Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
日本語の概要は準備中です。原文の説明を表示しています。
Use when optimizing for AI search visibility where individual experts and entities outperform generic brand pages. Use when building cross-platform authority for people, organizations, or products that AI models should recognize and cite.
日本語の概要は準備中です。原文の説明を表示しています。
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Build entity authority so AI search surfaces your people, brand, and expertise — not just your pages. Based on Lily Ray's entity-first framework.
Use this skill when you need to:
This skill is particularly valuable for:
Source Expert: Lily Ray (VP of SEO Strategy, Amsive Digital; columnist, Search Engine Land)
Core Thesis: Entity-first replaces website-first. AI search models surface entities — people, organizations, concepts — not URLs. Individual experts with cross-platform digital footprints get disproportionately cited by AI.
"The shift isn't from SEO to GEO. It's from page-first to entity-first. AI models don't rank pages — they recognize entities and assess their authority across the entire web." — Lily Ray, The Search Session (Advanced Web Ranking)
Supporting Research:
| Claude Does | You Decide |
|---|---|
| Audits entity presence across platforms | Which entities to prioritize |
| Identifies gaps in entity establishment | Content and PR strategy budget |
| Generates structured data recommendations | Which platforms to invest in |
| Creates entity-building content plans | Expert credentials and experience to highlight |
| Maps entity relationships | Strategic positioning decisions |
When invoked, I will guide you through:
Before building, assess where you stand.
GOOGLE KNOWLEDGE PANEL
[ ] Does the entity have a Knowledge Panel?
[ ] Is the information accurate and complete?
[ ] Are notable works/credentials listed?
[ ] Is the entity connected to related entities?
WIKIDATA / WIKIPEDIA
[ ] Does a Wikidata entry exist? (Q-number)
[ ] Is the entity described with proper claims and references?
[ ] Does a Wikipedia article exist? (if notable enough)
SCHEMA MARKUP
[ ] Is Person/Organization schema present on owned sites?
[ ] Does schema include sameAs links to official profiles?
[ ] Is author markup on all published content?
AI SEARCH TEST
[ ] Ask ChatGPT: "Who is [entity]?" — does it know them?
[ ] Ask Perplexity: "[entity] expertise" — what sources cited?
[ ] Ask Google AI Overview: "[entity] + topic" — are they mentioned?
CROSS-PLATFORM PRESENCE
[ ] LinkedIn profile complete and active?
[ ] Guest appearances on podcasts/webinars?
[ ] Published articles on third-party sites?
[ ] Conference speaking engagements listed?
[ ] Professional directory listings?
| Level | Description | Signals |
|---|---|---|
| 0 — Unknown | AI doesn't recognize the entity | No Knowledge Panel, no AI mentions |
| 1 — Emerging | AI has partial information | Some mentions, no consistent identity |
| 2 — Established | AI recognizes and can describe | Knowledge Panel exists, AI answers are accurate |
| 3 — Authoritative | AI cites as a source | Consistently referenced in AI responses on topic |
| 4 — Definitive | AI uses as primary reference | Top-of-mind entity for the topic domain |
Minimum viable entity:
Entity-reinforcing actions:
Minimum viable entity:
Minimum viable entity:
"Product review pages on authoritative publishers are MORE valuable now than before AI search. AI Overviews synthesize reviews — if you're not in the review set, you don't exist." — Lily Ray
AI models don't just crawl your website. They learn from the entire web. Cross-platform content is structural, not optional.
| Platform | Entity Signal Strength | Why |
|---|---|---|
| High | Professional identity, endorsements, content | |
| Podcast appearances | High | AI transcription creates entity co-occurrence |
| Guest articles | High | Third-party validation of expertise |
| Conference talks | Medium-High | Listed on event sites, referenced in recaps |
| YouTube | Medium | Transcripts + visual identity |
| Twitter/X | Medium | Real-time expertise signals |
| Reddit (as expert) | Medium | Cited by Perplexity frequently |
| Professional directories | Low-Medium | Structured entity data |
Every platform should reinforce the same entity identity:
| Content Type | Entity Signal | Example |
|---|---|---|
| Original research | Strongest — unique data creates citation necessity | "Our analysis of 10,000 campaigns shows..." |
| Expert interviews | Strong — entity co-occurrence with recognized experts | "I spoke with [Expert] about..." |
| Case studies | Strong — first-hand experience signal | "Here's how we achieved X for [Client]..." |
| Conference recaps | Medium — positions entity in professional context | "At [Conference], the key insight was..." |
| Contrarian analysis | Medium — differentiation from consensus | "The conventional wisdom is wrong because..." |
| How-to guides | Low-Medium — helpful but interchangeable | Commodity content unless paired with unique data |
Google AI Mode, AI Overviews, and Google Discover all display favicons prominently. Your favicon is the new meta description — it's the visual identifier in AI-generated results.
Actions:
| Metric | How to Track | Frequency |
|---|---|---|
| Brand search volume | Google Search Console, Google Trends | Monthly |
| Knowledge Panel accuracy | Manual check | Quarterly |
| AI mention test | Ask ChatGPT/Perplexity/Claude about entity | Monthly |
| Citation in AI Overviews | Search "[topic]" and check AI Overview sources | Weekly |
| Cross-platform consistency | Manual audit of all profiles | Quarterly |
| Branded traffic | Analytics — filter for brand terms | Monthly |
Context: SEO consultant wants AI search to recognize them as an expert on GEO.
Entity Audit Results:
Action Plan:
Expected Timeline: 3-6 months to reach Level 2 (Established)
Context: Marketing analytics tool wants AI to recommend them when users ask about marketing attribution.
Entity Audit Results:
Action Plan:
Expected Timeline: 6-12 months to appear in AI recommendations
PHASE 1: FOUNDATION (Week 1-2)
[ ] Canonical entity page created on owned site
[ ] Person/Organization schema deployed
[ ] All platform profiles consistent (name, photo, description)
[ ] sameAs links connecting all profiles
[ ] Wikidata entry created (if applicable)
PHASE 2: THIRD-PARTY SIGNALS (Month 1-3)
[ ] 2+ guest articles published on authoritative sites
[ ] 3+ podcast appearances completed
[ ] Professional directory listings updated
[ ] Expert quotes obtained in news/industry articles
PHASE 3: CONTENT AUTHORITY (Month 2-6)
[ ] Original research published
[ ] 5+ entity-reinforcing content pieces live
[ ] Cross-platform content calendar running
[ ] Case studies with named clients published
PHASE 4: MEASUREMENT (Ongoing)
[ ] Monthly AI mention test
[ ] Brand search volume tracked
[ ] Knowledge Panel monitored
[ ] Citation tracking in AI Overviews
Primary Sources:
Related Concepts:
name: entity-seo-playbook
category: seo-tools
subcategory: geo
version: 1.0.0
author: GUIA
source_expert: Lily Ray (VP SEO Strategy, Amsive Digital) — The Search Session podcast
difficulty: intermediate
mode: centaur
tags: [entity-seo, geo, ai-search, knowledge-graph, cross-platform, digital-pr, e-e-a-t, lily-ray]
created: 2026-02-10
updated: 2026-02-10
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Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
日本語の概要は準備中です。原文の説明を表示しています。
Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
日本語の概要は準備中です。原文の説明を表示しています。
Assess customer account health using product usage, support sentiment, payment status, and relationship signals
日本語の概要は準備中です。原文の説明を表示しています。
Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
日本語の概要は準備中です。原文の説明を表示しています。
Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
日本語の概要は準備中です。原文の説明を表示しています。