The revenue in the e-commerce market in the United States was modeled to amount to 1.18 trillion U.S. dollars in 2024. Following a continuous upward trend, the revenue has risen by 754.29 billion U.S. dollars since 2017. Between 2024 and 2029, the revenue will rise by 655.91 billion U.S. dollars, 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: Explore a comprehensive dataset of e-commerce sales, encompassing a variety of product categories, pricing, customer reviews, and sales trends over the past year. This dataset is ideal for analyzing market trends, customer behavior, and sales performance. Explore into the data to uncover insights that can optimize product listings, pricing strategies, and marketing campaigns.

Columns:

product_id: Unique identifier for each product. product_name: Name of the product. category: Product category. price: Price of the product. review_score: Average customer review score (1 to 5). review_count: Total number of reviews. sales_month_1 to sales_month_12: Monthly sales data for each product over the past year. Potential Analyses:

Identify top-performing product categories. Analyze the impact of pricing on sales and customer reviews. Discover seasonal sales trends and patterns. Evaluate customer satisfaction based on review scores and counts.

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.

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.

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...

Asia leads the ranking of biggest e-commerce markets worldwide. The total revenue of online retail in Asian countries added up to over *** trillion U.S. dollars in 2025. This was approximately ****billion U.S. dollars higher than the e-commerce revenue reached in the Americas. Australia, Oceania, and Africa were estimated to achieve far lower e-commerce revenues in 2025, with values below ** billion U.S. dollars. Asia’s leading position can mostly be attributed to China, which achieved a revenue of over ************ U.S. dollars in 2024. How dominant is Asia in e-commerce? The e-commerce market is highly developed on the Asian continent, and will continue to grow. For instance, India is among the countries with the highest Compound Annual Growth Rate (CAGR) for its e-commerce market. Between 2025 and 2030, the market value is forecast to grow with over ** percent annually. The Latin-American e-commerce market is also forecast to grow quickly in the coming years. Large Asian e-commerce markets have a strong influence over the rest of the world. A concrete example of an Asian e-commerce channel that is being adopted in the West is the TikTok Shop, connecting social media and e-commerce to form social commerce.

😍Upvote and share this would help me alot Thank You!

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.

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).

The revenue in the e-commerce market in Europe was modeled to amount to ****** billion U.S. dollars in 2025. Between 2017 and 2025, the revenue rose by *******billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The revenue will steadily rise over the period from 2025 to 2030, reflecting a clear upward trend.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on eCommerce. Widespread adoption and market penetration The growth in revenue is underpinned by increasing consumer participation in e-commerce. The number of e-commerce users in Europe is forecast to reach *** million by 2029, up from *** million in 2024. This surge in users is reflected in the rising market penetration rate, which is expected to increase from ***** percent in 2024 to ***** percent by 2029. The expanding user base and increasing penetration rates underscore the growing importance of e-commerce in the European retail landscape. Country-specific trends and contributions Within Europe, individual countries are making significant contributions to the e-commerce sector. Denmark leads with ** percent of company revenue generated through e-commerce sales in 2024, followed closely by Belgium at ** percent. The United Kingdom has consistently maintained the highest share of online retail sales, reaching approximately **** percent in 2022. This trend is further evidenced by the growing value of monthly internet retail sales in Great Britain, which increased from an index of ***** in November 2022 to ***** in November 2023. These country-specific trends highlight the varied pace of e-commerce adoption across Europe, contributing to the overall growth of the industry.

The revenue in the E-commerce market in the United States was forecast to continuously increase between 2023 and 2027 by in total ***** billion U.S. dollars (+** percent). After the ***** consecutive increasing year, the indicator is estimated to reach *** trillion U.S. dollars and therefore a new peak in 2027. Notably, the revenue of the E-commerce market was continuously increasing over the past years.Find other key market indicators concerning the average revenue per user (ARPU) and number of users.

Use of information and communication technology (ICT) and e-commerce activity by UK businesses. Annual data on e-commerce sales and how businesses are using the internet.

