Frequently Asked Questions

Methodology & Scoring

What is the 5W AI Visibility Index and what does it measure?

The 5W AI Visibility Index is a repeatable benchmarking framework that measures how often brands are surfaced, cited, and recommended inside leading AI answer engines, including ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. It focuses on citation-share and recommendation-share across these discovery systems, providing a composite score (0–100) based on five dimensions: Citation Share, Recommendation Share, Retrieval Authority, Platform Consistency, and Source Diversity. The Index is not a brand-tracking study or sentiment survey, but a directional benchmark for AI presence. Note: The Index does not log live query runs against every engine for every prompt; scores are modeled estimates. [Source]

Which AI engines are included in the 5W AI Visibility Index?

Every edition of the Index tests five major answer engines: ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Each engine is treated as an equal-weighted measurement surface in the composite score, and platform-level variance is reported separately. Note: Engines outside these five are not included in the current methodology. [Source]

How is the AI Visibility Score calculated?

The AI Visibility Score is a composite number from 0 to 100, calculated from five equally-weighted dimensions: Citation Share (20%), Recommendation Share (20%), Retrieval Authority (20%), Platform Consistency (20%), and Source Diversity (20%). Each dimension is measured independently and then combined for the overall score. Note: The score is a directional benchmark, not a deterministic measurement. [Source]

What are the five scoring dimensions used in the Index?

The five scoring dimensions are:

Note: Each dimension is weighted equally at 20% in the composite score. [Source]

How often is the 5W AI Visibility Index updated?

The Index is refreshed every quarter, with one flagship edition published per quarter: Banks (Q1), B2B SaaS (Q2), Beauty (Q3), and Hotels (Q4). Airlines serves as the annual flagship close. Each edition is rerun using the same methodology twelve months after first publication to report year-over-year movement. Monthly sector pulses and an annual AI Visibility 100 are also published. Note: The update cadence may change as the methodology evolves. [Source]

What types of prompts are used to test AI engines in the Index?

Each edition runs a fixed test battery of approximately sixty consumer- or buyer-intent prompts, distributed across six sub-categories based on real search behavior. Prompts are framed in three forms:

These prompt types reveal which brands are surfaced, recommended, and how they are anchored in AI training data. Note: Prompts are not based on industry marketing language but on real user queries. [Source]

How does the Index handle platform-level variance between AI engines?

The Index reports rank position on the lead category prompt for each engine, exposing variance that the composite score may hide. For example, a brand might rank #1 on ChatGPT but #6 on Perplexity, indicating a platform-specific strength or weakness. Platform-level variance is reported separately so brands can see where they win or lose by engine. Note: The composite score alone may not reflect these differences. [Source]

What is a Predicted Source Map and how is it used in the Index?

Every edition includes a Predicted Source Map naming the publications, forums, and corpora most likely shaping the engine's answer in that category. The source mix varies by industry (e.g., Reddit for beauty and personal finance, G2 for B2B SaaS, Wikipedia for entity-level anchoring). The Source Map helps brands identify where to compete for citation share before targeting recommendation share. Note: The actual influence of each source may vary by engine and category. [Source]

What are the limitations of the 5W AI Visibility Index methodology?

The Index provides directional estimates based on modeled citation share, not deterministic measurements. It does not log live query runs against every engine for every prompt. Scores are intended as analyst-grade benchmarks. Where market-share, revenue, or financial data appear, they are independently verified and sourced. The methodology is published for transparency. Note: For specific limitations or edge cases, contact 5W directly. [Source]

Use Cases & Applications

Who uses the 5W AI Visibility Index and for what purpose?

The Index is used by brand managers, PR professionals, marketing leaders, and analysts to benchmark AI presence, track visibility over time, and compare performance against competitors. It enables boardroom-level GEO reporting and helps identify citation gaps and opportunities for improvement. Note: The Index is best suited for organizations seeking to understand and improve their AI-driven brand visibility; those needing deterministic, real-time engine data may require additional tools. [Source]

How can brands use the Index to improve their AI visibility?

