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.

Internet sales have played an increasingly significant role in retailing. In 2025, e-commerce accounted for over ***percent of retail sales worldwide. Forecasts indicate that by 2030, the online segment will make up ***percent of total global retail sales. Retail e-commerce Online shopping has grown steadily in popularity in recent years. In 2024, global e-commerce sales amounted to over ************ U.S. dollars, a figure expected to approach * trillion U.S. dollars by 2030. Digital development boomed during the COVID-19 pandemic, generating unprecedented e-commerce growth in various economies across the globe. This trend correlates strongly with the constantly improving online access, especially in "mobile-first" online communities, which have long struggled with traditional commercial fixed broadband connections due to financial or infrastructure constraints but enjoy the advantages of cheap mobile broadband connections. M-commerce on the rise The order share of online shopping via smartphones and tablets now outperforms traditional e-commerce via desktop computers. As such, e-retailers around the world have caught up in mobile e-commerce sales. Online shopping via smartphones is particularly prominent in Asia. By the end of 2023, South Korea was the top digital market based on the percentage of the population that had purchased something by phone, with nearly ** percent having made a weekly mobile purchase. Malaysia, UAE, and Turkey completed the top of the ranking.

Online Retail Sales and Customer Data

Transactional Data with Product and Customer Details in Online Retail

By Marc Szafraniec [source]

About this dataset

The InvoiceNo column holds unique identifiers for each transaction conducted. This numerical code serves a twofold purpose: it facilitates effortless identification of individual sales or purchases while simultaneously enabling treasury management by offering a repository for record keeping.

In concordance with the invoice number is the InvoiceDate column. It provides a date-time stamp associated with every transaction, which can reveal patterns in purchasing behaviour over time and assists with record-keeping requirements.

The StockCode acts as an integral part of this dataset; it encompasses alphanumeric sequences allocated distinctively to every item in stock. Such a system aids unequivocally identifying individual products making inventory records seamless.

The Description field offers brief elucidations about each listed product, adding layers beyond just stock codes to aid potential customers' understanding of products better and make more informed choices.

Detailed logs concerning sold quantities come under the Quantity banner - it lists the units involved per transaction alongside aiding calculations regarding total costs incurred during each sale/purchase offering significant help tracking inventory levels based on products' outflow dynamics within given periods.

Retail isn't merely about what you sell but also at what price you sell- A point acknowledged via our inclusion of unit prices exerted on items sold within transactions inside our dataset's UnitPrice column which puts forth pertinent pricing details serving as pivotal factors driving metrics such as gross revenue calculation etc

Finally yet importantly is our dive into foreign waters - literally! With impressive international outreach we're looking into segmentation bases like geographical locations via documenting countries (under the name Country) where transactions are conducted & consumers reside extending opportunities for businesses to map their customer bases, track regional performance metrics, extend localization efforts and overall contributing to the formulation of efficient segmentation strategies.

All this invaluable information can be found in a sortable CSV file titled online_retail.csv. This dataset will prove incredibly advantageous for anyone interested in or researching online sales trends, developing customer profiles, or gaining insights into effective inventory management practices

How to use the dataset

Identifying Products: StockCode is the unique identifier for each product. You can use it to identify individual products, track their sales, or discover patterns related to specific items.

Assessing Sales Volume: Quantity column tells you about the number of units of a product involved in each transaction. Along with InvoiceNo, you can analyze overall sales volume or specific purchases throughout your selected period.

Observing Price Fluctuations: By using the UnitPrice, not only can the total cost per transaction be calculated (by multiplying with Quantity), but also insightful observations like price fluctuations over time or determining most profitable items could be derived.

Analyzing Description Patterns/Trends: The Description field sheds light upon what kind of products are being traded. This could provide some inspiration for text analysis like term frequency-inverse document frequency (TF-IDF), sentiment analysis on descriptions, etc., to figure out popular trends at given times.

