competitors. In comparison, long-established fast fashion titan Zara only amassed ****** downloads that same month.

From April to June 2025, U.S. retail e-commerce sales amounted to roughly *** billion U.S. dollars, marking a small decrease compared to the previous quarter. Overall, retail e-commerce sales outdid the quarterly sales records registered in 2020. E-commerce in the post-pandemic era During the second quarter of 2020, as COVID-19 spread across the globe, the U.S.'s quarterly e-commerce revenue reached *** billion for the first time in history. In 2021, online retail sales account for**** percent of total retail in the United States. Clothing and accessories, including footwear, is one of the largest B2C e-commerce merchandise categories. Retail e-commerce sales in the United States are estimated from samples used for the Monthly Retail Trade Survey and exclude online travel services, ticket sales agencies, and financial brokers. Latest trend? Quick commerce Shoppers expect fast delivery of their purchases, especially when it comes to grocery products. This segment of the e-commerce industry goes under quick commerce and is expected to generate increasing revenue in the next years. Major quick commerce companies like Instacart or Uber Eat operate in the United States, where the quick commerce market is forecast to hit nearly ** billion U.S. dollars by 2027.

E-commerce product recommendation is a feature commonly used in online retail to suggest products to customers based on various factors, including their browsing history, purchase behavior, product preferences, and other users' similar actions. This technique is pivotal in personalizing the shopping experience and increasing customer engagement and sales.

United States - E-Commerce Retail Sales was 304209.00000 Mil. of $ in April of 2025, according to the United States Federal Reserve. Historically, United States - E-Commerce Retail Sales reached a record high of 304209.00000 in April of 2025 and a record low of 4467.00000 in October of 1999. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - E-Commerce Retail Sales - last updated from the United States Federal Reserve on December of 2025.

This superstore dataset can be used for various analyses, including, but not limited to, profit analysis, customer segmentation, and profit prediction. Performing extensive data analysis on it to deliver insights on how the company can increase its profits while minimizing losses.

Online shopping sales across India amounted to around ** billion U.S. dollars in 2021. The e-commerce market is likely to grow to over *** billion U.S. dollars by 2025. The e-commerce market in India is the fastest-growing market in the world. Online retail segments In fiscal year 2017, the retail market was led by electronics with a penetration rate of about ** percent. However, in terms of groceries, local offline vendors or kiranas continued to be the preferred choice for daily groceries due the ease of bargaining and benefitting from the ‘old-customer’ designation with extra rations as a gesture from the vendor. Nevertheless, the number of online shoppers in the country was estimated to increase to over *** million in 2025, up from around ** million in 2017. Impact of COVID-19 on the marketThe coronavirus outbreak in March 2020 caused a surge in prices across e-commerce platforms. Panic purchasing resulted in the shortage of sanitary and food items online as well as in physical stores across the country. As the online consumption continued to increase, unscrupulous sellers jacked up the prices on certain items. Amazon and Flipkart, the two e-commerce market leaders in India urged sellers and even blocked certain products to exercise responsible pricing. Manufacturers increased production in order to keep up with the supply of fast-moving items. With the uncertainty surrounding the impact of COVID-19, manufacturers and retailers will presumably have to work in unison to keep track of an unprecedented demand and supply scenario.

E-commerce sales and total sales for retail trade in Canada, available on an annual basis.

While having slowed down from an impressive 24.9 percent year-on-year growth in 2017, Amazon's retail e-commerce sales in the United States are still growing in the double digits. In 2019, U.S. e-retail sales of the online platform increased by 19.1 percent and amounted to over 222.6 billion U.S. dollars.

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Description: The E-commerce Sales Data dataset provides a comprehensive collection of information related to user profiles, product details, and user-product interactions. It is a valuable resource for understanding customer behavior, preferences, and purchasing trends on an e-commerce platform.

Dataset Structure:

User Sheet: This sheet contains user profiles, including details such as user ID, name, age, location, and other relevant information. It helps in understanding the demographics and characteristics of the platform's users.

Product Sheet: The product sheet offers insights into the various products available on the e-commerce platform. It includes product IDs, names, categories, prices, descriptions, and other product-specific attributes.

Interactions Sheet: The interactions sheet is a crucial component of the dataset, capturing the interactions between users and products. It records details of user actions, such as product views, purchases, reviews, and ratings. This data is essential for building recommendation systems and understanding user preferences.

Potential Use Cases:

Recommendation Systems: With the user-product interaction data, this dataset is ideal for building recommendation systems. It allows the development of personalized product recommendations to enhance the user experience.

Market Basket Analysis: The dataset can be used for market basket analysis to understand which products are frequently purchased together, aiding in inventory management and targeted marketing.

User Behavior Analysis: By analyzing user interactions, you can gain insights into user behavior, such as popular product categories, browsing patterns, and the impact of user reviews and ratings on purchasing decisions.

Targeted Marketing: The dataset can inform marketing strategies, enabling businesses to tailor promotions and advertisements to specific user segments and product categories.

This E-commerce Sales Data dataset is a valuable resource for e-commerce platforms and data scientists seeking to optimize the shopping experience, enhance customer satisfaction, and drive business growth through data-driven insights.

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.