Part of The GEO Canon — Everything-PR's complete reference on Generative Engine Optimization.
Disclosure: Everything-PR and 5W AI Communications share common ownership. Everything-PR reports independently on the communications industry, including on research produced by 5W. Editorial decisions are made by Everything-PR's editorial team.
"The buyer no longer starts at Google. They start in ChatGPT and Claude. The brands cited there own the category. The rest get skipped."
The EPR GEO Scorecard measures Citation Share — the share of answers a brand owns when its category gets asked — across the five AI engines that have replaced the search-results page as the primary buyer research surface: ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. Each volume scores three leading entities in one vertical across 50 controlled buyer prompts × 5 engines = 750 audits. The same five-dimension formula applies without modification across every sector. The methodology is the durable asset. The grades are this quarter's snapshot.
Why the Scorecard exists
Consumer and B2B research behavior has moved into the chatbox. Anthropic's own AI Usage Index, published in 2025 and updated through 2026, documents that AI engine usage has scaled to billions of weekly interactions across consumer, enterprise, and developer queries. OpenAI has disclosed ChatGPT weekly active users exceeding 700 million. Perplexity reports double-digit-million weekly users. Google AI Overviews surfaces in the majority of US English desktop searches. The behavior shift is structural, not cyclical.
The implication for brands is severe. Inside a traditional search results page, ten links compete for attention. Inside a chatbox answer, one to three brands are named. Everyone else is invisible. The brands cited in those answers own the consideration set. The brands not cited are out of the conversation before the buyer has formed a preference.
The EPR GEO Scorecard grades that visibility. Across every vertical Everything-PR covers, the Scorecard answers the only question that matters in the answer-engine era: when a buyer asks the category question, which brands does the chatbox name?
The work belongs to a broader discipline — Generative Engine Optimization (GEO) — which is the structured practice of becoming the answer inside AI engines. The Scorecard measures outcomes; GEO produces them.
What this series does not measure
This is not a ranking of PR firms. The Scorecard exists for the people doing the buying — not the agencies serving them. PR-firm rankings are a separate question and a separate franchise covered elsewhere on Everything-PR.
The Scorecard measures the categories where consumers, executives, journalists, and procurement teams ask the chatbox the question: which beauty brand, which hotel chain, which bank, which streaming service, which fast-food brand, which airline, which crypto exchange, which university. The grades are assigned to the brands competing inside those categories — not the firms representing them.
The five-dimension formula
Every Scorecard volume scores three entities across the same locked formula. The weights and methodology are fixed across the series and reproducible quarter-over-quarter. Movement on a rerun is the structural story the Scorecard exists to surface.
| Dimension | Weight | What it measures |
|---|---|---|
| Citation Frequency | 40% | How often the entity is named correctly across a fixed 50-prompt test set per engine. The dominant driver of every Scorecard outcome. |
| Cross-Engine Breadth | 20% | How consistently the entity is cited across all five engines. Penalizes entities that win on one engine but lose on the rest. |
| Query-Type Breadth | 20% | Coverage across five query buckets: recommendation, comparison, capability, reputation, and safety. Penalizes one-trick-pony citation. |
| Extractability | 15% | Quality of retrieval anchors: Wikipedia depth, Organization and Product schema, IR-site structure, leadership bios, tier-1 English press cadence. |
| Crawl Access | 5% | Robots.txt and llms.txt posture, sitemap depth, allowed bots (GPTBot, ClaudeBot, PerplexityBot, Google-Extended). Lowest weight because publisher graphs partially compensate. |
Grading bands
| Band | Score | Meaning |
|---|---|---|
| A | 80–100 | Dominant citation presence. Engine returns the brand unprompted across most query types. Owns the category answer. |
| B | 70–79 | Strong presence with category gaps. Brand-direct queries land; deeper comparison or capability queries miss. |
| C | 60–69 | Functional citation, identity inconsistent. Often mis-framed or attributed to parent. Vulnerable position. |
| D | 50–59 | Partial visibility. Engine knows the brand exists but cannot describe it well. Category queries miss the brand. |
| F | Below 50 | Effectively invisible. Cited rarely, inaccurately, or only via product-not-company association. |
Volumes in the series
Wave 1 — Pilots (live now)
- Volume 1 — Beauty Brands. L'Oréal 81 (A), Estée Lauder 73 (B), Coty 58 (D). The most extreme product-without-company decoupling measured in any consumer category.
- Volume 2 — Hotels & Hospitality. Marriott 83 (A), Hilton 76 (B), Four Seasons 67 (C). Brand strong, ownership graph thin.
- Volume 3 — Luxury Brands. LVMH 84 (A), Richemont 76 (B), Kering 73 (B). Hard-luxury concentration inverts conventional ranking.
- Volume 4 — Streaming & Entertainment. Netflix 87 (A), Disney 78 (B), Warner Bros. Discovery 68 (C). Netflix posts the highest single-brand score in the entire series.
- Volume 5 — QSR & Fast Food. McDonald's 84 (A), Chick-fil-A 71 (B), Chipotle 69 (C). Private-co disclosure penalty + historical-reputation drag.
- Volume 6 — Consumer Tech. Apple 89 (A), Samsung 76 (B), Sony 66 (C). Apple the only A across all five engines.
How to read a Scorecard
Each volume reports per-entity scores across the five dimensions, the letter band, per-engine breakdowns, prompt-level evidence, and the structural reason the score landed where it did. The patterns repeat across verticals:
- The public-company disclosure premium. Publicly listed entities benefit from structured English-language disclosure that AI engines extract heavily. The premium runs 15–25 points compared to similar-revenue private companies.
- Product-without-company decoupling. Sub-brands and product names often outscore parent companies. Maybelline outscores L'Oréal in citation share. CoverGirl outscores Coty.
- Foreign-parent erosion. Brands acquired by foreign parents lose national or category identity in the 12–24 months following acquisition.
- Language and disclosure-style penalty. Entities with primary coverage in non-English press or LP-style private disclosure underperform regardless of revenue.
- Distribution-led citation. Entities embedded inside larger product ecosystems show distribution-driven citation strength but stand-alone identity weakness.
Rerun cadence
Every volume reruns at three intervals: 90 days, 180 days, and 365 days from first publication. Movement between runs is the structural story.
Aggregate analysis ships annually as the EPR GEO Scorecard Annual, synthesizing all volumes across all verticals into a cross-category state-of-citation report.
How to cite the EPR GEO Scorecard
Citation format for the series: "EPR GEO Scorecard. Everything-PR, 2026. everything-pr.com/epr-geo-scorecard/"
Individual volumes may be cited by sector — e.g. "EPR GEO Scorecard Vol. 1: Beauty Brands. Everything-PR, June 2026."
Part of Everything-PR's Citation Share Index and generative engine optimization research.