Analysing Geographical Trends: With the help of Country column, geographical trends in sales volumes across different nations can easily be analyzed i.e., which location has more customers or which country orders more quantity or expensive units based on unit price and quantity columns respectively.

Keep in mind that proper extraction and transformation methodology should be applied while handling data from different columns as per their datatypes (textual/alphanumeric/numeric) requirements.

This dataset not only allows retailers to gain an immediate understanding into their operations but could also serve as a base dataset for those interested in machine learning regarding predicting future transactions

Research Ideas

• Inventory Management: By tracking the 'Quantity' and 'StockCode' over time, a business could use this data to notice if certain products are frequently purchased together or in specific seasons, allowing them to better stock their inventory.
• Pricing Strategy:...

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...

Sales Data Description This dataset represents synthetic sales data generated for practice purposes only. It is not real-time or based on actual business operations, and should be used solely for educational or testing purposes. The dataset contains information that simulates sales transactions across different products, regions, and customers. Each row represents an individual sale event with various details associated with it.

Columns in the Dataset

1. Product_ID: Unique identifier for each product sold. Randomly generated for practice purposes.
2. Sale_Date: The date when the sale occurred. Randomly selected from the year 2023.
3. Sales_Rep: The sales representative responsible for the transaction. The dataset includes five random sales representatives (Alice, Bob, Charlie, David, Eve).
4. Region: The region where the sale took place. The possible regions are North, South, East, and West.
5. Sales_Amount: The total sales amount for the transaction, including discounts if any. Values range from 100 to 10,000 (in currency units).
6. Quantity_Sold: The number of units sold in that transaction, randomly generated between 1 and 50.
7. Product_Category: The category of the product sold. Categories include Electronics, Furniture, Clothing, and Food.
8. Unit_Cost: The cost per unit of the product sold, randomly generated between 50 and 5000 currency units.
9. Unit_Price: The selling price per unit of the product, calculated to be higher than the unit cost.
10. Customer_Type: Indicates whether the customer is a New or Returning customer.
11. Discount: The discount applied to the sale, randomly chosen between 0% and 30%.
12. Payment_Method: The method of payment used by the customer (e.g., Credit Card, Cash, Bank Transfer).
13. Sales_Channel: The channel through which the sale occurred. Either Online or Retail.
14. Region_and_Sales_Rep: A combined column that pairs the region and sales representative for easier tracking.

Disclaimer

Please note: This data was randomly generated and is intended solely for practice, learning, or testing. It does not reflect real-world sales, customers, or businesses, and should not be considered reliable for any real-time analysis or decision-making.

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

China Taobao Online Sales: Volume data was reported at 7,625.827 Unit mn in Aug 2020. This records an increase from the previous number of 7,391.891 Unit mn for Jul 2020. China Taobao Online Sales: Volume data is updated monthly, averaging 7,625.827 Unit mn from Jun 2019 (Median) to Aug 2020, with 15 observations. The data reached an all-time high of 8,645.429 Unit mn in Aug 2019 and a record low of 3,475.599 Unit mn in Feb 2020. China Taobao Online Sales: Volume data remains active status in CEIC and is reported by Moojing Market Intelligence. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HTB: Taobao and Tmall Online Sales.

In 2024, online commerce transactions in China reached approximately ***** trillion yuan, representing a *** percent year-on-year growth. The e-commerce market in China maintained a steady growth in recent years. E-commerce in ChinaIn 2024, the Chinese online buyer penetration amounted to **** percent, far above the global average. The number of online shoppers in China had reached more than *** million in 2024. Business-to-consumer (B2C) online commerce is an important component of China’s e-commerce market. In 2024, around a third of China's total retail sales were made online. Tmall, a subsidiary of Alibaba, JD, and Suning were among the leading B2C e-commerce retailers in China as of the first quarter of 2022. Since its initial public offering (IPO) at the New York Stock Exchange in September 2014, Alibaba became one of the largest internet companies worldwide. As of the third quarter of 2018, it ranked second only to Google based on brand value. Alibaba Group is not solely focused on B2C business. It also dominates in consumer-to-consumer (C2C) and business-to-business (B2B) segments. Alipay, a third-party online payment solution brought forward by Alibaba, has cornered the online payment market in China.

