Selection, not mentions.
When an AI is asked to recommend a business in a category and city, does it choose this one. Being named is visibility. Being chosen is selection.
Top 3 selection, not mention.
A business counts as recommended only when a model would confidently place it in its top 3 for a real buyer-intent query, in its own city and service category. Not a mention. Not one option among twenty. Top 3 selection.
Each model is tested with 3 query phrasings. It counts as recommending the business only if it selects it in at least 2 of the 3. The models-recommending figure is how many of the 4 cleared that bar.
We measure selection, not mention. Appearing indirectly, or in a long list, does not count. The model has to choose the business.
12 independent reads per audit.
Each audit runs 3 separate cold queries per model across 4 models (ChatGPT, Claude, Gemini, Perplexity), producing 12 independent reads. Queries are never bundled, so models cannot self-anchor and manufacture false agreement.
Majority across queries, not a single read.
Per model, a 2-of-3 majority across the three queries decides whether that model recommends. The score reflects selection across the full range of queries, not a single lucky one.
One phrasing is one camera angle.
Models are sensitive to how a question is asked. Three separate queries show how a business is interpreted across how buyers actually search.
Not all models respond the same way twice.
A June 2026 variance study across five markets and four models found Gemini and Perplexity stable enough for a single read -- their top picks held consistent across repeated queries on the same business. ChatGPT and Claude were less stable, especially in crowded categories where many businesses have similar web presence.
The response: 3 separate cold queries per model, with a 2-of-3 majority required for a recommendation to count. One lucky answer does not move your score.
Four factors, published weights.
ARO Score™ is the diagnostic composite inside the ARO Report. It is 0 to 100 and combines four factors:
- Recommendation rate (40%)
- Position strength (20%)
- Confidence (20%)
- Signal quality (20%)
Recommendation rate carries the most weight because being chosen is the point. The other three factors reflect how prominently, how consistently, and how reliably that selection holds. ARO Score™ explains what may be driving a business's ARO Index Score. It is not the public benchmark itself.
The public benchmark, rebuilt on observed outcomes.
The ARO Index Score is the public benchmark used to rank and compare businesses within a market and category. As of July 2026, it is calculated entirely from observational inputs, not from the ARO Score™ diagnostic composite. The audit method (v2) that produces each individual read is unchanged; only the ranking formula changed.
The ARO Index Score is 0 to 100 and combines four factors:
- Recommendation rate (45%)
- Cross-model consistency (20%)
- Recommendation position (25%)
- Recency (10%)
Recommendation rate carries the most weight because being recommended is the point. Cross-model consistency and recommendation position reflect how reliably and how prominently that recommendation holds across ChatGPT, Claude, Gemini, and Perplexity. Recency weights recent audits more heavily than older ones.
Where you appear in the list matters.
A recommended business named first earns more weight than one named fifth. Higher ranks earn descending credit down the list. A business that is recommended but given no clear rank receives a neutral middle weight. This rule is fixed and applied identically to every business.
Four grouped factors, not a checklist.
Signal quality captures the site-side factors that affect how well AI platforms can read and trust a business. Four groups:
- Content clarity
- Technical crawlability
- Structured data
- Authority signals
No individual dimension score is published. The aggregate drives the component weight.
Monthly by market.
Each market is re-audited monthly. Rankings update when new audits complete.
No business can pay to change its score.
Weights are fixed and applied identically to every business, client or not. Buying a subscription changes what advice you receive; it does not touch the audit engine or change how your score is calculated. Measurement and services are kept separate by design.
A score reads a moment.
Scores are dated and re-run. Models update, competition shifts, questions evolve. A score reads a moment; it is not a permanent grade.
The original single-query method (v1, through early 2026) is preserved below for reference.
V1 archive · original single-query method · through early 2026
About the ARO Index
The ARO Index is a live market dataset tracking which businesses AI platforms actually select when buyers ask for local service recommendations by city and category. It measures selection behavior, not mentions. A business can be mentioned without being selected. ARO Index records selection.
What We Measured
ARO Index tracked AI recommendation selection across four platforms: ChatGPT, Claude, Gemini, and Perplexity. For each market and category, a standardized query was submitted to all four platforms. The responses were recorded, analyzed, and used to produce ARO Scores and market rankings.
Rankings reflected which businesses were selected most consistently and prominently across platforms within a defined audit period.
How Audits Worked
Audits were conducted on a rolling cycle. Each audit covered a defined city and service category. A single query was submitted in a standardized format across all four AI platforms. Results were recorded at the time of submission.
ARO Index does not rely on scraped data, third-party aggregators, or predictive modeling. All data is collected through direct platform queries conducted by the ARO Index research team.
Audit Scope
Approved markets:
- Charleston, SC
- Nashville, TN
- Atlanta, GA
- Denver, CO
- Miami, FL
- Sacramento, CA
- Fresno, CA
Additional markets are added on a rolling basis. Each market is organized by service category.
ARO Score
The ARO Score is a composite measurement scored 0 to 100. It reflected how consistently and prominently a business was selected by AI platforms when buyers asked for recommendations in its city and category. A higher score indicates more consistent selection across platforms and queries.
Scoring methodology is proprietary. The ARO Score is a trademark of TaG Makes.
Data Currency and Versioning
ARO Index data reflects point-in-time audit results. AI platforms update their models continuously, which means recommendation behavior can shift between audit periods. Each report references its specific audit period and dataset.
Current rankings are always available at aroindex.com.
Research Lead
ARO Index research is conducted by Therese Grittner, founder of TaG Makes, based in Charleston, SC. Therese Grittner created the ARO Index methodology and oversees all audits, scoring, and market reporting.
Limitations
ARO Index measures AI platform behavior at the time of each audit. Results reflect the platforms' recommendation behavior during that specific period and may not represent behavior at other times.
Sample sizes vary by market and category. Posts with samples under 100 businesses are labeled as exploratory or directional.
ARO Index does not make causal claims. All findings are observational and correlative unless otherwise stated.
Contact
For research inquiries: therese@aroindex.com
For current rankings and market data: aroindex.com