Walmart is a well-known retailer known for offering products from various categories, including · grocery, clothing, shoes, accessories, health & bodycare pharmacy, and more. Walmart contains a vast amount of products. Walmart has many stores in the USA. Extracting occasions, hours & pricing from this retail giant is useful to stay ahead of the curve. In this blog post, we will discover what data scraping is, the importance of data scraping for Walmart analysis, the total number of stores in the USA, and the best practices to extract Walmart website data.

What is Data Scraping?

Data Scraping refers to the use of tools and techniques to automatically pull out data from a competitor’s website. This data is a valuable asset for businesses, organizations, and retailers. Web scraping is a powerful way to stay competitive by relying on data. If you want to stay ahead of competitors and improve your business revenue, then data scraping is necessary.

Why Data Scraping Matters for Walmart Analysis?

Data scraping matters for Walmart Analysis due to the following reasons.

  • Store Locations: Walmart data scraping is an intelligent way to collect important datasets related to its locations, such as store name, address, city name, zip code, etc. It helps you with innovative location planning.

  • Operating Hours: Scraping Walmart can be used to extract store operating hours ( operating time, closing time, and special schedules) based on store type, such as supercenters, discount stores, neighborhood markets, Sam’s Club, holiday closures, Christmas Eve, and 24-hour stores.

  • Pricing Data: Walmart can be scraped to gain pricing information such as case price, discount price, promotional deals, membership pricing, regional variation, and more. This data is used for competitive benchmarking.

  • Customer Reviews: Store data can be used to collect user comments, digital media like photos and videos, & ratings. These insights are effectively used for sentiment analysis and to identify customer positive, negative, or neutral tone.

  • Scale Handling: Walmart has thousands of stores nationwide. Scraping data from these websites will provide a wide range of data related to location, hours, and pricing. It helps businesses to interpret patterns and relationships. You can scrape data from Walmart to get information about thousands of stores nationwide.

  • Real-Time Accuracy: Data scraping is efficient for collecting current Walmart website data. It helps you to gather the latest product costs. Data scraping can also help you monitor Rollbacks and discounts offered by Walmart stores. Scraping data helps you to know the store’s state-specific price shifts and make price-related decisions.

  • Regional Segmentation: By scraping data from the Walmart website, you can efficiently collect store count per state and their operating hours. It enables you to identify state-specific price differences or trends. Walmart scraping is useful to unlock local shopping partners and trailer offerings.

How Many Walmart Stores Are In The US In 2025?

Let’s have a look at the following table. It shows the total number of Walmart stores in the USA.

State & Territory Number of Locations Population Population Per Locations
Texas 509 (11%) 29.00M 56.97K
Florida 341 (7%) 21.48M 62.98K
California 273 (6%) 39.51M 144.73K
North Carolina 193 (4%) 10.49M 54.34K
Georgia 187 (4%) 10.62M 56.77K
Illinois 152 (3%) 12.67M 83.37K
Ohio 142 (3%) 11.69M 82.32K
Missouri 137 (3%) 6.14M 44.80K
Tennessee 136 (3%) 6.83M 50.21K
Pennsylvania 135 (3%) 12.80M 94.83K

Walmart Stores By City

City Name State / Territory Number of Locations
San Antonio Texas 28
Houston Texas 24
Orlando Florida 23
Brightest City called Las Vegas Nevada 21
El Paso Texas 18
Tucson Arizona 17
Jacksonville Florida 17
Cowtown called Fort Worth Texas 15
Phoenix Arizona 15
Oklahoma City Oklahoma 15

Geographic Expansion of Walmart

Region/State Walmart Store Count (Current Year) Walmart Store Count (Previous Year) Expansion Notes
Northeast 120 110 Walmart has a steady growth in urban centers
Midwest 300 280 Strong expansion in suburban areas
South 500 470 Walmart has a high growth driven by population increase
West 250 240 Expansion focused on coastal cities

Walmart Store Count By Format

Format Count Share of Total
Supercenters 3,570 68%
Discount Stores 370 7%
Neighborhood Markets 1,266 25%

Comparison of the Walmart Store with the Previous Years

The table below shows a comparison of the Walmart store with previous years.

