How Quoted turns AI citation data into a sustained traffic advantage for your business
The methodology, the four deliverables you receive, and how the work compounds into a proprietary research asset specific to your business.
Everyone tracks citations. We track why they persist.
Most AEO platforms answer a binary question: are you being cited? That's useful, but it's a snapshot. It tells you what happened, not why—and not what to do about it.
Our research is built around a different question: what content characteristics predict whether a citation persists or decays over time? Answering that requires longitudinal data, structural analysis, and statistical rigor—not a single crawl.
We track the questions your buyers actually ask AI
Every research program starts with a prompt set—typically 100+ questions per vertical, covering the full buyer journey:
"What is [concept]?"
Questions from buyers just entering the market. These prompts test whether AI engines recognize your brand as a foundational source in your space.
"How does X differ from Y?"
Questions from buyers building a shortlist. These prompts reveal whether AI engines trust your explanations over your competitors' content.
"X vs. Y" and "best [category]"
Prompts from buyers evaluating options. High commercial intent. These are the citations that directly influence purchase decisions.
"Is [product] worth it for [use case]?"
Prompts from buyers ready to commit. Being cited here means being part of the final consideration set—the last mile before purchase.
Where the prompts come from
Most AEO tools let you type in prompts and hope they're the right ones. The Prompt Index is our proprietary process for building a verified, data-backed prompt set unique to your vertical and your buyers. It draws from five sources:
Community Intelligence
We mine buyer questions from Reddit (r/cybersecurity, r/marketing, r/SaaS), G2 reviews, PeerSpot discussions, and AnswerThePublic. These capture how real buyers phrase questions in their own language—not marketing jargon, not RFP language.
Search Console Data
We connect your Google Search Console to identify the queries your buyers are already searching. Research shows 77% of Google page-1 queries also appear in AI engine responses for the same topic. Your existing search data is one of the best predictors of AI prompts.
Team & Customer Polling
We ask 3–5 people on your team and your customers to share their recent AI search history for your product category. Even 50 real queries from actual buyers are more valuable than 1,000 guesses. These surface the exact phrasing and intent that no external tool can capture.
Sales Conversations
Your sales calls contain the exact questions buyers ask before purchasing. We analyze transcripts from Gong, Chorus, or call notes to extract every product and category question. These are the highest-intent prompts—the ones that directly influence deals.
AI Search Volume Data
We cross-reference every candidate prompt against AI search volume data to validate that real people are actually asking these questions. Prompts that nobody searches for don't make the index—regardless of how good they sound on paper.
Every prompt is run in three variants—because a generic question and a buyer’s real question return almost nothing in common.
The 5-source Prompt Index gives us the right questions. But how a buyer actually phrases the question matters as much as the question itself. We tested this directly. The result was sharp enough that it changed how we run every engagement.
In a 45-call smoke test across cybersecurity questions, we ran each prompt three ways: generic (the bare question), persona-prefixed (with a one-line buyer-role prefix), and persona-rewritten (the question phrased the way a real healthcare-CISO would type it). On average, generic and persona-rewritten variants of the same underlying question shared 0% of their citations. Two parallel universes of sources for the same buyer intent.
This finding is why every prompt in your Index is tracked in all three variants every week. Generic-prompt monitoring (what Profound, Stacker, and BrightEdge ship by default) measures a different citation graph than the one your buyers actually encounter in their own sessions. Tracking only the generic variant means optimizing toward sources your buyer will never see.
Read the full methodology and raw data: Smoke Test: Is Your AEO Software Tracking Real Citations? →
Operational implication: Your monthly performance read reports citations across all three variant types per prompt, not pooled. You and your client can see where you appear in the generic citation universe AND in the persona-aware one—and where the gap is.
What we collect, every week, across every engine
Prompt execution
Every week, we run 100+ prompts across three major AI engines. We capture the full response, including every source cited, its position in the answer, and the context in which it appears.
Source analysis
For every URL cited in every response, we scrape and analyze the page across 50+ structural and contextual features: content length, heading structure, schema markup, entity density, definitiveness, recency, and more.
Persistence tracking
We track the same prompts, on the same engines, week after week—measuring which URLs continue to appear, which drop off, and which new sources replace them. This is the longitudinal layer that makes our data unique.
Competitive mapping
We track which brands co-appear in the same AI responses and how citation share shifts over time. This reveals who you're actually competing against for AI visibility.
Engine behavior profiling
Each AI engine has distinct citation preferences. ChatGPT favors consensus sources. Perplexity leans on real-time content. We profile each engine separately so nothing gets missed.
Content feature scoring
Every cited URL receives a feature score across all tracked dimensions. Over time, we identify which features consistently predict citation persistence in your vertical—and which are noise.
