Store Network: 12,180 locations span 45 states and territories.
Top Market: California leads with 1,828 stores nationwide.
City Coverage: 3,274 cities contain 7-Eleven locations.
Competitive Edge: 7-Eleven exceeds major competitors in locations.
Data Intelligence: Coordinates enable detailed geographic analysis.
The U.S. convenience-store landscape offers valuable location, geographic, and competitive intelligence for retailers, investors, delivery platforms, and market researchers. The available 2026 location dataset records 12,180 7-Eleven stores across 45 states and territories and 3,274 cities, with California representing the largest concentration at 1,828 locations. This makes 7-Eleven Store Locations Data Scraping In USA 2026 useful for analyzing store density, regional expansion, population coverage, and competitive positioning.
Scrape 7-Eleven US Store Data to provide structured information such as store names, street addresses, cities, states, ZIP codes, phone numbers, coordinates, country, and update dates. Through 7-Eleven Store Location Data Extraction, businesses can transform scattered location information into standardized datasets for mapping, territory planning, competitor benchmarking, and retail intelligence.
The 2026 dataset indicates that 7-Eleven maintains a substantial physical footprint throughout the United States. California leads with 1,828 stores, followed by Texas with 1,454 and Florida with 1,157. Together, these three states account for approximately 36% of the total locations in the dataset.
The geographic distribution also reveals differences in store density. Virginia has 783 stores serving approximately 8.54 million people, while Illinois has 396 stores serving 12.67 million people. Such differences can help analysts identify areas with high convenience-store penetration and markets where additional retail opportunities may exist.
| Rank | State / Territory | Stores | Share of U.S. Locations | Population | Population per Store | Estimated Market Coverage |
|---|---|---|---|---|---|---|
| 1 | California | 1,828 | 15% | 39.51M | 21.61K | Very High |
| 2 | Texas | 1,454 | 12% | 29.00M | 19.94K | Very High |
| 3 | Florida | 1,157 | 9% | 21.48M | 18.56K | Very High |
| 4 | New York | 836 | 7% | 19.45M | 23.27K | High |
| 5 | Virginia | 783 | 6% | 8.54M | 10.90K | Very High |
| 6 | Ohio | 515 | 4% | 11.69M | 22.70K | High |
| 7 | Michigan | 493 | 4% | 9.99M | 20.26K | High |
| 8 | Illinois | 396 | 3% | 12.67M | 32.00K | Moderate |
| 9 | Colorado | 383 | 3% | 5.76M | 15.04K | Very High |
| 10 | North Carolina | 375 | 3% | 10.49M | 27.97K | Moderate |
The dataset shows that stores are distributed across 45 states and territories, while 11 listed states and territories have no locations. These geographic differences are important for territory planning, regional benchmarking, and identifying underserved markets.
Businesses can use structured location intelligence to Extract Number Of 7-Eleven Stores In USA Data and monitor changes in store counts over time. Rather than analyzing locations individually, a standardized dataset makes it possible to aggregate outlets by state, city, ZIP code, or geographic region.
The 7-Eleven USA Store Location Dataset can also support geospatial analysis. Latitude and longitude fields allow analysts to map store clusters, calculate distances between locations, examine urban concentration, and compare 7-Eleven's footprint against competing convenience-store networks.
Store density becomes even more apparent when examining individual cities. Las Vegas leads the listed cities with 175 locations, followed by Dallas with 132 and Orlando with 122. Los Angeles, Chicago, and San Diego also demonstrate significant concentrations.
| Rank | City | State / Territory | Locations | Relative Concentration | Potential Intelligence Use |
|---|---|---|---|---|---|
| 1 | Las Vegas | Nevada | 175 | Very High | Territory benchmarking |
| 2 | Dallas | Texas | 132 | Very High | Competitive mapping |
| 3 | Orlando | Florida | 122 | Very High | Retail density analysis |
| 4 | Los Angeles | California | 114 | High | Urban market intelligence |
| 5 | Chicago | Illinois | 95 | High | Competitive benchmarking |
| 6 | San Diego | California | 88 | High | Regional expansion analysis |
| 7 | Denver | Colorado | 84 | High | Geographic opportunity analysis |
| 8 | Virginia Beach | Virginia | 77 | High | Local market monitoring |
| 9 | Austin | Texas | 73 | High | Store network analysis |
| 10 | Indianapolis | Indiana | 67 | High | Territory planning |
This city-level view demonstrates why location datasets are useful beyond simply counting stores. Retail analysts can identify clusters, compare metropolitan markets, evaluate competitive intensity, and monitor changes in geographic coverage.
