Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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Use when creating content that must be discoverable by AI search engines (ChatGPT, Perplexity, Gemini). Use when SEO alone isn't enough, when you need AI citations, or when optimizing for the "zero-click" future.
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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Create content optimized for AI-powered search using Generative Engine Optimization (GEO) principles.
Create content optimized for both traditional search engines AND AI/LLM-powered search (ChatGPT, Perplexity, Google AI Overviews, Claude) using Generative Engine Optimization (GEO) principles.
Generative Engine Optimization = Practices to ensure AI models:
Strategic Importance: Gartner predicts 25% drop in traditional search volume by 2026. Queries now average 23 words (vs. 4 before).
| Criteria | SEO (Traditional) | GEO (New Frontier) |
|---|---|---|
| Objective | Rank page on Google SERP | Become the SOURCE in AI response |
| Metric | Position on results page | Mention/citation in generated response |
| Mechanic | Links, backlinks, domain authority | Language, semantics, contextual relevance |
| User Result | Click to your site | Info consumed without click |
| Optimization | Keywords, tags, structure | Semantic clarity, E-E-A-T, direct answers |
Principle: AI must understand CONTEXT, not just keywords.
Actions:
❌ "Our SEO GEO optimization ranking solution"
✅ "GEO (Generative Engine Optimization) optimizes content so AI like
ChatGPT cites your brand in their responses."
Principle: Provide key answer at the BEGINNING, not the end.
Actions:
Optimal Structure:
## [Question]
[Direct answer in 1-2 sentences]
[Detailed development]
[Concrete examples]
[Conclusion/Action]
Example:
## What is ethical copywriting?
Ethical copywriting is a persuasive writing approach that excludes
manipulation and is founded on transparency, honesty, and respect
for the prospect.
[Details follow...]
Principle: AI favors content that's easy to analyze ("parse").
✅ "## How to create a landing page that converts?"
❌ "## The secret marketers don't want you to know"
E-E-A-T = Expertise + Experience + Authority + Trustworthiness
Concrete Signals to Integrate:
"According to a [University X] study published in [year]..."
"Based on our analysis of [X] clients over [period]..."
"Source: [URL of official document]"
"Last updated: [date]"
Data:
Ideal GEO Article Structure:
1. Intro with direct answer (100-150 words)
2. Definition/Context (200-300 words)
3. In-depth development (1500-2000 words)
- Subsections with H2/H3
- Concrete examples
- Tables/lists
4. Case studies/Proof (300-500 words)
5. Actionable conclusion (100-150 words)
Principle: AI favors RECENT information.
Actions:
Principle: Facilitate extraction and citation by AI.
Techniques:
❌ "That's 37% more."
✅ "Ethical copywriting increases conversion rates by 37% on average
compared to manipulative approaches."
# [H1: Clear Title with Primary Topic]
[Opening paragraph with direct answer to main query]
## Quick Summary
- **What:** [One-line definition]
- **Why it matters:** [Key benefit/importance]
- **Key takeaway:** [Main action or insight]
## Table of Contents
[For posts >1500 words]
## [H2: First Major Section]
[Direct answer/definition first, then elaboration]
### [H3: Supporting Detail]
[Expand with examples, data, explanations]
## [H2: How to [Action]]
1. **Step 1:** [Clear instruction]
- [Supporting detail]
2. **Step 2:** [Clear instruction]
- [Supporting detail]
## [H2: Comparison Section] (if applicable)
| Aspect | Option A | Option B |
|--------|----------|----------|
| [Criteria] | [Value] | [Value] |
## Key Takeaways
- [Bullet summary 1]
- [Bullet summary 2]
- [Bullet summary 3]
## Frequently Asked Questions
### [Natural language question]?
[2-3 sentence direct answer]
### [Natural language question]?
[2-3 sentence direct answer]
## Conclusion
[Summary + clear call to action]
---
*Last updated: [Date]*
*Sources: [Citations]*
For any topic, consider addressing:
Simultaneously optimize for:
❌ Keyword stuffing → AI detects and devalues ❌ Clickbait headlines → AI seeks descriptive info ❌ Superficial content → AI prefers depth ❌ Absence of sources → Trustworthiness questioned ❌ Unexplained jargon → Missing context ❌ Buried answers → AI scans the beginning
| Claude handles | You provide |
|---|---|
| Applying GEO structure and formatting | Topic expertise and unique insights |
| Creating FAQ sections from content | Validation of accuracy |
| Optimizing for semantic clarity | E-E-A-T signals (credentials, experience) |
| Suggesting citable sentence structures | Source verification and dates |
| Generating comparison tables | Strategic decisions on positioning |
| Pass | Focus | Action |
|---|---|---|
| 1st | Structure | Apply GEO template, answer-first format |
| 2nd | E-E-A-T | Add citations, credentials, dates |
| 3rd | Citability | Make sentences autonomous and extractable |
| 4th | Checklist | Run GEO checklist to verify all elements |
name: llm-optimized-content
category: seo-tools
subcategory: geo
version: 2.0
author: GUIA
source_expert: GEO Framework (Gartner, industry research)
difficulty: intermediate
mode: cyborg
tags: [geo, seo, llm, ai-search, content, perplexity, chatgpt]
created: 2026-01-28
updated: 2026-02-03
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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。