.WALMART Whois Database, discover comprehensive ownership details, registration dates, and more for .WALMART TLD with Whois Data Center.

Get access to the Walmart Basic Product Details Dataset, which includes essential information on a wide range of products available at Walmart.

This comprehensive dataset features product names, categories, descriptions, prices, and more. Ideal for market analysis, competitive research, and e-commerce applications.

Download now to enhance your data-driven strategies and insights with detailed Walmart product information.

The dataset having basic details of a dataset like title, id, image, price and descripton.

Records count: 2.5 million +

This is a layer provided by the Walmart Emergency Operations Center (EOC) to view store/club status.

According to the data, Walmart's net sales were forecast to be around *** billion U.S. dollars in 2021, following the upsurge in 2020 that was driven by COVID-19. From 2021 onwards, Walmart's net sales were forecast to increase with each consecutive year. By 2026, it was forecast that Walmart's net sales would grow to ***** billion U.S. dollars, which includes store-based and e-commerce net sales.

Walmart store status from the Walmart Emergency Operations Center.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Discover the Walmart Products Free Dataset, featuring 2,000 records in CSV format. This dataset includes detailed information about various Walmart products, such as names, prices, categories, and descriptions.

It’s perfect for data analysis, e-commerce research, and machine learning projects. Download now and kickstart your insights with accurate, real-world data.

The dataset comprises transactional information from previous 5 years from Walmart retail stores, with diverse details such as customer demographics, order specifics, product attributes, and sales logistics. It includes data on the city where purchases were made, customer age, names, and segments, along with any applied discounts and the quantity of products ordered. Each transaction is uniquely identified by an order ID, accompanied by order date, priority, and shipping details like mode, cost, and dates. Product-related information encompasses base margins, categories, containers, names, and sub-categories, enabling insights into profitability, sales, and regional performance. The dataset also provides granular details such as profit margins, unit prices, and ZIP codes, facilitating analysis at multiple levels like customer behavior, product performance, and operational efficiencies within Walmart's retail ecosystem.

The columns in dataset are:

1. City: The city where the purchase was made.
2. Customer Age: Age of the customer making the purchase.
3. Customer Name: Name of the customer.
4. Customer Segment: Segment to which the customer belongs (like retail, wholesale, etc.).
5. Discount: Any discount applied to the purchase.
6. Number of Records: The count of records for each transaction.
7. Order Date: Date when the order was placed.
8. Order ID: Unique identifier for each order.
9. Order Priority: Priority level of the order (like high, medium, low).
10. Order Quantity: Quantity of products ordered.
11. Product Base Margin: Base margin percentage for the product.
12. Product Category: Category to which the product belongs (like electronics, groceries, etc.).
13. Product Container: Container type of the product.
14. Product Name: Name of the product.
15. Product Sub-Category: Sub-category to which the product belongs.
16. Profit: Profit earned from the transaction.
17. Region: Region where the purchase was made.
18. Row ID: Unique identifier for each row.
19. Sales: Total sales amount.
20. Ship Date: Date when the order was shipped.
21. Ship Mode: Mode of shipping (like standard, express, etc.).
22. Shipping Cost: Cost associated with shipping.
23. State: State where the purchase was made.
24. Unit Price: Price per unit of the product.
25. Zip Code: ZIP code of the customer or store location.

Use our constantly updated Walmart products dataset to get a complete snapshot of new products, categories, pricing, and consumer reviews. You may purchase the entire dataset or a customized subset, depending on your needs. Popular use cases: Identify product inventory gaps and increased demand for certain products, analyze consumer sentiment and define a pricing strategy by locating similar products and categories among your competitors. The dataset includes all major data points: product, SKU, GTIN, currency,timestamp, price,a nd more. Get your Walmart dataset today!

• Don't worry about solving CAPTCHAs, rotating proxies or installing headless browsers
• No need to update scrapers with every minor or major website layout or design change

• Simple pricing, pay per successful result only. Say goodbye to being charged for failed requests.

• Filter results by number of reviews, date

• Review data includes meta data about customers such as avatar, location, profile url, etc.

• Get page meta data like product price information, rating distribution, etc.

In the fiscal year ended January 31, 2024, Walmart's global net sales amounted to 642.6 billion U.S. dollars, an increase of approximately six percent in comparison to a year earlier.

Walmart Walmart was founded in 1962 by Sam Walton when he and his brother James “Bud” Walton opened the first Wal-Mart Discount City in Rogers, Arkansas. Since then, Walmart has grown to become the largest publicly-owned retail company in the world. In the United States, the company includes Walmart discount stores, supercenters, neighborhood markets, and Sam’s Club warehouse membership clubs. The company also has many international operations. Walmart is considered a variety store which focuses on low prices featuring apparel as well as hard goods, and has been committed to upholding their basic value of customer service. Beginning in the early 1990s, Walmart went to great lengths to increase their market share. They introduced a full line of groceries into their stores, diversified their market by appealing to certain ethnic groups through bilingual advertisements, and took steps to promote the awareness of environmental issues.As of 2024, Walmart operated 10,616 stores worldwide; with 4,615 of those stores located in the United States alone.

Dataset Description :

This is the historical data that covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields:

Store - the store number

Date - the week of sales

Weekly_Sales - sales for the given store

Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week

Temperature - Temperature on the day of sale

Fuel_Price - Cost of fuel in the region

CPI – Prevailing consumer price index

Unemployment - Prevailing unemployment rate

Holiday Events Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Analysis Tasks

Basic Statistics tasks

1) Which store has maximum sales

2) Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation

3) Which store/s has good quarterly growth rate in Q3’2012

4) Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together

5) Provide a monthly and semester view of sales in units and give insights

Statistical Model

For Store 1 – Build prediction models to forecast demand

Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.

Change dates into days by creating new variable.

Select the model which gives best accuracy.

Senegal Google Search Trends: Online Shopping: Walmart data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. Senegal Google Search Trends: Online Shopping: Walmart data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 7.000 Score in 13 Jan 2022 and a record low of 0.000 Score in 14 May 2025. Senegal Google Search Trends: Online Shopping: Walmart data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Senegal – Table SN.Google.GT: Google Search Trends: by Categories.

Walmart products sample dataset having 1000+ records in CSV format. Download monthly dataset for walmart data and it having around 100K+ records.

Get 50% discount for all datasets. Link

Dataset

This dataset was created by Vishnupriya

Released under Apache 2.0

Contents

Open data hub with services and maps of Walmart store status (open/closed)

This dataset features product listings from Walmart.com, specifically those with available ingredient information, categorized for deeper analysis. With over 43,000+ enriched records, it offers a clear view into formulations across several high-impact sectors.

Category-wise Breakdown

Walmart reported $165.61B in Sales Revenues for its fiscal quarter ending in April of 2025. Data for Walmart | WMT - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Walmart reported $58.67B in Trade Creditors for its fiscal quarter ending in January of 2025. Data for Walmart | WMT - Trade Creditors including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Historical sales data for 45 Walmart stores located in different regions are available. There are certain events and holidays which impact sales on each day. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to inappropriate machine learning algorithm. Walmart would like to predict the sales and demand accurately. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc. The objective is to determine the factors affecting the sales and to analyze the impact of markdowns around holidays on the sales.

Holiday Events Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Analysis Tasks

Basic Statistics tasks 1) Which store has maximum sales

2) Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation

3) Which store/s has good quarterly growth rate in Q3’2012

4) Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together

5) Provide a monthly and semester view of sales in units and give insights

Statistical Model For Store 1 – Build prediction models to forecast demand (Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.) Change dates into days by creating new variable. Select the model which gives best accuracy.