Scrape Red Robin Restaurants Data In USA for Restaurant Analytics

Introduction

The U.S. restaurant industry continues to evolve through changing consumer preferences, geographic expansion, delivery adoption, and increasingly data-driven location strategies. Within the casual dining segment, Red Robin remains a recognizable restaurant brand with a substantial physical footprint across the United States. As of March 11, 2026, the brand operates 464 restaurants across 45 states and territories and 406 cities, creating a valuable dataset for studying restaurant distribution, regional concentration, and market coverage.

For businesses analyzing this footprint, Scrape Red Robin Restaurants Data In USA to provide structured visibility into restaurant names, addresses, cities, states, ZIP codes, phone numbers, geographic coordinates, and update dates. Such information can support location intelligence, competitor benchmarking, territory planning, and restaurant market analysis.

Similarly, organizations can Scrape Red Robin Restaurant Locations In USA to understand where restaurants are concentrated and identify markets with comparatively high or low brand penetration. Geographic restaurant data becomes particularly useful when combined with population, demographics, commercial development, and competitive restaurant information.

Businesses can also Extract Red Robin Restaurant Count Data to measure the brand's presence by state and city. The March 2026 dataset shows California leading the country with 54 locations, followed by Washington with 36 and Pennsylvania with 31. These figures provide a foundation for understanding Red Robin's national restaurant network.

U.S. Red Robin Restaurant Footprint in 2026

The Number of Red Robin locations in the USA in 2026 stands at 464 restaurants as of March 11, 2026. The restaurants span 45 states and territories and 406 cities, demonstrating a broad national footprint while remaining concentrated in selected regions.

California accounts for 54 restaurants, approximately 12% of the total U.S. network. Washington follows with 36 locations, representing about 8%, while Pennsylvania has 31 restaurants, representing approximately 7%.

Texas, Colorado, Oregon, Michigan, Arizona, Ohio, and Virginia complete the top ten states and territories. Collectively, these markets illustrate how Red Robin's footprint is distributed across western, southern, midwestern, and eastern parts of the country.

Table 1: Red Robin Restaurant Distribution Across Leading U.S. States

The data shows an important distinction between total restaurant count and restaurant density. California has the largest number of Red Robin restaurants, but its population means there is approximately one restaurant for every 731,704 people. Washington and Oregon, by comparison, have considerably fewer residents per restaurant.

This makes restaurant-count analysis more useful when population metrics are included. A market with fewer restaurants may still demonstrate stronger geographic penetration than a larger state with a greater absolute restaurant count.

Geographic Concentration and Market Intelligence

Red Robin Restaurant Market Intelligence can be developed by combining restaurant locations with population, competitive density, commercial districts, shopping centers, delivery coverage, and local consumer demand. Such intelligence helps businesses move beyond simple location counting and evaluate the characteristics of individual markets.

California's 54 locations make it the largest state-level market by restaurant count. Washington's 36 locations are particularly significant relative to its population, while Oregon's 20 locations also represent substantial restaurant coverage relative to its population base.

Texas provides another useful example. Although it has 25 restaurants, its population of approximately 29 million produces a much higher population-per-restaurant figure than Washington or Oregon. This illustrates why raw restaurant counts should be interpreted alongside demographic and geographic variables.

Cities With the Highest Concentration

At the city level, Portland, Oregon, leads the available city ranking with four Red Robin restaurants. Albuquerque, Vancouver, San Antonio, Colorado Springs, Las Vegas, and Spokane each have three locations.

The city-level data can help analysts identify established urban clusters and examine whether restaurants are concentrated around large population centers, shopping corridors, entertainment districts, or suburban commercial areas.

Table 2: Selected U.S. Cities With Multiple Red Robin Locations

The city-level structure also highlights the importance of location-level datasets. A state may have a significant restaurant count, but individual cities can display very different levels of concentration. For market researchers, this supports city-by-city comparisons rather than relying solely on statewide figures.

States and Territories Without Red Robin Restaurants

The 2026 dataset identifies 11 states and territories without Red Robin restaurants: Puerto Rico, Vermont, Mississippi, West Virginia, District of Columbia, Wyoming, American Samoa, Guam, U.S. Virgin Islands, Northern Mariana Islands, and North Dakota.

These locations represent geographic areas without an identified restaurant in the dataset as of March 11, 2026. For expansion research, such information can be useful for identifying markets requiring further investigation. However, the absence of a location alone does not establish expansion potential because factors such as operating costs, consumer demand, real estate availability, competition, and corporate strategy also influence restaurant development.