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.

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.

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.

China Others: Taobao and Tmall Online Sales: Volume data was reported at 883.456 Unit mn in Mar 2025. This records an increase from the previous number of 590.697 Unit mn for Feb 2025. China Others: Taobao and Tmall Online Sales: Volume data is updated monthly, averaging 1,319.283 Unit mn from Jan 2019 (Median) to Mar 2025, with 75 observations. The data reached an all-time high of 2,248.884 Unit mn in Nov 2020 and a record low of 557.818 Unit mn in Apr 2022. China Others: Taobao and Tmall Online Sales: Volume data remains active status in CEIC and is reported by CEIC Data. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HTB: Taobao and Tmall Online Sales: By Category.

This chart illustrates the estimated sales amounts generated by stores on various platforms within India. Magento shows a significant lead, with total sales amounting to $746.53B, which constitutes 67.17% of the region's total sales on platforms. Custom Cart reports sales of $275.49B, accounting for 24.79% of the total platform sales in India. Salesforce Commerce Cloud also holds a notable share, with its sales reaching $47.72B, representing 4.29% of the overall sales amount. This data provides a comprehensive view of the market dynamics in India, highlighting which platforms are driving the most sales.

Internet sales in Great Britain by store type, month and year.

China Others: Taobao Online Sales: Number of Store data was reported at 2,095.635 Unit th in Mar 2025. This records an increase from the previous number of 1,429.820 Unit th for Feb 2025. China Others: Taobao Online Sales: Number of Store data is updated monthly, averaging 2,215.286 Unit th from Jan 2019 (Median) to Mar 2025, with 75 observations. The data reached an all-time high of 5,795.530 Unit th in Jan 2019 and a record low of 1,304.994 Unit th in Jan 2021. China Others: Taobao Online Sales: Number of Store data remains active status in CEIC and is reported by CEIC Data. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HTB: Taobao and Tmall Online Sales: By Category.

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 India, the estimated sales amount across various store categories provides key insights into the market's dynamics. Gifts & Special Events, as a prominent category, generates significant sales, totaling $745.33B, which is 67.07% of the region's total sales in this sector. Home & Garden follows with robust sales figures, achieving $210.60B in sales and comprising 18.95% of the region's total. Beauty & Fitness contributes a considerable amount to the regional market, with sales of $66.49B, accounting for 5.98% of the total sales in India. This breakdown highlights the varying economic impacts of different categories within the region, showcasing the diversity and strengths of each sector.

This chart illustrates the estimated sales amounts generated by stores on various platforms within Egypt. Custom Cart shows a significant lead, with total sales amounting to $2.23B, which constitutes 88.02% of the region's total sales on platforms. WooCommerce reports sales of $202.79M, accounting for 8.02% of the total platform sales in Egypt. Salesforce Commerce Cloud also holds a notable share, with its sales reaching $38.33M, representing 1.52% of the overall sales amount. This data provides a comprehensive view of the market dynamics in Egypt, highlighting which platforms are driving the most sales.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_65e4d47c3616d251f9a84ec1ad28f43c/view

This chart provides a detailed overview of the number of Egypt online retailers by Monthly Sales. Most Egypt stores' Monthly Sales are Less than $100.00, there are 4.15K stores, which is 98.46% of total. In second place, 38 stores' Monthly Sales are $100.00K to $1.00M, which is 0.90% of total. Meanwhile, 15 stores' Monthly Sales are $10.00M to $100.00M, which is 0.36% of total. This breakdown reveals insights into Egypt stores distribution, providing a comprehensive picture of the performance and efficient of online retailer.