Keyword Strategy for AI Citations
Build a keyword framework designed for AI retrieval, not Google
Chapter 5 — Keyword Strategy for AI Citations
Most keyword research is built for Google. It optimises for search volume, ranking difficulty, and click-through rate. None of those metrics matter for Reddit-driven AI citations and using the wrong framework here means creating content that ranks nowhere in the conversations that actually drive AI answers.
This chapter builds a keyword strategy from scratch using the logic of how AI retrieval systems actually work.
AEO Keywords vs SEO Keywords
Traditional SEO keywords are built around what people type into a search bar: short, fragmented, often ambiguous. "CRM software," "email marketing tool," "project management app." These phrases optimise for a Google index that returns ten links for the user to evaluate.
AEO keywords Answer Engine Optimisation keywords are built around what people ask an AI assistant. They are full sentences. They carry intent, context, and often a specific constraint. "What's the best CRM for a five-person sales team that doesn't want to pay per seat?" is an AEO keyword. No one types that into Google. Everyone asks it to Perplexity.
The structural difference is significant: AEO keywords are conversational, comparison-driven, and problem-specific. They contain the audience, the use case, and the decision context in a single phrase. And because they mirror exactly how Reddit users phrase their questions in posts and comments, Reddit is the platform best positioned to answer them which is why AI models go there first.
Standard keyword tools like Semrush, Ahrefs, and Google Keyword Planner do not surface these phrases. They are built to measure search volume on short queries, not to capture the long-form conversational patterns of AI-directed research. Finding AEO keywords requires a different method.
The 4 Keyword Categories
Category 1 Comparison Queries
The highest-citation category. When someone asks an AI to compare two tools, the AI almost always retrieves a Reddit thread where a practitioner has done exactly that.
Structure: [Tool/Approach A] vs [Tool/Approach B] for [specific use case or audience]
Examples:
- Notion vs Coda for a remote team managing client projects
- Klaviyo vs Mailchimp for a DTC brand doing under $1M revenue
- HubSpot vs Pipedrive for a founder running their own sales
- Webflow vs WordPress for a marketing team without a developer
- Stripe vs Paddle for a SaaS company selling to European customers
Category 2 Recommendation Queries
The second-highest-citation category. These are the "what should I use" questions that buyers ask before they've made a decision. AI models synthesise Reddit recommendations into ranked answers.
Structure: best [product category] for [specific audience or constraint]
Examples:
- Best project management tool for an agency billing by the hour
- Best email platform for cold outreach with high deliverability
- Best CRM for a solo founder who hates admin
- Best analytics tool for a Shopify store doing its first $500K
- Best HR software for a startup hiring its first ten employees
Category 3 Problem Queries
These surface in threads where someone is actively stuck and asking for help. A well-placed, specific answer in one of these threads with your product as the solution earns both upvotes and AI citations for the problem/solution pairing.
Structure: how do I [fix/solve/deal with] [specific operational pain]
Examples:
- How do I stop losing leads between marketing and sales handoff
- How do I reduce churn when customers never finish onboarding
- How do I track team performance without micromanaging
- How do I set up email sequences that don't end up in spam
- How do I handle billing disputes from international customers
Category 4 Validation Queries
Buyers who have already shortlisted a product use these queries to pressure-test the decision. AI models pull Reddit sentiment directly into answers here making it critical to have positive, specific mentions in these threads.
Structure: is [product/approach] worth it / any good / legit for [context]
Examples:
- Is Intercom worth it for a startup with under 5,000 users
- Is hiring a fractional CMO actually effective for a Series A company
- Is SEO still worth investing in for a new SaaS in 2025
- Is Figma overkill for a two-person product team
- Is LinkedIn outreach still effective for B2B lead generation
How to Find These Keywords Using Free Methods
Reddit search. Go to Reddit and search a broad term in your category. Filter by "Top" and "All Time." Read the thread titles they are written in natural language by real buyers. The phrasing of high-upvote thread titles is your AEO keyword research.