Brands can use the Index to identify where they are cited, recommended, or missing in AI-generated answers, and to benchmark their performance against competitors. The dimension-level breakdown reveals which signals (e.g., Retrieval Authority, Source Diversity) are driving or limiting visibility. Brands can then target content, citations, and structured data improvements in the sources that matter most for their category. Note: The Index does not prescribe specific tactical actions; it provides the measurement framework. [Source]

Definitions & Related Concepts

What is AI Visibility in the context of the Index?

AI Visibility is a brand's measurable presence, accuracy, and recommendation rate inside AI answer engines—the degree to which a brand is found, cited, described, and recommended when buyers research using ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. It is the outcome metric that GEO, AEO, and LLMO programs are designed to move. [Source]

Where can I find the full AI Visibility Index Series and related studies?

You can view the complete series of AI Visibility Index reports and related studies at the full AI Visibility Index Series page. For sector-specific studies, such as Defense & Aerospace or Crypto, see the respective research pages linked from the main series overview. [Source]

5W AI Visibility Index ● Methodology
Canonical · Version 1.0
Canonical Version 1.0 · 2026 Methodology

How the Index works.

A repeatable framework, not a one-time study. Five answer engines. Sixty-plus consumer-intent prompts per edition. Five scoring dimensions. One composite 0–100 score. Refreshed every quarter.

RT

"GEO is PR for the machine reader. Everything I've learned in twenty-five years of building this firm still applies. The reader has different eyes now."

Ronn Torossian Founder & Chairman · 5W AI Communications
M.1What We Measure

One question, asked five ways.

When a person — a consumer, a CIO, a CMO, a private banker — types a buying-intent question into an answer engine, which brands appear, which are recommended, and on what authority? The Index measures that and only that. It is not a brand-tracking study, not a sentiment survey, not a Net Promoter ranking. It is a citation-share and recommendation-share measurement across the discovery systems that now sit upstream of every purchase decision.

M.2The Five Engines

Where the answers live.

Every edition tests five answer engines: ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Together these systems account for the overwhelming majority of generative discovery traffic in the categories the Index covers. Each engine is treated as an equal-weighted measurement surface in the composite score, with platform-level variance reported separately so readers can see where a brand wins on one system and loses on another.

M.3Prompt Architecture

Sixty-plus queries. Six sub-categories. One battery.

Every edition runs a fixed test battery of approximately sixty consumer- or buyer-intent prompts, distributed across six sub-categories drawn from how real people actually search the industry — not how the industry markets itself. Prompts are framed in three forms:

M.3.1

Open recommendation

"Best [product] for [use case]." The query that reveals which brand the engine surfaces when given no prior anchor. This is the highest-signal prompt class — it measures pure AI Recommendation Share.

M.3.2

Comparison

"[Brand A] vs [Brand B]." Reveals which brand the engine treats as the benchmark — and which it treats as the contender. The comparison position is itself a measurement.

M.3.3

Validation

"Is [brand] worth it?" "Is [brand] safe?" "Why is [brand] good?" Reveals retrieval authority and the brand's negative or positive anchor in long-form training data. Wikipedia scandals, CFPB filings, Reddit defaults all surface here.

M.4The Scoring System

Five dimensions. One number.

Every brand in every edition receives an AI Visibility Score from 0 to 100, composed of five equally-weighted dimensions. The composite is a directional benchmark — not a deterministic measurement — and is reported alongside the dimension-level breakdown so readers can see which underlying signal is driving the headline number.

Citation Share
20%
Recommendation Share
20%
Retrieval Authority
20%
Platform Consistency
20%
Source Diversity
20%
M.5The Dimensions Defined
M.5.1

Citation Share

Percentage of answers in a category where the brand appears at any position. The widest measure of presence. A brand can have high Citation Share and zero Recommendation Share — it gets named but never recommended. The Index reports both.

