Run a free restaurant SEO audit. Find missing FoodEstablishment schema, NAP inconsistencies, slow LCP from food photography, AI crawler gaps, and local search issues.
Run Free Audit →No credit card required · Results in under 2 minutes
A restaurant's primary SEO challenge is simple to describe and technically demanding to solve: when someone searches "best Italian restaurant near me" or "brunch spots in [neighborhood]," Google must instantly decide which local businesses have the strongest combination of relevance, distance, and prominence signals. Those three factors — the pillars of Google's local ranking algorithm — each have technical SEO components that most restaurant websites get wrong.
Getting local SEO right for a restaurant is not optional. Studies consistently show that over 70% of people who search for a local restaurant visit within a day. The difference between ranking in the local pack (the three map results that appear above organic listings) and ranking on page two is often a handful of fixable technical issues that no one has ever audited.
seo.yatna.ai crawls your restaurant website and evaluates it across 7 weighted categories — including the AI readiness checks that matter for the growing number of diners who ask ChatGPT or Perplexity for restaurant recommendations. Start a free audit and see exactly where your site falls short.
1. No FoodEstablishment schema
Google's structured data vocabulary includes FoodEstablishment (and subtypes: Restaurant, CafeOrCoffeeShop, FastFoodRestaurant, FoodTruck, etc.) specifically for food service businesses. Without this schema, your site is invisible to the rich result features — star ratings, price range, cuisine type, hours — that appear directly in Google results for restaurant queries. Most restaurant websites use a generic LocalBusiness type at best, missing the more specific type that Google prefers.
2. Missing openingHoursSpecification
Hours of operation are a critical local ranking and rich result signal. openingHoursSpecification in your schema markup tells Google precisely when you're open — including split shifts, seasonal hours, and holiday closures. Restaurant CMS templates often include hours as plain text in a footer widget, with no machine-readable markup. When your hours change seasonally and you update the text but not the schema, you can end up ranking for "open now" searches when you're actually closed.
3. Menu not marked up with hasMenu
The hasMenu property of FoodEstablishment schema allows you to link to your menu URL, and menu items can be marked up with MenuItem schema including name, description, offers (price), and suitableForDiet. Restaurants with markup on high-search-volume menu items ("prix fixe dinner menu," "gluten-free options") can rank for those specific queries. Most restaurant sites link to a PDF menu — not crawlable, not rankable, invisible to schema.
4. No AggregateRating schema from Google/Yelp review data
Review aggregation is one of the most visible SERP features for restaurants — the star rating that appears in search results. This requires AggregateRating schema on your page with ratingValue, reviewCount, and bestRating. Many restaurants embed a Yelp widget that renders reviews client-side in JavaScript, which Google cannot index. Properly implemented AggregateRating schema using your actual aggregated review data enables star display rich results.
5. NAP inconsistency between website and delivery platforms Your restaurant's Name, Address, and Phone number appear in dozens of places: your website, Google Business Profile, Yelp, TripAdvisor, DoorDash, Uber Eats, OpenTable. When your address format differs ("123 Main St" vs. "123 Main Street"), your phone number has changed, or your name is listed slightly differently ("Joe's Pizza" vs. "Joe's Pizza & Pasta"), Google's ability to consolidate these signals degrades. NAP inconsistency directly suppresses local pack rankings.
6. AI crawlers not configured
AI assistants are increasingly used to find dining recommendations. "What are the best farm-to-table restaurants in Brooklyn?" asked in Perplexity or ChatGPT returns AI-generated answers with citations — and the citations come from restaurants whose websites AI crawlers can access. If your robots.txt blocks GPTBot, PerplexityBot, or ClaudeBot (common on WordPress sites using aggressive security plugins), your restaurant is invisible to this growing discovery channel.
7. No llms.txt file
llms.txt is an emerging web standard that helps AI language models navigate your site — identifying your most important pages, your menu, your story, your hours, and your reservation link. A well-structured llms.txt pointing AI crawlers to your key content is a low-effort addition that increases your chances of being cited in AI-generated dining recommendations. Restaurants that adopt it early gain a meaningful advantage in AI search visibility.
