LocalBusiness schema is the foundation — the specific @type, required properties, and @id structure that makes everything else work: LocalBusiness schema is the base entity schema that Google uses to identify, categorize, and trust your business across search and AI systems. The most common implementation errors that negate LocalBusiness schema value: using the generic '@type: LocalBusiness' when a more specific subtype exists (use 'Plumber,' 'Dentist,' 'HVACBusiness,' 'Attorney,' 'MedicalBusiness' — Google's schema.org type hierarchy has 100+ specific subtypes), missing the '@id' property that creates a stable entity identifier Google uses to associate your schema with your GBP and Knowledge Panel, omitting 'priceRange' which provides consumer-facing pricing context that appears in search results, and providing a generic 'description' that doesn't include primary service keywords and geographic context. The complete minimum LocalBusiness implementation includes: @context, @type (specific subtype), @id (your canonical homepage URL with /#business appended), name, url, telephone, address with full PostalAddress, geo with GeoCoordinates, openingHoursSpecification for each day, and priceRange. Validate implementation using Google's Rich Results Test (search.google.com/test/rich-results) after every schema change — the tool shows exactly which properties Google recognizes and flags errors preventing rich result eligibility. Use Semrush's Site Audit structured data report or Ahrefs' Site Audit to identify schema errors across all pages simultaneously rather than page-by-page manual validation.

Service schema on individual service pages tells Google and AI systems exactly what services the business offers in machine-readable format — the implementation that drives AI Overview citations: Service schema on individual service pages extends the LocalBusiness entity with specific, indexed service descriptions that Google's AI systems use when answering 'who offers X in [city]' queries. The Service schema implementation for a plumbing company's water heater replacement page: '@type: Service,' 'name: Water Heater Replacement,' 'description' of 150 to 200 words covering the service, common scenarios requiring it, and geographic context, 'provider' linking to the LocalBusiness @id, 'areaServed' listing the cities and ZIP codes where the service is offered, and 'hasOfferCatalog' with pricing context where appropriate. Service schema with 'areaServed' is the primary schema signal for service area businesses — it explicitly declares geographic service coverage to both search engines and AI crawlers. The 15 to 20 minutes spent implementing Service schema on each primary service page compounds over time as AI-generated search results increasingly cite structured service data. Use Google's Rich Results Test on each service page after implementation. Use Semrush's AI Visibility tracker or Ahrefs' AI Overview report to monitor whether Service schema additions produce increases in AI Overview citations for service + city queries. Track organic click changes in Google Search Console 8 to 12 weeks after schema implementation to confirm whether rich result eligibility is producing CTR improvements on target keywords.

FAQPage schema converts your Q&A content into directly extractable AI answers — the highest-impact schema type for AI Overview and featured snippet eligibility in 2026: FAQPage schema is the single schema type with the most direct and measurable impact on AI-generated search responses in 2026. Google's AI Overviews, Gemini responses, and featured snippets all draw directly from FAQPage schema when answering question-intent queries. The implementation: wrap each question and answer pair in '@type: Question' and 'acceptedAnswer' with '@type: Answer' properties within a parent '@type: FAQPage' object. Each question should be phrased exactly as a user would search it ('How much does HVAC repair cost in Phoenix?' rather than 'Pricing FAQ'). Each answer should be complete and standalone — answerable without reading surrounding content — since Google extracts these answers directly into AI responses. The answer length sweet spot is 50 to 150 words — long enough to be genuinely informative but short enough to be displayed without truncation in featured snippet and AI Overview contexts. Implement FAQPage schema on every page with Q&A content: service pages, location pages, blog posts, and the main FAQ page. Validate with Google's Rich Results Test and check for the 'FAQ' rich result type — when eligible, your search result can expand to show 2 to 3 Q&A pairs directly in the SERP, significantly increasing your SERP real estate. Monitor AI Overview appearance for your target FAQ keywords using Semrush's AI Visibility tracker to confirm schema additions are producing AI citation improvements.

AggregateRating schema displays star ratings in search results — increasing click-through rates by 15 to 30% when implemented correctly: AggregateRating schema tells Google to display your average star rating and review count directly in organic search results as rich snippet stars. The click-through rate improvement from star ratings in search results is well-documented and typically ranges from 15 to 30% improvement over equivalent listings without star ratings. The implementation: '@type: AggregateRating,' 'ratingValue' (your current average, e.g., '4.8'), 'reviewCount' (total review count, e.g., '127'), 'bestRating' ('5'), and 'worstRating' ('1'). Critical implementation rules: the rating data must reflect actual reviews displayed on the page — Google's guidelines prohibit using AggregateRating schema on a page that doesn't show individual reviews or a verifiable aggregate. The most compliant implementation: a testimonials page or service page that displays actual customer reviews with individual Review schema for each review, plus an AggregateRating as the aggregate. Use Google's Rich Results Test to verify AggregateRating is eligible for rich results on each page where implemented. Use Semrush's Position Tracking to compare click-through rates before and after AggregateRating implementation on target service pages — this confirms the CTR improvement is materializing as expected. Schema validation errors are common with AggregateRating — Ahrefs' Site Audit flags structured data issues with specific error descriptions that help debug implementation problems.

BreadcrumbList, Article, and ReviewSnippet schema complete the structured data foundation — and each serves a distinct function in how Google displays and understands your content: BreadcrumbList schema tells Google the hierarchical relationship of your pages, enabling breadcrumb trail display in search results ('Home > Services > Plumbing > Water Heater Replacement') that provides users and Google with page context. Implementation is straightforward: a nested array of ListItem objects with 'position,' 'name,' and 'item' properties for each level of the page hierarchy. Article schema on blog posts signals to Google that the content is authored journalism or editorial content, enabling the Article rich result type (byline, date, thumbnail) that improves SERP visual presence for blog content. Every blog post should have Article schema with 'author' linking to the Person @id of the content author, 'datePublished,' 'dateModified,' and 'publisher' linking to the Organization @id. Review schema on individual testimonial or review items (separate from AggregateRating) allows Google to display individual review snippets in AI-generated business responses. Use Google's Rich Results Test on a representative sample of each page type after full schema implementation. Run Semrush's Site Audit structured data report monthly to catch schema errors introduced by CMS updates, template changes, or new page additions. Use Ahrefs' Site Audit structured data section to verify that schema is correctly formatted across all pages rather than just the pages you manually validate.