Quick Audit: Check the 12 Entity Signals That Impact Local Search in 2026

Quick Audit: Check the 12 Entity Signals That Impact Local Search in 2026

Hook: If you’re tired of noisy, slow audits and want a fast, actionable scan that surfaces the entity signals feeding AI answers, this compact checklist gets you there in one pass. In 2026, AI answer engines no longer guess — they stitch together entity signals across the web. Missed or inconsistent signals cost you visibility in knowledge panels, AI summaries, and local answers.

Why entity signals matter now (the 2026 reality)

Search has evolved into Answer Engine Optimization (AEO). Leading guides updated in late 2025 and early 2026 make this clear: AI engines pull from knowledge graphs, verified profiles, and high-quality mentions to produce concise answers. That means the most influential signals are the ones that confirm an entity’s identity, attributes and trustworthiness across platforms—not just keyword-stuffed pages.

“Audiences form preferences before they search.” — Search Engine Land, Jan 16, 2026

AI models prioritize structured, consistent, and corroborated entity data. The checklist below focuses on the 12 high-impact signals that feed knowledge graphs and AI answers. Each item has a one-minute check, the tools to use, and the remedial step if something is off.

How to use this checklist (two-minute scoring)

Run through each signal quickly; score 0–2 per item (0 = fail, 1 = partial, 2 = good). Total possible = 24. Use the ranges below to triage:

• 18–24: Strong entity foundation. Focus on scale (reviews, PR).
• 12–17: Fix consistency and structured data next.
• <12: Urgent — core identity or citation issues likely blocking AI answers.

The 12 Entity Signals Checklist (scan & fix)

1) Core Entity Identity & Canonical Name

What to check: Does your site and primary profiles use the exact legal/brand name consistently? Do variations (LLC, Inc., punctuation) match where needed?

• Quick test: Search "site:example.com "Your Brand Name"" and ""Your Brand Name" -site:example.com" to see external usage.
• Tools: Google search, site search, grep/ctrl+F on CMS, Mention/Brand24.
• Fix: Pick one canonical brand name. Publish it in the site header, footer, and JSON-LD @id. Use sameAs links to key profiles.

Priority: Critical — inconsistent name fragments confuse AI entity resolution.

2) NAP (Name, Address, Phone) Consistency

What to check: Exact address format, phone format, suite numbers, and opening hours across website, Google Business Profile, and major citations.

• Quick test: Collect the top 10 citations (Yelp, Bing, Facebook, local directories) and compare formatting.
• Tools: Moz Local, BrightLocal, manual Google Maps checks.
• Fix: Standardize the canonical format on your website and push updates to major platforms. Normalize phone formats (E.164 recommended for schema).

Priority: High — NAP is a primary signal for local entity matching.

3) Google Business Profile (and other map profiles)

What to check: Category accuracy, primary/secondary categories, services list, attributes, photos, and verification status.

• Quick test: Is the GBP verified? Do attributes (appointment, delivery) reflect reality?
• Tools: Google Business Profile dashboard, Bing Places, Apple Business Connect.
• Fix: Correct categories and fill attributes. Add service-level schema that mirrors GBP services. Update photos monthly and respond to Q&A/reviews promptly.

Priority: Critical — GBP is heavily used by AI answers for local intent.

4) Structured Data & JSON-LD

What to check: Presence of LocalBusiness or relevant vertical schema, accurate properties (address, geo, telephone, openingHours, sameAs), and a valid @id.

• Quick test: Run the URL through the Rich Results Test and Schema.org validator.
• Tools: Google Rich Results Test, Schema Markup Validator, Lighthouse.
• Fix: Add or correct JSON-LD. Use consistent @id (canonical URL) and include sameAs links to verified profiles and Wikidata when available.

Priority: Critical — structured data is the direct signal set AI engines parse.

5) Knowledge Graph / Wikidata / Wikipedia Presence

What to check: Does the entity have a Wikidata QID and a Wikipedia page (if notable)? Is the QID referenced in your JSON-LD via sameAs or @id links?