8. Slow LCP from unoptimized food photography
Food photography is essential for restaurant marketing — but it's also the most common cause of catastrophic LCP scores on restaurant websites. A hero image of a signature dish uploaded at 4 MB and served at full desktop resolution to mobile users routinely produces LCP times of 6–8 seconds. Converting hero images to WebP, implementing responsive srcset attributes, and using a CDN with image optimization middleware typically moves LCP from failing to passing Core Web Vitals thresholds.
9. Multiple location pages with duplicate content Multi-location restaurants often create a page per location by cloning a template and swapping the address. When the page body — "Welcome to Joe's Pizza, where we serve the best hand-tossed pizza in [city]" — is identical across 8 location pages with only the city name changed, Google treats them as thin duplicate content. Each location page needs genuinely unique content: that location's specific story, chef, neighborhood context, and distinct menu items.
10. Missing geo coordinates in LocalBusiness schema
The geo property of LocalBusiness schema accepts latitude and longitude coordinates via GeoCoordinates. While Google can derive coordinates from your address, explicitly providing them in schema removes any ambiguity — particularly important for restaurants in dense urban areas, food halls, or locations where street addresses are ambiguous. Missing geo coordinates is a minor but consistently overlooked schema gap found in restaurant audits.
| Category | Weight | What We Check |
|---|---|---|
| AI Readiness | 20% | GPTBot/PerplexityBot access, llms.txt, AI-friendly hours and menu structure |
| E-E-A-T | 20% | Authorship on menu/story pages, chef credentials, health certification links |
| Technical SEO | 20% | Crawlability, robots.txt AI crawler config, sitemap, canonical tags on location pages |
| On-Page SEO | 15% | Title tags with cuisine type and city, meta descriptions, heading structure |
| Schema Markup | 15% | FoodEstablishment type, openingHoursSpecification, hasMenu, AggregateRating, geo |
| Performance | 5% | LCP on hero food photography, CLS from reservation widget loading, mobile speed |
| Images | 5% | Food photo compression, WebP, srcset, descriptive alt text for dish names |
A recent audit of a well-reviewed mid-range restaurant group with 4 locations returned an overall score of 47/100, with the following priority findings:
LocalBusiness — no Restaurant or FoodEstablishment subtype usedrobots.txt blocked all bots except Googlebot via a wildcard Disallow — GPTBot and PerplexityBot blockedllms.txt fileImplementing the schema, image optimization, and robots.txt fixes brought the audit score to 69/100 and measurably improved local pack appearance within 6 weeks.
Does schema markup help a restaurant appear in the local pack?
Schema markup is one signal among many in local pack rankings — proximity, review volume, and Google Business Profile completeness are typically larger factors. But FoodEstablishment schema with openingHoursSpecification and AggregateRating enables rich result features (hours, star ratings) that improve click-through rates, which is an indirect ranking signal. It's also a prerequisite for several AI search citation features.
My menu is a PDF — is that a problem?
Yes. PDF menus are not crawlable by Google or AI assistants, cannot be marked up with schema, and cannot rank for individual dish or menu-item searches. At minimum, link to an HTML menu page with text content. Ideally, implement hasMenu with MenuItem schema on individual items for maximum structured data coverage.
How important is food photography optimization for SEO? Extremely important for Core Web Vitals, which are a confirmed ranking signal. A restaurant site that fails LCP because of unoptimized hero images is being penalized in rankings relative to competitors with the same content and faster load times. Image optimization is consistently the highest-ROI technical fix for restaurant sites.
Are AI assistants really being used to find restaurants?
Increasingly yes. Perplexity, ChatGPT, and similar tools are seeing high engagement for "best [cuisine] in [city]" style queries — exactly the queries that drive restaurant discovery. These tools cite sources from sites they can crawl. Ensuring GPTBot and PerplexityBot are not blocked in your robots.txt is a 5-minute fix that can meaningfully expand your AI search visibility.
seo.yatna.ai audits your restaurant website in minutes — checking schema, performance, local SEO signals, AI crawler access, and 7 weighted categories. No installation required. First audit is free.
7 AI agents. 7 audit categories. One score. Free for your first audit.
Run Free Audit →