Google site search. In Google, type site:reddit.com [your category] best OR vs OR worth it. The threads that rank on the first page of Google for these queries are already being cited by AI models. Their titles are your priority keywords.
Perplexity reverse-engineering. Ask Perplexity a buying question in your category. Look at what it cites. If it cites a Reddit thread, that thread's title and top comments contain the keyword patterns you need to replicate. This is the most direct signal available you are reading exactly what the AI retrieval system prioritised.
The Keyword-to-Content Map
Not every keyword warrants the same content response. Matching keyword type to content format determines whether you earn a citation or get scrolled past.
| Keyword Category | Best Content Response | Why |
|---|---|---|
| Comparison | Full post (Comparison Post format) | Needs depth, fairness, named entities across both options |
| Recommendation | Comment in existing thread | High-volume query threads already exist; joining beats creating |
| Problem | Comment or Case Study post | Specificity beats length; a solved-problem story cites better than a list |
| Validation | Comment in existing thread | Sentiment threads are live join them fast, in the first hour |
The general rule: if the thread already exists and has traction, comment. If no thread exists or existing ones are thin, create the post. AMAs work best for clusters of problem and validation keywords they generate multiple threads worth of answers in a single session.
The Priority Framework
Score each keyword on three dimensions, each rated 13:
- AI Citation Likelihood Is this a query someone would ask an AI? Does it appear in Perplexity results already? (1 = unlikely, 3 = highly likely)
- Audience Intent How close to a purchase decision is someone asking this? (1 = early awareness, 3 = active buying decision)
- Competition How many well-upvoted Reddit threads already address this exactly? (1 = saturated, 3 = underserved)
Multiply the three scores. Maximum is 27. Prioritise anything scoring 15 or above for Month 1 content.
How Reddifier Fits Here
Once your keyword list is scored and prioritised, enter the top 20 into Reddifier as a keyword workspace organised by category, with each category mapped to its corresponding subreddit tier from Chapter 3.
Reddifier monitors Reddit 24/7 for new threads matching these keywords and alerts you the moment a high-scoring thread is posted. This matters because validation and recommendation threads move fast a thread posted at 8am can have 50 comments and a locked-in upvote hierarchy by noon. Getting your comment in during the first hour is the difference between a top-voted citation-magnet and a buried response no one reads.
The keyword workspace in Reddifier also tracks how often each keyword surfaces across subreddits over time giving you a live signal of which topics are gaining momentum, so your content calendar stays ahead of the conversation rather than chasing it.
Keyword Scoring Table
Copy this into a spreadsheet. Score each column 13. Multiply for Priority Score.
| Keyword Phrase | Category | AI Citation Likelihood (1-3) | Audience Intent (1-3) | Competition (1-3) | Priority Score | Content Response | Target Subreddit | |---|---|---|---|---|---|---|---| | [Keyword 1] | Comparison | | | | | | | | [Keyword 2] | Recommendation | | | | | | | | [Keyword 3] | Problem | | | | | | | | [Keyword 4] | Validation | | | | | | | | [Keyword 5] | Comparison | | | | | | | | [Keyword 6] | Recommendation | | | | | | | | [Keyword 7] | Problem | | | | | | | | [Keyword 8] | Validation | | | | | | | | [Keyword 9] | Comparison | | | | | | | | [Keyword 10] | Problem | | | | | | | Scoring guide:
- AI Citation Likelihood: 1 = rarely appears in AI answers 2 = sometimes cited 3 = consistently cited from Reddit
- Audience Intent: 1 = early research 2 = evaluating options 3 = ready to decide
- Competition: 1 = many strong threads exist 2 = some threads, room to add 3 = underserved, no strong answer exists yet
- Priority Score = Column 3 Column 4 Column 5. Target = 15 for Month 1.