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

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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...

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.

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, 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.

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)

Sales for electronics and appliances of U.S. electronic shopping and mail-order houses have increased since 2003. The most significant growth was reported in 2021, when e-commerce sales hit over 86 billion U.S. dollars, up from nearly 51 billion U.S. dollars in 2019. In 2022, e-commerce sales slightly decreased to 85.5 billion U.S. dollars.

The Online Sales Dataset provides a detailed overview of global online sales transactions across various product categories. It includes transaction details such as order ID, date, product category, product name, quantity, unit price, total price, region, and payment method.

This driver measures the value of retail sales conducted online in the United States. Data is sourced from the US Census Bureau and is presented in 2017 dollars.

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 October of 2025.

In 2024, global e-commerce sales grew by *** percent compared to the previous year. In that period, e-commerce accounted for approximately ** percent of all retail sales worldwide. Asian countries lead the way According to an estimate, China and Indonesia ranked **************** respectively on the list of countries with the greatest share of retail sales projected to take place online in 2023. Following the same trend, estimates also revealed that the three fastest-growing retail e-commerce countries in the world are all in Asia. Amazon on top When looking at the leading e-commerce companies worldwide, as opposed to the leading e-commerce countries, ****** is the clear market leader with a market cap of over************n U.S. dollars as of March 2025. Not only that, but the *************************** company is also by far the***** visited online marketplace in the world, with approximately *** billion monthly visits.

There are many potential insights one can draw from this dataset. The ecommerce data provided contains information about sales orders, including the order ID, order date, shipping date, customer names, location (country, city, state), product categories, product names, sales amount, and profit amount. The dataset covers a range of orders over several years, with information on different product categories and their associated sales. It also provides insights into the distribution of orders across cities and states. After importing the data into Tableau, one can sort to see which states have the most total sales (CA, WA, AZ), which product categories have the highest profit (chairs, phones, machines), and various other intersections of data. The analysis from this data can be used to make decisions about what products to increase or reduce stock of, which states to focus on to push sales, and how to maximize profits by looking at which product categories have the highest profit margins.

If you’re interested, please take a look!

Dataset originally from https://www.kaggle.com/datasets/imgowthamg/walmart-sales-dataset

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

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 the first quarter 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.

Canada Retail E-Commerce Sales data was reported at 4,425,564.000 CAD th in Dec 2022. This records a decrease from the previous number of 4,499,923.000 CAD th for Nov 2022. Canada Retail E-Commerce Sales data is updated monthly, averaging 1,878,200.500 CAD th from Jan 2016 (Median) to Dec 2022, with 84 observations. The data reached an all-time high of 4,943,411.000 CAD th in Dec 2020 and a record low of 789,553.000 CAD th in Feb 2016. Canada Retail E-Commerce Sales data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.H021: Retail E-Commerce Sales.

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.

Survey on the Use of Information and Communication Technologies and Electronic Commerce in Companies: Electronic commerce (e-commerce) sales. National.

Objectives:

• I leveraged advanced data visualization techniques to extract valuable insights from a comprehensive dataset. By visualizing sales patterns, customer behavior, and product trends, I identified key growth opportunities and provided actionable recommendations to optimize business strategies and enhance overall performance. you can find the GitHub repo here Link to GitHub Repository.

Data Description:

there are exactly 6 table and 1 is a fact table and the rest of them are dimension tables: Fact Table:

payment_key:
  Description: An identifier representing the payment transaction associated with the fact.
  Use Case: This key links to a payment dimension table, providing details about the payment method and related information.

customer_key:
  Description: An identifier representing the customer associated with the fact.
  Use Case: This key links to a customer dimension table, providing details about the customer, such as name, address, and other customer-specific information.

time_key:
  Description: An identifier representing the time dimension associated with the fact.
  Use Case: This key links to a time dimension table, providing details about the time of the transaction, such as date, day of the week, and month.

item_key:
  Description: An identifier representing the item or product associated with the fact.
  Use Case: This key links to an item dimension table, providing details about the product, such as category, sub-category, and product name.

store_key:
  Description: An identifier representing the store or location associated with the fact.
  Use Case: This key links to a store dimension table, providing details about the store, such as location, store name, and other store-specific information.

quantity:
  Description: The quantity of items sold or involved in the transaction.
  Use Case: Represents the amount or number of items associated with the transaction.

unit:
  Description: The unit or measurement associated with the quantity (e.g., pieces, kilograms).
  Use Case: Specifies the unit of measurement for the quantity.

unit_price:
  Description: The price per unit of the item.
  Use Case: Represents the cost or price associated with each unit of the item.

total_price:
  Description: The total price of the transaction, calculated as the product of quantity and unit price.
  Use Case: Represents the overall cost or revenue generated by the transaction.

Customer Table: customer_key:

Description: An identifier representing a unique customer.
Use Case: Serves as the primary key to link with the fact table, allowing for easy and efficient retrieval of customer-specific information.

name:

Description: The name of the customer.
Use Case: Captures the personal or business name of the customer for identification and reference purposes.

contact_no:

Description: The contact number associated with the customer.
Use Case: Stores the phone number or contact details for communication or outreach purposes.

nid:

Description: The National ID (NID) or a unique identification number for the customer.

Item Table: item_key:

Description: An identifier representing a unique item or product.
Use Case: Serves as the primary key to link with the fact table, enabling retrieval of detailed information about specific items in transactions.

item_name:

Description: The name or title of the item.
Use Case: Captures the descriptive name of the item, providing a recognizable label for the product.

desc:

Description: A description of the item.
Use Case: Contains additional details about the item, such as features, specifications, or any relevant information.

unit_price:

Description: The price per unit of the item.
Use Case: Represents the cost or price associated with each unit of the item.

man_country:

Description: The country where the item is manufactured.
Use Case: Captures the origin or manufacturing location of the item.

supplier:

Description: The supplier or vendor providing the item.
Use Case: Stores the name or identifier of the supplier, facilitating tracking of item sources.

unit:

Description: The unit of measurement associated with the item (e.g., pieces, kilograms).

Store Table: store_key:

Description: An identifier representing a unique store or location.
Use Case: Serves as the primary key to link with the fact table, allowing for easy retrieval of information about transactions associated with specific stores.

division:

Description: The administrative division or region where the store is located.
Use Case: Captures the broader geographical area in which...

It facilitates monitoring of the EU’s digital targets for 2030 set by the Digital Compass for the EU's Digital Decade, evolving around four cardinal points: skills, digital transformation of businesses, secure and sustainable digital infrastructures, and digitalization of public services.

The aim of the European ICT usage survey is to collect and disseminate harmonised and comparable information on the use of Information and Communication Technologies and e-commerce in enterprises at European level.

Coverage:

The characteristics to be provided are drawn from the following list of subjects:

• ICT systems and their usage in enterprises,
• use of the internet and other electronic networks by enterprises,
• e-commerce,
• e-business processes and organisational aspects,
• ICT competence in the enterprise and the need for ICT skills,
• barriers to the use of ICT, the internet and other electronic networks, e-commerce and e-business processes,
• ICT security and incidents,
• access to and use of the internet and other network technologies for connecting objects and devices (Internet of Things),
• access to and use of technologies providing the ability to connect to the internet or other networks from anywhere at any time (ubiquitous connectivity),
• use of Artificial Intelligence,
• use of Cloud computing,
• data analytics,
• use of 3D printing,
• use of robotics,
• use of social media,
• internet advertising
• ICT and the environment.

Breakdowns:

• by size class,
• by NACE Rev. 2 categories,
• by NUTS 2 regions (until 2010 and on optional basis since 2023)

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