answer-engine-optimization-playbook

Answer Engine Optimization (AEO) is the practice of ensuring your product is cited as the primary answer in Large Language Models (LLMs). Unlike traditional SEO, which focuses on winning a “blue link,” AEO focuses on being summarized as a top recommendation across multiple citations.

The AEO Core Principles

• Mentions Over Ranking: LLMs summarize multiple citations. To “win” an answer, you must be mentioned most frequently across the sources the LLM retrieves.
• The Long Tail is Back: While Google searches average 6 words, LLM prompts average 25 words. Optimize for highly specific, conversational follow-up questions.
• Zero Domain Authority Barrier: Early-stage startups can win AEO immediately by getting mentioned in a single trusted Reddit thread or YouTube video, bypassing the years of authority-building required for Google.

Step-by-Step AEO Workflow

1. Identify High-Intent Questions

Move beyond keywords to full questions.

• Mine Sales/Support Data: Identify the exact questions customers ask on calls or in support tickets. These reflect the “long tail” prompts they use in LLMs.
• Convert Paid Search Data: Take your high-converting PPC keywords and use an LLM to “Turn these keywords into the 10 most common questions a buyer would ask.”
• Target the “Follow-up”: Anticipate the second and third questions (e.g., “Does this integrate with Looker?”, “What is the specific pricing for 50 seats?”).

2. Optimize On-Site Content (The “Help Center” Strategy)

• Subdirectory vs. Subdomain: Move all help center and documentation content to a subdirectory (e.g., brand.com/help) rather than a subdomain (help.brand.com) to consolidate authority.
• Information Gain Heuristic: To avoid being filtered as “typical” AI spam, include original research, unique data points, or expert opinions that don’t exist in other citations.
• Answer the Tail: Create specific pages for obscure use cases (e.g., “How to use [Product] for [Specific Niche Use Case]”). These often become the sole citation for specific LLM queries.

3. Off-Site Citation Building

LLMs rely on Retrieval-Augmented Generation (RAG). You must appear in the “Search” results the LLM pulls.

• Reddit Strategy: Identify active threads related to your category. Provide authentic, high-value answers. Disclose your identity (“I work at Webflow…”) to maintain community trust and avoid being banned by anti-spam filters.
• YouTube/Vimeo: Create videos for non-glamorous, high-LTV B2B keywords. LLMs frequently cite video transcripts for technical “how-to” questions.
• Tiered Affiliates: Focus on “Listicle” sites (e.g., Dotdash Meredith, TechRadar, Forbes Advisor). If these sites mention you as #1, LLMs will likely summarize you as #1.

4. Setup AEO Tracking

LLM answers are non-deterministic; they change per run.

• Share of Voice (SoV): Track what percentage of the time you appear in the “citation pill” for your top 50 questions.
• Distribution Tracking: Ask the same question 5-10 times to see the distribution of answers.
• Post-Conversion Surveys: Because LLM traffic often looks like “Direct” or “Branded Search” in analytics, ask every new sign-up: “How did you hear about us?” specifically looking for mentions of ChatGPT/Perplexity.

Measuring Success via Experiments

Do not assume “best practices” work for your niche.

1. Select 200 target questions.
2. Split into a Control Group (100 questions, no action) and a Test Group (100 questions).
3. Intervene on the Test Group (e.g., add Reddit comments, create YouTube videos, update help docs).
4. Monitor “Share of Voice” for both groups over 4 weeks.
5. Scale the tactics that show a statistically significant increase in mentions compared to the control.

Examples

Example 1: B2B SaaS Integration

• Context: A user asks Perplexity, “What meeting transcription tool has a Looker integration?”
• Input: The product doesn’t have a native integration, but can use Zapier.
• Application: Create a help center article titled “Visualizing Meeting Sentiment in Looker via Zapier.”
• Output: Perplexity retrieves this specific page as the only relevant citation and tells the user: “You can use [Product] via a Zapier workaround to get data into Looker.”

Example 2: Local Marketplace

• Context: A user asks ChatGPT, “What’s the best dog-friendly restaurant in Austin with live music?”
• Input: Traditional SEO targets “Best restaurants Austin.”
• Application: Ensure the restaurant is mentioned in a Reddit thread “Dog friendly music spots Austin” and has “Dog Friendly” and “Live Music” tags in Schema.org markup on the site.
• Output: ChatGPT summarizes these attributes and places the restaurant in a clickable “carousel” card at the top of the chat.

Common Pitfalls

• Using 100% AI-Generated Content: LLMs and search engines increasingly filter for “typicality.” Pure AI content without a “human-in-the-loop” for editing and original data usually results in zero citations.
• Reddit Identity Faking: Creating 100 fake accounts to “shill” a product. Communities and LLM search-evaluation teams detect this pattern easily, leading to domain-wide bans.
• Ignoring the Conversion Gap: Thinking AEO isn’t working because “Referral” traffic is low. Users often see the answer in ChatGPT, then open a new tab to search for the brand directly. Always use “How did you hear about us?” surveys.
• Focusing on RAG vs. Core Model: Trying to “train” the model on your data. This is impossible for most. Focus entirely on the RAG (Search) citations, as this is what determines the real-time answer.