From raw data to citation contributors
Raw citation data tells you what happened. Our analysis layer tells you why. We call the output citation contributors—the specific content attributes that statistically correlate with citation persistence in a given vertical.
Feature extraction
Every cited URL is analyzed across 50+ structural and contextual features. Some are obvious (word count, schema markup). Others are specific to how AI engines process content and are part of our proprietary research.
Correlation analysis
We compare the features of URLs that persist in citations against those that decay. Over weeks and months of longitudinal data, statistical patterns emerge—certain content characteristics consistently predict persistence, and they vary by vertical.
Playbook generation
The citation contributors become prescriptive recommendations: what to publish, how to structure it, which gaps to fill. These aren't generic best practices—they're specific to your prompts, your vertical, and your competitive landscape, backed by your data.
Four deliverables. Each one earns its place in the engagement.
Below the methodology sits the work product. Every Residency partner gets four deliverables — the minimum coherent research engagement. Below this set, the work doesn't compound; above it, complexity outpaces our ability to deliver well.
DELIVERABLE 01
Your domain's citation report
Weekly. Which of your pages were cited, on which engines, for which queries, with what persistence rate. Per-engine breakdown. Your raw data is yours.
DELIVERABLE 02
Prioritized recommendations queue
Monthly. The specific pages and articles most worth updating right now, ranked by expected citation impact. Each recommendation cites the underlying research finding and shows the data behind the call.
DELIVERABLE 03
Competitive citation surveillance
Weekly tracking + alerts. Five named competitors' citation share in your category. When their share moves — up or down — you get the specific pages driving it. Real-time enough to react before a shift compounds.
DELIVERABLE 04
Your custom playbook
Quarterly. A strategic synthesis of what works for your domain, vertical, and buyers — refined over a full quarter of citation data so each version's recommendations have earned their place. By month four, your first version. By month twelve, a proprietary research asset no competitor can replicate.
Read the full playbook spec below ↓Generic AEO advice expires. Your playbook compounds.
Every AEO guide on the internet gives you the same advice: add schema, use answer-first structure, keep content fresh. That's fine as a starting point. But it doesn't tell you which tactics actually move the needle in your vertical, for your prompts, against your competitors. The Playbook does.
Data-backed, not opinion-based
Every recommendation traces back to statistical findings from your longitudinal citation data. We don't guess what works—we measure it.
Specific to your vertical
What drives citation persistence in cybersecurity is different from martech. Your playbook reflects the patterns that matter in your market, not generic best practices.
Always current
Quarterly deep-dives feed new findings into the playbook. Tactics get validated, refined, or deprecated based on fresh data. You always have an up-to-date SOP.
Eight chapters. Every one backed by your data.
The Playbook is organized as a complete content operations manual. Each chapter addresses a different dimension of AEO performance—from high-level strategy down to line-by-line structural guidance.
Executive Summary & Citation Health
Your citation health score, key changes since the last update, top wins, emerging risks, and the three highest-priority actions for the coming quarter. Written for leadership—everything they need in two pages.
Prompt Landscape Map
The complete set of prompts your buyers ask AI engines, organized by buyer stage (awareness, education, comparison, decision). Which prompts you're cited on, which you're missing, and which ones carry the most commercial value.
Citation Persistence Analysis
Which of your pages hold citations over time and which decay. Persistence scores by URL, by engine, and by prompt category. The structural features that correlate with longevity in your vertical.
Competitor Citation Intelligence
Who gets cited on your prompts, what they do structurally, and where their content outperforms yours. Side-by-side feature comparisons on the prompts that matter most to your buyers.
Content Gap & Opportunity Matrix
A prioritized list of content to create or restructure, ranked by citation impact. Each gap includes the target prompt, the current winner, and the specific structural changes needed to compete.
Structural & Formatting Guidelines
The content blueprint: optimal word counts, heading structures, schema markup recommendations, answer placement, source attribution patterns, and entity clarity standards—all calibrated to what persists in your vertical.
Voice & Tone for Citation Persistence
How the way you write affects whether you get cited. Sentence structure patterns, confidence signaling, definitional clarity, and the specific voice characteristics that correlate with citation persistence in your market.
Quarterly Action Plan
A concrete, prioritized task list for the next 90 days. What to publish, what to restructure, what to monitor. Each action links back to the data that generated it, so your team knows why it's a priority.
Browse a sample playbook as if it were yours
This is what a Quoted Playbook looks like inside. Click through the chapters to see the kind of analysis, recommendations, and operational guidance you'd receive. All data shown is illustrative.
Executive Summary
Last updated: Apr 2, 2026Driven by improved schema coverage and answer-first restructuring on 12 key pages.
Moved from uncited to Position 1 across ChatGPT and Perplexity after FAQ schema + answer-first restructure.
CrowdStrike published 8 new pages targeting comparison prompts. Citation share on "vs." queries dropped 15%.
