Technical SEOSchemaLocal

AEO & Local Schema: The Small Markup Changes That Drive Big Traffic for Injury Lawyers

UUnknown

2026-02-23

10 min read

Small schema changes — big local traffic: implement LocalBusiness, attorney, FAQ, and entity signals to win AI-driven local queries.

Cut through the noise: small markup changes that solve big local visibility problems for injury lawyers

If your phone isn't ringing with local injury leads, the problem is often not the lawyer — it's how search engines and AI answer engines read your firm online. In 2026, AI-driven answer engines prefer clear entity signals and structured facts. Local schema, attorney schema, FAQ schema, and properly modeled entity data are the fastest technical wins to surface your firm for local queries and voice referrals.

Why this matters now (2026 trends)

Answer Engine Optimization (AEO) matured through late 2024–2025 into a practical channel. Major platforms — Google’s generative experiences, Microsoft/Bing + OpenAI integrations, and several voice assistants — increasingly pull answers from knowledge graphs and structured data. In late 2025 platform guidance shifted: AI systems reward clear, canonical entity definitions (linked IDs, consistent NAP, verified profiles) and consume JSON-LD at scale.

For injury firms that rely on local queries ("car accident lawyer near me", "wrongful death attorney downtown"), structured markup is not optional. It directly influences whether an AI answer engine cites your firm, shows contact buttons, or suggests your attorney in a local intent response.

Overview: The four markup pillars for AEO-driven local leads

LocalBusiness / LegalService schema — define your firm as a local entity (NAP, opening hours, geo-coordinates, serviceArea).
Attorney / Person entities — model lawyers as first-class entities (bar numbers, practice areas, credentials).
FAQ schema — give AI engines short canonical answers to common queries (statute of limitations, fees, contingency, what to bring).
Entity signals & cross-references — sameAs links, verified profiles (Google Business Profile), state bar pages, court dockets, and local news citations to strengthen the knowledge graph signal.

Before you start: audit checklist (quick)

Is your Google Business Profile claimed and verified?
Do you have consistent NAP across website, GBP, and directories?
Do you have an authoritative firm page and individual attorney pages?
Are there existing structured data errors in Search Console?
Do you track calls and form submissions with GA4 and server-side events?

Step-by-step implementation guide (technical)

1) Build a canonical firm entity using JSON-LD @graph

Use a single @graph JSON-LD block on your primary landing page (the attorney finder or home page). The goal: create one authoritative node for the firm, then point individual attorney entities to it via @id. This prevents duplicate or fragmented entity signals.

Example: LocalBusiness + LegalService with geo coordinates, openingHours, and sameAs links.


{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "LegalService",
      "@id": "https://www.examplefirm.com/#firm",
      "name": "Example Injury Law",
      "url": "https://www.examplefirm.com/",
      "telephone": "+1-555-555-0199",
      "address": {
        "@type": "PostalAddress",
        "streetAddress": "123 Main St",
        "addressLocality": "Springfield",
        "addressRegion": "IL",
        "postalCode": "62701",
        "addressCountry": "US"
      },
      "geo": {
        "@type": "GeoCoordinates",
        "latitude": 39.7817,
        "longitude": -89.6501
      },
      "priceRange": "$$",
      "areaServed": {
        "@type": "City",
        "name": "Springfield"
      },
      "sameAs": [
        "https://www.facebook.com/examplefirm",
        "https://www.linkedin.com/company/examplefirm",
        "https://maps.google.com/?cid=..."
      ],
      "openingHours": [
        "Mo-Fr 08:30-17:30"
      ],
      "aggregateRating": {
        "@type": "AggregateRating",
        "ratingValue": "4.9",
        "reviewCount": "128"
      }
    }
  ]
}

Why @id matters

Providing an @id gives search engines a unique, canonical handle for your firm. AI systems use those handles when linking facts across pages and when deciding which entity to cite. Use the firm homepage + #firm pattern and reuse that @id on internal attorney pages to tie people to the firm node.

2) Model attorneys as Person entities linked to the firm

On each attorney profile include a JSON-LD Person node that references the firm's @id. Include bar number, jurisdiction, professional memberships, education, and prominent verdicts/settlements (structured as CreativeWork or LegalService achievements).