Year U.S. Store Count Global Store Count Key Notes
2022 4,742 ~10,800 Slight contraction; 1 U.S. store closed
2023 ~4,600+ 10,623 Expansion steady; 380 distribution facilities
2024 ~4,600 10,800+ 7,200 item rollbacks; 1.6M U.S. associates
2025 5,206 10,797 Growth in Neighborhood Markets: 255M weekly visits

Walmart Store Competitor Analysis

Now, let’s understand the top 5 competitors of Walmart thoroughly.

Walmart Competitors Number of Stores Number of States Number of Cities
Dollar General 19,882
(+15,267 locations than Walmart)		 48
( -4 states than Walmart)		 7,698
(+5,062 cities than Walmart)
Family Dollar 8,427
(+3,812 locations than Walmart)		 48
( -3 states than Walmart)		 3,961
(+1,325 cities than Walmart)
Target 1,963
( -2,652 locations than Walmart)		 51
(-1 states than Walmart)		 1,298
( -1,338 cities than Walmart)
Ross Stores 1,775
( -2,840 locations than Walmart)		 45
(-7 states than Walmart)		 1,150
( -1,486 cities than Walmart)
Big Lots 1,392
( -3,223 locations than Walmart)		 48
( -4 states than Walmart)		 1,094
( -1,542 cities than Walmart)

Walmart Pricing Strategy in 2025

The table below will help you understand Walmart's pricing strategy.

Category Key Insight
Everyday Prices 10–25% below rivals
Grocery Essentials Lowest margin items
Discounts on Clothing Seasonal markdowns
Deals on electronics items Holiday rollbacks
Dynamic Pricing Online daily shifts
Holiday Sales Up to 50% off
Inflation Strategy Subsidized basics
Membership Perks Walmart+ fuel deals

What Are The Best Practices to Extract Walmart Website Data?

Scraping data from the Walmart website required a smart approach. These approaches are as follows:

  • Use Official APIs: It is good if you use Walmart's official API. This is the most reliable approach that helps you access the right structured data. It enables you to access continuous data with no hurdles. Because this is official, it will minimize parsing issues. This data scraping method ensures compliance by following site policies. The official API of Walmart is created for developers so that they can integrate it fast. This API not only exchanges data securely, but also provides large datasets. It can effectively match your enterprise needs.

  • Respect robots.txt: Like every website Walmart website also has robots.txt. This file contains the information for the search engine that bots can crawl. You have to adhere to it to stay in compliance with site rules. Respecting robots.txt helps you avoid triggering anti-bot measures. Adhering to this text file effectively reduces violation risks and supports reasonable data use. It can protect the server by preventing overload issues. This approach is useful to maintain a good site relationship. The use of robots.txt will help you better know data that you can use.

  • Rotate Proxies: If you wish to scrape Walmart data seamlessly, then you have to use a good VPN to avoid IP blocking. Many times, it happens that scraping data aggressively can create extensive server loads. By shifting the IP address or proxy, you will be able to balance server load and prevent slowdown. Rotating proxies empower you bypass restrictions to access required data. It helps you gather valuable data at large scale.

  • Implement Rate Limits: When you extract data from Walmart, you have to limit your scraping efforts to reduce server strain. It helps you to scrape the needed data without breaking. Implementing rate limiting shows that you are respecting the server bandwidth limit. It will help you reduce errors, and that means you will receive fewer failed requests. Scraping Walmart data by implementing a rate limit can help you maintain reliability by ensuring smooth operations.

  • Scrape Publicly Available Data: When scraping Walmart data, you have to scrape only publicly available data. You do not have to scrape private or personal data of the user because it violates ethical principles and privacy laws. Scraping only publicly available data builds your brand trust and minimizes legal issues. This data usage is safe for long-term usage.

Conclusion

Walmart is the biggest retail giant in the world. It has many stores in 19 countries, including Africa, Canada, Central America, and more. Walmart has a vast amount of products and is well-known for its EDLP(Every Day Low Prices) Strategy. Pulling out locations, hours, and pricing data helps you to understand the market trends and achieve your business mission.

Retailergators is the benchmark for extracting quality data from the Walmart website. If you want to gain the advantages of scraping this retail website, you can reach out.