7-Eleven Market Intelligence Data can combine location attributes with broader retail information to support competitive analysis. Store coordinates can be connected with demographic, traffic, neighborhood, and commercial datasets to create detailed market profiles.
Another important application is to Extract 7-Eleven Pricing Data. When location intelligence is combined with product and pricing information, businesses can analyze regional assortment differences, promotional patterns, convenience-store pricing, and competitive price positioning.
Chain Location Data provides a broader perspective when 7-Eleven is compared with other major convenience-store networks. The supplied 2026 comparison shows 7-Eleven ahead of Alimentation Couche-Tard, Circle K, Speedway, Casey's, and QuikTrip in total U.S. locations.
| Chain | U.S. Locations | States / Territories | Cities | Difference vs. 7-Eleven Locations | Difference vs. 7-Eleven Cities | Geographic Position |
|---|---|---|---|---|---|---|
| 7-Eleven | 12,180 | 45 | 3,274 | --- | --- | Largest listed footprint |
| Alimentation Couche-Tard | 6,852 | 46 | 2,253 | -5,328 | -1,021 | Large national network |
| Circle K | 6,846 | 47 | 2,241 | -5,334 | -1,033 | Broad state coverage |
| Speedway | 2,856 | 34 | 1,359 | -9,324 | -1,915 | Regional/national network |
| Casey's | 2,850 | 19 | 1,632 | -9,330 | -1,642 | Strong regional presence |
| QuikTrip | 1,230 | 22 | 457 | -10,950 | -2,817 | Concentrated footprint |
The comparison highlights an important distinction between store count and geographic reach. Although some competitors operate across more states, 7-Eleven has a substantially larger number of locations and cities in the supplied dataset. This makes location-level competitive benchmarking particularly valuable.
A comprehensive dataset can contain fields such as:
These fields enable multiple forms of analysis. Geographic coordinates can power store mapping, while ZIP codes can support regional segmentation. Phone numbers and addresses can assist with location verification, while update timestamps help maintain dataset freshness.
The sample records also demonstrate the importance of standardized address formatting. Multiple stores can exist within the same city or road corridor, making coordinates and complete addresses essential for distinguishing individual outlets.
The supplied dataset identifies several states and territories without 7-Eleven locations, including Alaska, Nebraska, Mississippi, Wyoming, Montana, North Dakota, Puerto Rico, Guam, American Samoa, the U.S. Virgin Islands, and the Northern Mariana Islands.
Such gaps can be valuable for market researchers. Absence of locations does not automatically indicate an expansion opportunity, but it creates a starting point for evaluating population, competition, logistics, consumer demand, and commercial infrastructure.
Location scraping can therefore support longitudinal analysis. By comparing datasets collected at different points in time, researchers can identify newly opened stores, removed locations, geographic shifts, and changes in regional concentration.
A structured location database can support several business applications. Retailers can benchmark store density against competitors, real estate teams can evaluate potential trade areas, and delivery businesses can identify serviceable geographic clusters.
Franchise and investment analysts can also examine city-level and state-level penetration. Mapping store coordinates against population or commercial zones can reveal areas with unusually high or low outlet concentration.
For data-driven organizations, combining location information with product, pricing, review, and demographic datasets creates a broader convenience-retail intelligence layer. This enables more sophisticated monitoring than a simple store-count report.
The 2026 dataset provides a detailed snapshot of the U.S. 7-Eleven network, covering 12,180 locations, 45 states and territories, and 3,274 cities. California, Texas, and Florida account for the largest store concentrations, while cities such as Las Vegas, Dallas, and Orlando demonstrate particularly strong location density.
For businesses seeking scalable retail intelligence, Grocery Data Scraping Services can extend location analysis into product availability, pricing, assortment, and competitive monitoring. Companies can also Scrape Online 7-Eleven Grocery data to complement physical store intelligence with digital product information and pricing signals.
Combining these sources with Food Data Scraping in the USA enables a broader view of convenience and grocery markets, helping organizations monitor retail footprints, compare competitors, identify market opportunities, and develop data-driven expansion strategies.
If you are seeking for a reliable data scraping services, Food Data Scrape is at your service. We hold prominence in Food Data Aggregator and Mobile Restaurant App Scraping with impeccable data analysis for strategic decision-making.