Location-Level Data and Competitive Analysis

Red Robin Competitive Data Scraping can provide businesses with a structured way to monitor restaurant networks across competing casual dining brands. Location datasets can include restaurant names, street addresses, city and state information, ZIP codes, telephone numbers, latitude, longitude, country, and update dates.

The supplied records demonstrate the level of geographic detail available. For example, individual restaurants can be identified in Abilene, Green Bay, Eau Claire, Anchorage, Wasilla, Huntsville, Montgomery, and Woodburn. Each record can be mapped geographically and connected with additional market variables.

Such information can support territory mapping, competitor proximity analysis, franchise research, retail site selection, and geographic market segmentation.

The Role of U.S. Food Data Collection

Food Data Scraping in the USA enables researchers to build broader restaurant intelligence datasets beyond a single brand. Restaurant information can be combined across multiple chains, independent restaurants, delivery platforms, grocery businesses, and food-service providers.

For restaurant analysts, structured data collection can reveal location density, geographic gaps, menu changes, operating hours, delivery availability, pricing differences, and competitive movements. When collected consistently, this information can also support historical trend analysis.

Restaurant Data Fields for Business Intelligence

A comprehensive restaurant dataset can include:

• Restaurant name and location name
• Street address
• City and state
• ZIP code
• Telephone number
• Latitude and longitude
• Country
• Operating hours
• Restaurant category
• Menu information
• Delivery availability
• Last updated date

These fields can be combined with external demographic, economic, and competitive datasets to create a more comprehensive restaurant market intelligence framework.

Menu and Consumer Intelligence Opportunities

Restaurant Menu Data Scraping Services can extend location intelligence into product-level analysis. A restaurant location dataset identifies where a brand operates, while menu data can reveal what it sells, how products are positioned, and how offerings differ across markets.

For Red Robin and comparable restaurant brands, menu intelligence may include product names, categories, prices, descriptions, ingredients, nutritional information, modifiers, meal combinations, and availability.

This creates opportunities for menu benchmarking, price monitoring, product assortment research, and competitive analysis.

AI Restaurant Intelligence can further transform structured restaurant datasets into actionable analytical outputs. Machine-learning systems can classify restaurant categories, identify geographic clusters, detect menu changes, compare pricing patterns, and organize large volumes of restaurant information.

For businesses monitoring hundreds or thousands of restaurants, AI-assisted analysis can reduce the time required to identify meaningful changes across markets.

Cuisine and Menu-Level Analysis

Cuisine-wise Menu Data can help researchers compare restaurant offerings across different cuisines and dining categories. By organizing menus according to cuisine, meal type, dish category, ingredients, price bands, and dietary attributes, businesses can study consumer-facing product strategies at scale.

Although Red Robin is primarily associated with burgers and American casual dining, its menu-level data can be evaluated alongside other restaurant concepts to understand competitive positioning within broader food-service markets.

Key Insights From the 2026 Dataset

The March 2026 Red Robin dataset produces several important observations:

• 464 restaurants form the national footprint, covering 45 states and territories.
• California leads with 54 restaurants, accounting for approximately 12% of the U.S. network.
• Washington has 36 locations, making it the second-largest market by restaurant count.
• Pennsylvania has 31 restaurants, placing it third nationally.
• Oregon and Washington demonstrate high location density relative to their populations.
• 406 cities contain Red Robin restaurants, indicating a broad metropolitan and regional distribution.
• Eleven states and territories have no identified locations, creating a distinct geographic coverage pattern.
• Portland has four restaurants, the highest city count among the cities listed in the dataset.

Conclusion

The Number of Red Robin locations in the USA in 2026 provides a useful snapshot of the brand's geographic presence and market distribution. With 464 restaurants across 45 states and territories and 406 cities as of March 11, 2026, the dataset demonstrates a substantial but geographically concentrated casual-dining footprint.

For businesses seeking deeper restaurant intelligence, location records can be enriched with menu, pricing, delivery, demographic, and competitor information. Food Data Scraping Services can help organizations collect and structure these datasets for market research, location intelligence, competitive benchmarking, and ongoing monitoring.

Businesses can also Scrape Restaurant API Data to integrate structured restaurant information into dashboards, analytical platforms, research systems, and internal applications. Automated data pipelines can support recurring updates rather than relying on isolated snapshots.

Finally, Red Robin Gourmet Food Delivery App Data can complement physical restaurant location information by adding digital ordering, menu, pricing, availability, and delivery-related intelligence. Combining location and digital restaurant data creates a broader view of restaurant-market activity and enables organizations to analyze both physical footprint and digital food-service presence.

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.