• Quick test: Search "Wikidata Your Brand" or "site:wikidata.org "Your Brand"". Check for a Knowledge Panel on branded queries.
• Tools: Wikidata, Wikipedia, Google Knowledge Panel search.
• Fix: If eligible, create or clean Wikidata entries—add reliable sources, official website, and identifiers. If not eligible for Wikipedia, ensure Wikidata captures your official QID and references.

Priority: High — knowledge graph IDs are an anchor for AEO.

6) Reviews: Volume, Velocity & Sentiment

What to check: Review counts and recent reviews across GBP, Yelp, industry vertical sites; sentiment trends and response rate from the business.

• Quick test: Sort reviews by date and look at the last 90 days—are reviews steady, increasing, or stagnant?
• Tools: Google Business Profile insights, ReviewTrackers, BrightLocal.
• Fix: Implement structured review acquisition (post-visit SMS/email), respond to all reviews quickly, and flag fraudulent reviews. Add Review schema where appropriate.

Priority: High — review signals influence AI trust and answer snippets.

7) Citation & Directory Quality (not just quantity)

What to check: Presence in high-authority local and vertical directories—law, healthcare, hospitality—and whether listings are complete and consistent.

• Quick test: Pull the top 20 citations and mark completeness (hours, services, description, images).
• Tools: Moz Local, Yext (if used), manual checks for verticals (Avvo, Healthgrades, TripAdvisor).
• Fix: Prioritize fixing the top 10 authoritative listings and the top 3 vertical directories in your niche. Remove duplicates where possible.

Priority: Medium-High — low-quality citations add noise; authoritative citations add trust.

8) Local Pages & Service-Level Entity Evidence

What to check: Do location pages have unique content, service schemas, staff bios, and geo signals? Are pages thin or duplicated?

• Quick test: Open each location page. Look for unique paragraphs, service lists, and embedded maps.
• Tools: Screaming Frog (crawl for duplicates), Google Search Console (indexing), Copyscape for duplication checks.
• Fix: Create unique landing pages focused on local intent (city + service), include schema for OpeningHoursSpecification and Service, and add local testimonials or case studies.

Priority: High — location pages are where AEO pulls local claim evidence.

9) Backlink & Mention Graph

What to check: Authoritative backlinks from local publishers, chamber of commerce, local news, and industry sites. Also check co-mention patterns (brand mentioned next to local terms).

• Quick test: Run a link overview and sort by domain authority. Identify local media and partner links.
• Tools: Ahrefs, Majestic, Semrush, Google Search Console, Link Explorer.
• Fix: Prioritize outreach for local PR, sponsorships, and guest posts. Seek co-mentions on trusted local resources—the AI models favor corroborating mentions.

Priority: Medium — links provide corroboration and authority.

10) Social Profiles & Mentions

What to check: Verified profiles, profile metadata (bio links, address where supported), and search-visible social mentions (TikTok, X, Reddit, Instagram). Look for consistent handles and bios.

• Quick test: Check the top social platform profiles for completeness and the last 30 days’ activity.
• Tools: Native platform checks, CrowdTangle (for publishers), Social listening tools (Meltwater, Sprout Social).
• Fix: Standardize handles where feasible, link to canonical site, and pin or highlight local proof (events, service spots). Use structured data for social profiles in JSON-LD sameAs.

Priority: Medium — social mentions shape pre-search preferences and add corroboration.

11) Technical Health & Crawlability

What to check: Indexability, canonical tags, hreflang (if multi-loc/ multi-language), robots.txt, XML sitemap, page speed, mobile UX, and Core Web Vitals.

• Quick test: Run a Lighthouse audit and check the site: operator for indexing oddities.
• Tools: Google Search Console, Lighthouse, Screaming Frog, PageSpeed Insights.
• Fix: Correct canonical chains, unblock important paths, improve mobile speed and fix largest contentful paint issues. Ensure each location page is crawlable and returns 200 status.

Priority: Critical — AI relies on current, crawlable signals.

12) Visual Identity & Media Signals

What to check: Logo markup, consistent logo across profiles, structured image markup, and branded video content. Are images indexed and captioned with entity context?

• Quick test: Check the image alt text and file names for brand+location keywords. Verify logo appears in JSON-LD logo property.
• Tools: Google Images search, Rich Results Test, video platforms insights (YouTube/TikTok analytics).
• Fix: Add structuredImage objects in JSON-LD, standardize logo and cover images across profiles, and upload geo-tagged images where supported.