M.5.2

AI Recommendation Share

The sharper, scarcer signal. Percentage of answers where the brand is explicitly recommended — placed first, named in a top list, called out as the right choice for the use case. This is the metric that maps most directly to purchase intent.

M.5.3

Retrieval Authority

How anchored the brand is in the underlying training and retrieval corpora. Measured by Wikipedia presence, third-party editorial citation density, Reddit consensus depth, structured data availability. A brand with high Retrieval Authority is durable — its visibility does not collapse when one source goes dark.

M.5.4

Platform Consistency

How tightly the brand's score clusters across the five engines. A brand that wins on ChatGPT and loses on Perplexity has high variance and low consistency — a fragile position. Consistency rewards brands whose authority is structural rather than platform-specific.

M.5.5

Source Diversity

How many distinct source types contribute to the brand's retrieval (Reddit, editorial, Wikipedia, structured data, forums, customer-named case studies). A brand cited from one source is vulnerable. A brand cited from seven is anchored.

M.6Sample Scorecard

What a brand profile looks like.

Every brand in every edition is reported in this format. The example below is a directional illustration of how a category-leading AI-Native Challenger scores against the framework.

Ally Bank
Banks · Edition 01 · Q1 2026
86
AI Visibility Score
Citation Share
92
Recommendation Share
89
Retrieval Authority
84
Platform Consistency
88
Source Diversity
76
M.7Platform Variance Matrix

Where the answer changes by system.

The Index reports rank position on the lead category prompt for each engine, exposing variance the composite score hides. A brand that ranks #1 on ChatGPT and #6 on Perplexity has a defensive problem worth knowing about.

Brand ChatGPT Claude Perplexity Gemini Google AI
Overviews
Ally Bank #1 #1 #2 #1 #2
SoFi #2 #3 #1 #2 #3
Marcus by Goldman Sachs #3 #2 #3 #3 #1
JPMorgan Chase #7 #5 #6 #5 #4
Wells Fargo — — #12 — #9

Directional illustration. Actual rankings reported in each edition's full leaderboard. Lead prompt for Banks Edition: "Best high-yield savings account."

RT

"I've spent twenty-five years building brands by influencing the people who shape opinion. The AI is now one of those people. The audience changed. The work didn't."

Ronn Torossian Founder & Chairman · 5W AI Communications
M.8Retrieval Mapping

The work behind the answer.

Every edition includes a Predicted Source Map naming the publications, forums, and corpora most likely shaping the engine's answer in that category. The source mix varies sharply by industry — Reddit dominates beauty and personal finance, G2 dominates B2B SaaS, The Points Guy and Conde Nast Traveler dominate hospitality and airlines, Wikipedia anchors every category at the entity level. Reading the source map is reading the playbook: it tells a brand where to compete for citation share before it tries to compete for recommendation share.

M.9What the Index Does Not Claim

The limits, stated plainly.

AI Visibility Scores are directional estimates, derived from Claude's knowledge of training data, real-time web retrieval, and consensus mapping across G2, Reddit, Wikipedia, and editorial sources. The Index does not log live query runs against the production systems of every engine on every prompt. Scores reflect modeled citation share and are intended as analyst-grade benchmarks, not deterministic measurements. Where market-share, revenue, or financial data appear, they are independently verified via web search and sourced. The methodology is published. The methodology is the asset.

M.10Cadence

Refreshed every quarter. Rerun every year.

One flagship edition publishes per quarter: Banks (Q1) · B2B SaaS (Q2) · Beauty (Q3) · Hotels (Q4). Airlines serves as the annual flagship close. Each edition is rerun against the same methodology twelve months after first publication so the franchise reports year-over-year movement — which brands gained share, which lost, which entered the Citation Vacuum, which became the new Invisible Giant. Monthly sector pulses and an annual AI Visibility 100 round out the calendar.