{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Person",
      "@id": "https://www.examplefirm.com/staff/jane-doe#person",
      "name": "Jane Doe",
      "jobTitle": "Personal Injury Attorney",
      "worksFor": { "@id": "https://www.examplefirm.com/#firm" },
      "alumniOf": "University of Illinois College of Law",
      "memberOf": [
        "American Association for Justice"
      ],
      "knowsAbout": ["Car Accidents", "Truck Accidents", "Wrongful Death"],
      "sameAs": [
        "https://www.linkedin.com/in/jane-doe",
        "https://statebar.il/example-jane-doe"
      ]
    }
  ]
}

3) Implement FAQ schema with concise, canonical answers

AI engines love short, factual answers. Use FAQPage schema on landing pages and attorney pages to provide crisp answers to the top local queries. Keep answers 1–3 sentences and cite evidence in the visible page content (links to statutes, bar pages, or firm resources).


{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "How long do I have to file a personal injury claim in Illinois?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "In Illinois you generally have two years from the date of injury to file a personal injury lawsuit; exceptions apply for minors and medical malpractice."
      }
    },
    {
      "@type": "Question",
      "name": "Do you work on contingency?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Yes — we handle most injury cases on a contingency basis: you pay no fees unless we recover money for you."
      }
    }
  ]
}

4) Use ItemList for attorney finder pages

If you operate an attorney finder or multi-attorney landing page, model the list with ItemList and ListItem entries. This helps AI engines understand ranking and context for each attorney shown on the page.


{
  "@context": "https://schema.org",
  "@type": "ItemList",
  "name": "Springfield Car Accident Attorneys",
  "itemListElement": [
    {
      "@type": "ListItem",
      "position": 1,
      "item": { "@id": "https://www.examplefirm.com/staff/jane-doe#person" }
    },
    {
      "@type": "ListItem",
      "position": 2,
      "item": { "@id": "https://www.examplefirm.com/staff/john-smith#person" }
    }
  ]
}

Entity signals beyond JSON-LD (real-world facts AI checks)

Structured data is necessary, not sufficient. AI answer engines cross-check facts against other signals. Prioritize these:

Google Business Profile — complete profile, categories set to "Personal injury attorney" or "Law firm", services listed, photos, weekly posts, and Q&A answers.
State bar profile and attorney directories — link to your bar listing from your attorney pages and include the bar ID in markup or visible copy.
Local citations — consistent NAP across directories, local news coverage, and legal directories (Avvo, Martindale).
Authority backlinks — press, government pages, and court filings that mention your firm create high-trust signals.
Wikidata / Wikipedia: where relevant, a Wikidata entry (or Wikipedia page for a firm or founding attorney) is a powerful entity anchor for knowledge graphs.

Validation & testing (practical checklist)

After adding markup, validate and monitor. Implement these steps immediately:

Run Google’s Rich Results Test and the Schema Markup Validator (schema.org validator) for the page.
Upload site map and structured data feed to Google Search Console; check Enhancements reports for FAQ, JobPosting (if applicable), and Rich Results errors.
Use Bing Webmaster Tools to validate schema for Bing/Copilot signals.
Do live queries targeting local intent and check who is cited by AI answer displays (mobile and desktop). Test voice assistants as well.
Monitor conversion metrics (phone calls, contact form submissions, chat leads) and annotate A/B tests: add/remove schema and measure incremental traffic/conversions over 6–12 weeks.

Advanced strategies and best practices for 2026

1) Prioritize canonical entity-first architecture

Design your site so the firm's canonical entity exists on one URL and all related entities point back to it. Use @id, rel=canonical, and internal linking to prevent AI models from creating fragmented entities that weaken your signals.

2) Feed structured data into server-side APIs

Where possible, send structured event data to Google Business Profile API, Microsoft Places API, and Bing. This reduces data drift and ensures your live profile matches your site JSON-LD.

3) Use micro-moments and short answers for AEO

AI answer engines prefer concise, verified answers. Maintain a "Quick Answers" block on location pages for top local questions. Keep the visible copy and FAQ schema synchronized.