This dataset contains a synthetic but realistic sample of e-commerce sales for an online store, covering the period from 2024 to 2025. It includes details about orders, customers, products, regions, pricing, discounts, sales, profit, and payment modes.

It is designed for data analysis, visualization, and machine learning projects. Beginners and advanced users can use this dataset to practice:

Exploratory Data Analysis (EDA)

Sales trend analysis

Profit margin and discount analysis

Customer segmentation

Predictive modeling (e.g., sales or profit prediction)

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.

In 2023, e-commerce sales in France amounted to approximately *** billion euros. This represents an increase of nearly **** percent compared to the previous year when online sales were approximately ***** billion euros.

Key players In the fourth quarter of 2023, Amazon emerged as the leading e-commerce platform in France, attracting an average of ** million unique monthly visitors. Leboncoin followed in second place, with an average traffic of **** million monthly visitors. Notably, E.Leclerc experienced a notable increase in average monthly traffic, signaling its growing prominence in the French online grocery landscape.

Fashion leads the way As of 2023, fashion was the most purchased product category online in France, and one player stood out in this segment. The Chinese fast-fashion company Shein ranked second after Amazon in the top online stores in the French e-commerce market in 2023, garnering an estimated *** billion U.S. dollars in e-commerce net sales that year, racking up more online sales than the likes of Boulanger, Apple and Cdiscount.

View quarterly updates and historical trends for US E-Commerce Sales. from United States. Source: Census Bureau. Track economic data with YCharts analytic…

View quarterly updates and historical trends for US E-Commerce Sales TTM. from United States. Source: Census Bureau. Track economic data with YCharts anal…

China E-commerce: Sales Revenue: Year to Date: Business to Business data was reported at 16.800 RMB bn in Jun 2017. This records a decrease from the previous number of 26.000 RMB bn for Dec 2016. China E-commerce: Sales Revenue: Year to Date: Business to Business data is updated quarterly, averaging 11.500 RMB bn from Jun 2010 (Median) to Jun 2017, with 19 observations. The data reached an all-time high of 26.000 RMB bn in Dec 2016 and a record low of 2.960 RMB bn in Mar 2011. China E-commerce: Sales Revenue: Year to Date: Business to Business data remains active status in CEIC and is reported by China e-business Research Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICG: E-commerce: Business Sales Revenue.

This synthetic dataset represents E-commerce sales transactions containing 34,500 records across 17 features. It has been carefully designed to simulate realistic online shopping data and can be used for multiple data science and machine learning tasks, including:

🔹 Sales Analysis – revenue trends, profit margins, regional performance, category-wise sales 🔹 Customer Segmentation – analyzing customer demographics, purchase behavior, loyalty scores 🔹 Churn Prediction – identifying customers likely to stop purchasing 🔹 Product Performance – tracking returns, pricing impact, and demand across categories

📊 Columns Overview

• order_id → Unique identifier for each order
• customer_id → Unique identifier for each customer
• product_id → Unique identifier for each product
• category → Product category (Electronics, Fashion, Home, Beauty, Sports, Toys, Grocery)
• price → Unit price of the product
• discount → Discount applied (%)
• quantity → Number of items purchased
• payment_method → Payment type (Credit Card, Debit Card, UPI, PayPal, COD, Wallet)
• order_date → Date of purchase
• delivery_time_days → Days taken to deliver the order
• region → Geographic region of the customer
• returned → Whether the product was returned (Yes/No)
• total_amount → Final bill amount after discounts
• shipping_cost → Delivery charges
• profit_margin → Profit earned from the order
• customer_age → Age of the customer (18–70)
• customer_gender → Gender of the customer (Male/Female/Other)

✅ Use Cases

• Exploratory Data Analysis (EDA)
• Time-series sales forecasting
• Customer segmentation (RFM analysis, clustering)
• Churn prediction (classification)
• Return prediction
• Price and discount analysis

👉 This dataset is ideal for machine learning practice, analytics projects, and Kaggle competitions related to sales, marketing, and customer behavior.

E-commerce sales for North American Industry Classification System (NAICS) food services and drinking places, includes all members under sales, for Canada, for one year of data.

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