Methodology
AI Coach Visibility Index — v1 · Signed off April 21, 2026
1. Purpose & Positioning
The AI Coach Visibility Index is a public, weekly-refreshed ranking of US-based professional coaches by how prominently they appear in the answers of leading generative AI models (ChatGPT, Perplexity, Gemini, Claude) when prospective clients ask for coach recommendations.
- 1.Establish quso.ai as the category reference point for AI search visibility in coaching.
- 2.Generate a compounding stream of inbound leads via the 'check yourself' self-service tool, which doubles as a product demo.
- 3.Produce a defensible, data-backed dataset that unlocks press, reports, and partnership conversations.
2. v1 Launch Scope
| Parameter | v1 Value |
|---|---|
| Categories | 6 coaching categories |
| Geographies | 5 — top 4 states by coach density + Remote/Virtual |
| Leaderboards | 30 (6 × 5) |
| Queries per leaderboard | 8 diverse query phrasings |
| AI models queried | ChatGPT, Gemini (Search grounding), Perplexity, Claude |
| Weekly query volume | 30 × 8 × 4 = 960 API calls |
| Deployment | coachindex.quso.ai (Cloudflare Workers) |
| Contact | help@quso.ai |
3. Definition of Visibility
Visibility is scored per query response on a 0–4 ordinal scale. A coach's score reflects how discoverable they are to a buyer reading the AI response — not merely whether their name appears somewhere in the text.
| Score | Appearance | Criteria |
|---|---|---|
| 0 | Not mentioned | Coach's name does not appear in the response or citations. |
| 1 | Bare mention | Name appears but buried — listed 6th+ in a long list, or only in a tangential citation. |
| 2 | Top 3–5 | Named as one of 3–5 recommended options, with at least one supporting detail. |
| 3 | Top 1–2 | Featured as a primary recommendation (one of the first two named), with substantive context. |
| 4 | Top 1–2 + cited | Primary recommendation AND a direct link to their website/profile, or an unusually strong endorsement. |
Citation handling for Perplexity: Because Perplexity surfaces citations prominently, appearance in citations counts toward visibility even when the coach is not named in the prose.
4. Categories
| # | Category | Buyer intent |
|---|---|---|
| 1 | Executive Coach | C-suite, VP, senior leaders seeking 1-on-1 growth, transitions, or performance work. |
| 2 | Career Coach | Individuals navigating job changes, promotions, layoffs, industry pivots. |
| 3 | Business Coach | Small business owners and operators scaling revenue, teams, ops. |
| 4 | Leadership Coach | New managers, emerging leaders, team leads developing leadership skills. |
| 5 | Life Coach | General personal development, purpose, motivation, transitions. |
| 6 | Health & Wellness Coach | Nutrition, fitness, habit, holistic wellbeing. |
5. Geographic Segmentation
State-level segmentation rather than metro-level to broaden addressable coverage and better serve hybrid/remote coaches.
| Geography | v1 Launch? |
|---|---|
| California | Yes |
| Texas | Yes |
| New York | Yes |
| Florida | Yes |
| Remote / Virtual | Yes |
| Virginia, Maryland, North Carolina | Month 2 |
| Colorado, Washington, New Jersey, Georgia | Month 3 |
6. Query Templates
Each leaderboard runs 8 query variations per AI model. [CATEGORY] = category name, [GEO] = state name or "online".
Generic high-intent queries (4)
- 1."Who is the best [CATEGORY] in [GEO]?"
- 2."Top [CATEGORY]s in [GEO] 2026"
- 3."Recommend a [CATEGORY] in [GEO]"
- 4."I need a [CATEGORY] in [GEO], who should I hire?"
Use-case specific (2)
- •Executive: "Executive coach for new CEOs in [GEO]"
- •Career: "Career coach for tech layoffs in [GEO]"
- •Business: "Business coach for service businesses in [GEO]"
- •Leadership: "Leadership coach for new managers in [GEO]"
- •Life: "Life coach for mid-career transitions in [GEO]"
- •Health & Wellness: "Wellness coach for sustainable habit change in [GEO]"
Qualified intent (2)
- 7."Certified [CATEGORY] in [GEO] with strong track record"
- 8."Best [CATEGORY] in [GEO] with good reviews"
7. AI Models & Weighting
| Model | Weight | Rationale |
|---|---|---|
| ChatGPT (GPT-4o) | 40% | Dominant consumer usage. Most general buyers start recommendation queries here. |
| Google Gemini | 40% | Enormous footprint via Google Search AI Overviews and Google AI Mode. Queried via Gemini API with Google Search grounding enabled. |
| Perplexity | 10% | Smaller audience but over-indexes for active search / shopping intent. |
| Claude | 10% | Growing professional adoption but smaller consumer share for recommendation queries today. |
8. Scoring Formula (0–100)
Each ranked coach receives a per-category, per-geography score computed in four steps:
- 1.Per-query score: 0–4 per the visibility scale in Section 3.
- 2.Per-model score: average the 8 per-query scores, then scale to 0–100 by multiplying by 25.
- 3.Weighted total: sum the four per-model scores using model weights (0.40 × ChatGPT + 0.40 × Gemini + 0.10 × Perplexity + 0.10 × Claude).
- 4.Round to the nearest integer. The result is the coach's published Visibility Score.
Worked example — Sarah Chen, Executive Coach, New York
| Model | Avg (0–4) | ×25 | ×Weight | Contribution |
|---|---|---|---|---|
| ChatGPT | 2.5 | 62.5 | ×0.40 | 25.0 |
| Gemini | 1.5 | 37.5 | ×0.40 | 15.0 |
| Perplexity | 3.0 | 75.0 | ×0.10 | 7.5 |
| Claude | 2.0 | 50.0 | ×0.10 | 5.0 |
| Total | 53 / 100 |
9. Tier Cutoffs
Each ranked coach is assigned a tier based on their percentile position within their specific category-geography leaderboard. Percentile-based tiering normalizes across categories with different visibility ceilings.
| Tier | Percentile | Typical band |
|---|---|---|
| Platinum | Top 1% | 1–5 coaches per board |
| Gold | Top 2–10% | 5–15 coaches per board |
| Silver | Top 11–30% | 15–40 coaches per board |
| Bronze | Top 31–80% | 40–80 coaches per board |
| Unranked | Bottom 20% / score < 10 | Remainder |
10. Opt-Out & Privacy
Pre-seeded coaches
Coaches surfaced by AI model responses during the initial seed run are publicly ranked by default — defensible on the same grounds as Forbes 30 Under 30 or G2 rankings: subjects are public figures by virtue of being named by a third-party system. A one-click opt-out is available on every profile; requests are honored within 7 business days.
Self-check tool users
Opting in by definition. Coaches tick a consent checkbox (default: checked) at sign-up. Unchecking produces an anonymous ranked record — score counts toward category statistics but name is not displayed publicly.
Data access & deletion
Any coach may request a full data download or deletion by emailing help@quso.ai. No resale of personal data; no unaffiliated third-party sharing. Deletion requests honored within 30 days.
11. Refresh Cadence
The full 30-leaderboard query batch runs weekly, every Monday at 06:00 UTC. Weekly refreshes keep data competitive: AI model responses shift frequently enough (due to model updates, citation indexing, and web content changes) that monthly snapshots go stale.
Self-check results reference the most recent weekly batch — instant to serve, as it's a database lookup against archived responses rather than fresh API calls.