Priority: Medium — visuals are used by AI to create richer cards and answer snippets.

Rapid remediation playbook (next steps after scoring)

After you score, use this prioritized 30/60/90-day plan:

1. Days 0–30: Fix critical identity issues: canonical name, NAP, GBP verification, and JSON-LD LocalBusiness. Re-run Rich Results Test.
2. Days 31–60: Clean top 10 citations, optimize 1–3 location pages with unique content and service schema, and implement a review capture process.
3. Days 61–90: Drive local PR and link acquisitions, amplify social proof, and build Wikidata/QID presence if applicable.

Tools & one-command checks

Keep a small toolkit for speed. Below are the essential tools and quick commands for a one-person audit:

• Google Rich Results Test — validate JSON-LD and structured data output.
• site:example.com "Brand Name" — find external mentions and potential duplicates.
• "Your Brand" knowledge panel check — search branded queries and look for QID.
• Screaming Frog (crawl duplicates, indexability), Lighthouse (technical), and GSC (coverage + performance).
• BrightLocal / Moz Local — quick citation snapshot.

Real-world example (brief case study)

In late 2025 we audited a regional dental chain with conflicting NAP formats across 30 citations, missing LocalBusiness JSON-LD, and no Wikidata entry. After standardizing NAP, adding JSON-LD with the brand's canonical @id, and creating a Wikidata QID linked across profiles, the chain gained a Knowledge Panel for its flagship location within eight weeks and saw a 22% lift in map-driven calls. The lesson: small identity fixes produce outsized AEO impact.

Monitoring: keep the entity fresh

Entity signals are not set-and-forget. Set up monitoring to prevent drift:

• Weekly: GBP insights and review alerts.
• Monthly: Citation audit on top 20 listings.
• Quarterly: Structured data revalidation and Wikidata checks.

Advanced signals (2026 trends to watch)

As of early 2026, AI engines increasingly weigh these advanced signals:

• Co-citation graphs: How often your brand is mentioned near verified local points of interest.
• Entity temporal signals: Recency patterns in reviews and news coverage that indicate current relevance.
• First-party engagement: Session behavior on location pages—used as a proxy for real-world interest.
• Verified identifiers: Taxonomy IDs, government business registry links, and industry membership numbers—these are becoming high-trust evidence for AI.

Common pitfalls and how to avoid them

• Over-relying on quantity: High citation counts with inconsistent data create noise — prefer quality authoritative listings.
• Ignoring JSON-LD: Page content alone won’t be reliably parsed by AI. Structured data is the contract you sign with answer engines.
• Duplicated locations: Multiple GBP listings or duplicate pages fragment signals—consolidate and redirect.
• Not tracking temporal signals: If review velocity drops, AI may downgrade perceived activity—keep asking for timely feedback.

Checklist download & next action

Want a printer-friendly, one-page version of this audit? Download the free Entity Signals Checklist (PDF) at justsearch.online/checklists to keep in your audit toolkit. Use it on every client and location scan.

Final takeaways

In 2026, local discoverability is less about chasing keywords and more about proving a stable, corroborated entity across the web. The 12 signals above are the minimal set that AI answer engines use to decide whether to cite, summarize, or ignore you. Run the two-minute scan, prioritize fixes by score, and automate monitoring so your entity doesn’t drift.

Ready to run a quick audit? Start with the three critical fixes: canonical name + NAP normalization, Google Business Profile verification and category cleanup, and LocalBusiness JSON-LD with a persistent @id. Need help? Visit justsearch.online/audit to request a 15-minute review with our local AEO team.

Related Reading

• JioStar’s Record Quarter: What Streaming Growth in India Means for Global Media Investors
• Fail‑Safe Patching: Avoiding the 'Fail To Shut Down' Windows Update Pitfall
• Does Giving Up Alcohol Boost Testosterone? The Evidence and a Practical 30-Day Plan
• Patient Guide: Choosing a Homeopath in 2026 — What Credentials, Tools and Community Indicators Matter
• Playbook: Preventing Drift When AI-Based Task Templates Scale Across Teams