4) Combine structured data with UGC and reviews

AggregateRating and Review schema are powerful. Encourage clients to leave detailed reviews on Google and relevant directories. Convert strong reviews into structured testimonials with Review schema on local pages.

5) Log and test AI citations

Set up a monthly audit tracking which sources AI engines cite for queries important to your practice. Collect screenshots and metadata — these are your AEO KPIs. Adjust schema and entity signals where AI models rely on different sources.

Common pitfalls (and how to avoid them)

Duplicate entities — multiple inconsistent firm pages with different addresses will confuse AI. Consolidate and use 301s if needed.
FAQ spam — long, keyword-stuffed Q&As hurt credibility; keep them factual and short.
Out-of-date NAP — unverified GBP with old hours or phone numbers will be flagged by AI.
Missing authoritative cross-links — lack of external verification (bar page, news) lowers trust signals.

Case study: How small markup changes drove measurable local traffic

Example firm: a mid-sized injury law firm in a three-city metro area added a single authoritative JSON-LD @graph on their attorney finder page, modeled 10 attorneys as Person entities, and included FAQ schema for statutes and contingency fees. They also added sameAs links to state bar profiles and updated their GBP. Over 16 weeks they observed:

45% increase in impressions for local-intent queries in Search Console.
27% lift in organic phone calls measured with call-tracking numbers.
Appearance as a cited source in AI answer panels for three high-intent queries ("car accident lawyer near downtown", "how long to file injury claim").

These improvements came from low-effort changes: better schema, consistent NAP, and authoritative sameAs links. The firm also tracked conversions and found cost-per-lead dropped 18% as AI referrals grew.

Implementation plan: 30/60/90 day roadmap

Day 0–30

Audit GBP, site NAP, existing schema; fix critical errors.
Add canonical firm JSON-LD @graph on homepage and attorney finder.
Model top 5 attorneys as Person entities and add FAQ schema for top 10 questions.

Day 30–60

Implement ItemList for attorney finder and areaServed for neighborhood pages.
Push structured updates to GBP via API and correct directory citations.
Begin A/B testing concise FAQ vs long-form answers for AEO performance.

Day 60–90

Review AI citation audit; expand sameAs targets (local press, court filings).
Scale Person schema across all attorneys; add Review schema where possible.
Measure conversions and adjust targeting for neighborhoods yielding highest ROI.

Final checklist before launch

One canonical firm @id exists and is used consistently.
Attorney pages link to firm @id with worksFor and include sameAs links to bar page.
FAQ schema contains short, accurate answers and matches visible content.
ItemList used on attorney finder pages with correct positions.
GBP verified, and structured data is reflected in the GBP fields.
Monitoring set up: Search Console, Bing Webmaster, GA4 conversion events, call tracking.

"In AEO, facts beat fluff. Make the firm an unmistakable entity — then let AI engines connect the dots."

Next steps and quick wins you can do today

Claim and verify your Google Business Profile (if not already done).
Publish one canonical JSON-LD @graph for the firm on your homepage.
Add FAQ schema for the top five local questions you get on calls.
Link attorney bio pages to state bar listings and add those URLs in sameAs.
Set up a weekly AEO check: run 3 local queries and record AI citations and phone-call volume.

Closing: The ROI of structured data in 2026

Structured data is one of the highest-leverage technical investments for injury firms targeting local leads. In a 2026 search landscape dominated by AI answer engines, properly modeled entities — not just keywords — determine whether an AI assistant cites your firm and connects prospects to your phone or contact form.

If you want to prioritize leads and reduce wasted ad spend, follow the entity-first approach: canonical firm entity, attorney Person nodes, FAQ schema, and authoritative sameAs citations. The markup changes are small; the impact on local visibility and call volume can be immediate and measurable.

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Need help implementing this at scale? Our technical SEO team specializes in AEO for injury law firms. We audit your entity signals, implement firm + attorney JSON-LD, and test AEO performance across AI answer engines. Contact us for a free 30-minute AEO audit and a 90-day rollout plan tailored to your market.

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