These pages are losing citations to competitors with better entity clarity and source attribution.
Prompt Landscape Map
78 prompts trackedCitation Persistence Analysis
12-week rolling windowCompetitor Citation Intelligence
5 competitors trackedAggressive publishing cadence. 8 new pages in Q1 targeting comparison prompts. Strong entity clarity and schema coverage.
Strong on educational prompts. Weak on comparison queries. No FAQ schema detected on key pages.
Losing ground on awareness prompts. Content is authoritative but poorly structured for AI extraction.
Content Gap & Opportunity Matrix
12 gaps identifiedStructural & Formatting Guidelines
Calibrated to cybersecurity verticalPages in this range showed 2.1x citation persistence vs. shorter or longer content in this vertical.
AI extracts favor self-contained passages in this range. Each section should answer one question completely.
Pages with prompt-matched H2s showed +54% higher citation rates across Perplexity and ChatGPT.
3.2x more likely to appear in AI Overviews with FAQPage schema. HowTo schema for procedural content.
44.2% of LLM citations come from the first 30% of the page. Never bury the answer.
Voice & Tone for Citation Persistence
Calibrated to cybersecurity verticalQ2 2026 Action Plan
14 actions · 90-day windowAll data shown is illustrative. Your playbook is built from your company's actual citation data.
Every quarter, your playbook gets sharper
The Playbook isn't written once and forgotten. Each quarterly deep-dive adds new longitudinal findings and integrates them directly into the playbook—so your content operations guide is always current.
New data analyzed
12 additional weeks of citation data across all prompts and engines. New patterns surface, existing findings get validated or refined.
Deep-dive report delivered
A standalone quarterly analysis with new findings, trend shifts, competitor movements, and emerging opportunities specific to your vertical.
Playbook updated
New findings are integrated into every relevant chapter. Recommendations get refined. Deprecated tactics are removed. Your SOP stays current.
The result: a compounding advantage
After four quarters, your playbook contains a full year of longitudinal citation intelligence—refined, validated, and specific to your market. Your competitors starting from scratch need a year to build what you already have.
Methodology is public. The longitudinal dataset and the prescriptive layer are not.
We publish enough of our research that a skeptical analyst could replicate any single finding from raw data. We do not publish the things that take months of compounding data and analyst effort to build—the longitudinal corpus, the feature-correlation models, the prescriptive recommendation engine that turns raw citations into a content roadmap. That split mirrors how Moz, Forrester, and modern AI research labs (Anthropic, OpenAI) structure their disclosures: concepts public, infrastructure proprietary.
For our customers this matters in two directions. Open methodology means any claim we put in your monthly report or your client deck has a published research artifact you can hand to a skeptic. Proprietary infrastructure means the longitudinal dataset and the analytical layer that produces your recommendations are yours under engagement—and can’t be replicated by a competitor without starting from scratch.
- Half-Life Report issues — one reproducible experiment per week, ~2,000 words, with batch IDs that let any reader independently replicate the finding
- Half-Life Quarterly — the long-form research synthesis, free to read at research.gotquoted.com
- The methodology behind every finding — how prompts are constructed (ICP variants, persona rewrites), which engines were queried, sample sizes, statistical treatments (Jaccard, FDR correction)
- A verification-grade slice of data alongside each published issue — sanitized batch JSON, enough to reproduce the headline number from scratch
- The experiment-runner code via the forthcoming GitHub methodology repo — the harness, not the dataset
- The annual State of AI Search Citations report — methodology-heavy benchmark across 10 verticals, with raw data published alongside
- Honest descriptions of what we don’t know — bear-case sections in every issue, FDR-corrected p-values, sample-size caveats
- The longitudinal citation dataset — the full week-over-week corpus of cited URLs across every prompt we track. Per-issue slices are published; the rolling history isn’t.
- The feature-correlation models — which structural content properties predict citation persistence in which vertical, with what effect size. Concepts are described in Half-Life; the trained models aren’t released.
- The prescriptive recommendation engine — the layer that turns raw citation data into the page-level "change this paragraph, add this section" output your team acts on each month
- The canonical per-vertical prompt indices — the 100-prompt sets developed from buyer-question corpora, validated against AI search volume, refined quarterly
- Client-specific findings — your domain’s performance read, your competitor citation graph, the prompts run on your behalf
- Pipeline infrastructure — rate-limiting, retry orchestration, engine-specific scrape behavior, cost models
The simplest version of the rule: if a finding appears in a Half-Life issue, the underlying methodology and a verification slice of data are publishable. If it’s a recurring stream (your weekly tracking, the rolling vertical corpus, the prescriptive model), it’s the product—not a publication.
See what we'd track for your company
Book a 20-minute call. We'll walk through what a research program looks like for your vertical, which prompts we'd track, what deliverables you'd receive, and what you'd learn in the first 90 days.