Graph and download economic data for E-Commerce Retail Sales as a Percent of Total Sales (ECOMPCTSA) from Q4 1999 to Q2 2025 about e-commerce, retail trade, percent, sales, retail, and USA.

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.

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.

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.

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

Dataset

This dataset was created by Muhammad Azeem

Released under CC0: Public Domain

Contents

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

Quarterly retail e-commerce sales are estimated from the same sample used for the Monthly Retail Trade Survey (MRTS) to estimate preliminary and final U.S. retail sales. Coverage includes all retailers whether or not they are engaged in e-commerce. Online travel services, financial brokers and dealers, and ticket sales agencies are not classified as retail and are not included in either the total retail or retail e-commerce sales estimates.

In the fourth quarter of 2023, the share of e-commerce in total U.S. retail sales was 15.6 percent and increased from 2022, when e-commerce sales amounted to 14.9 percent of total retail sales in the holiday quarter.

😍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.

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 TTM. from United States. Source: Census Bureau. Track economic data with YCharts anal…

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 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.

United States Retail Sales: E Commerce data was reported at 121.460 USD bn in Sep 2018. This records an increase from the previous number of 120.479 USD bn for Jun 2018. United States Retail Sales: E Commerce data is updated quarterly, averaging 36.097 USD bn from Dec 1999 (Median) to Sep 2018, with 76 observations. The data reached an all-time high of 141.719 USD bn in Dec 2017 and a record low of 5.241 USD bn in Dec 1999. United States Retail Sales: E Commerce data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.H002: Retail Sales: By NAIC System: Quarterly.

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.

United States Retail Sales: E Commerce to Total Sales data was reported at 9.100 % in Sep 2018. This records an increase from the previous number of 9.000 % for Jun 2018. United States Retail Sales: E Commerce to Total Sales data is updated quarterly, averaging 3.950 % from Dec 1999 (Median) to Sep 2018, with 76 observations. The data reached an all-time high of 10.400 % in Dec 2017 and a record low of 0.700 % in Dec 1999. United States Retail Sales: E Commerce to Total Sales data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.H002: Retail Sales: By NAIC System: Quarterly.

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

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.

Dataset

This dataset was created by Shivam Mishra

Contents