Methodology · Original data · August 2026

Five-Market Med Spa Reputation and AI-Visibility Analysis: Full Methodology

Published August 4, 2026 · Data collected August 4, 2026 · Companion to How to Get More Med Spa Clients

This page documents, permanently and in full, how CustomerHalo's five-market med spa study was conducted: the queries run, how competitors were selected, what counted as an appearance, when data was collected, which fields were collected, and the anonymized market-level results. It is the second study in this series; the first covered dental practices in five different metros. Questions and corrections: [email protected].

Study design

Five independent med spas — one in each of the Nashville, San Diego, Charlotte, Minneapolis, and Kansas City metropolitan areas — were analyzed with CustomerHalo's standard production pipeline on August 4, 2026, with Botox selected as the focus service. The subjects were a convenience sample chosen by the study team from public listings and aggregator directories: independent (non-chain, non-franchise) med spas with active Google Business Profiles, their own websites, and menus including neurotoxin and dermal-filler treatments. National franchises and multi-metro brands were screened out during selection. Selection was not randomized, subject practices were not involved or notified, and no non-public information about any practice was collected or used. The five metros deliberately differ from the dental study's five.

Anonymity policy. Subject med spas are reported as Markets A–E. The letters are deliberately not in the metro order listed above, so per-market metrics cannot be trivially mapped to a metro. Subject appearances in Botox-related answers are reported in aggregate (3 of 26) and are not attributed to lettered markets. One exception is knowingly made: the guide reports that one subject was the most-reviewed business in its comparison set (1,363 reviews) and appeared in none of its market's Botox answers — that finding is the study's most instructive and could not be stated without those figures. Limit of this policy: all metrics are drawn from public listings, so a sufficiently determined reader could in principle re-identify practices by exhaustively searching public data; we judged this acceptable because nothing non-public is disclosed and no practice is evaluated negatively by name.

Timestamps

• Scans initiated: August 4, 2026, approximately 19:05–19:12 UTC, through the production analyze flow.
• Analyses completed: August 4, 2026, within roughly ten minutes of initiation per market.
• All Google Business Profile data, reviews, and AI answers reflect that collection window. Review counts and answers change over time.

Data fields collected

• Business profile (subject): name, primary Google Business Profile category and category IDs, star rating, total review count, address, coordinates, website URL, and place reference — from public Google Business Profile data via a commercial local-data provider (DataForSEO).
• Subject reviews: the newest reviews, newest-first, up to a requested depth of 300 per practice — date, star rating, and text. Reviewer identities were not used in the analysis.
• Competitor listings and reviews: name, category IDs, review count, and place reference for each selected competitor, plus that competitor's newest 200 reviews.
• Subject website: pages fetched from the practice's own site to identify advertised services.
• AI-answer records: for each query — the query text, intent type, engine identifier, outcome classification, whether the subject was mentioned and/or cited, the domains cited in the answer with a source-type classification, and the businesses named in the answer.

Competitor-selection criteria

Comparison groups were selected automatically by the production pipeline, with no manual curation:

1. Candidate pool: Google Business listings sharing the subject's category IDs within 25 km of the subject's coordinates, with more than 20 reviews, ordered by review count descending, up to 50 candidates.
2. Filters: the subject's own listing and any listing whose website matches the subject's domain are excluded; candidates need at least 40 reviews; recognizable national chains are excluded; duplicate business names are collapsed to the highest-reviewed listing; names appearing three or more times in the pool (multi-location brands) are excluded.
3. Ranking: candidates whose primary category matches the subject's primary category are preferred; up to four competitors are retained per market.
4. Review depth: the newest 200 reviews were retrieved for each selected competitor.

All five markets produced full four-competitor groups (20 competitors, holding 499 to 1,255 reviews). Disclosed limitation: the pipeline's chain filter is name-based, and one national franchise's per-location listings (distinct location names under a shared brand) passed it in two markets. Those listings are real local alternatives a client would see, so they were retained; the filter's limits are noted here for reproducibility.

The queries

Nine queries were run live per market (45 total), constructed by the production pipeline: one entity query (the med spa's name plus its city — a control for whether the engine can find the practice at all), one category query ("best med spa in {city}"), three service buyer queries built from services the practice advertises on its own website, and four focus queries — buyer-phrasing variants and problem phrasings for the focus service (Botox), generated in client language. The 26 Botox-related buyer searches reported in the guide are, per market, every Botox-named service query plus the four focus queries (marked * below) — four to six per market depending on the practice's advertised services. Entity queries are redacted to preserve subject anonymity; all other queries are shown verbatim.

* One of the 26 Botox-related buyer searches.

Engine, geographic context, and appearance definitions

• Engine: Google's Gemini with Google Search grounding (pipeline engine identifier gemini_grounded_google_search). One run per query. AI answers are not deterministic; a re-run will produce different answers.
• Geographic context: supplied entirely in the query text ("… in Nashville, TN"). No IP-based or profile-based location simulation was used.
• Outcome classification per answer: cited (subject named and its website cited as a source), mentioned (subject named without citation), competitors_only (other businesses named, subject absent), no_businesses (the answer named no businesses).
• "Appearance" means the subject med spa was named in the generated answer — an outcome of cited or mentioned.

Anonymized market-level results

Reading the recency columns: "Service-evidence instances" counts reviews the pipeline's classifier attributed to a specific named service (a single review can qualify for more than one service, so instances can exceed unique reviews). The share column is the percentage of those instances dated within the 12 months before analysis. "Reviews retrieved" can be below the listing's total when the provider returns fewer usable reviews; for practices above 300 total reviews, retrieval was capped at the newest 300.

Visibility outcomes, in aggregate

• Entity control: four of the five subjects were found when asked about by name; one — a practice with an unconventional two-word name — returned an answer naming no businesses at all. Engines that cannot resolve a name cannot recommend it.
• 26 Botox-related buyer searches: subject med spas appeared in 3 (two were "crow's feet" phrasings; one was an uncited mention on a "Botox in {city}" search). Appearances are not attributed to lettered markets, per the anonymity policy.
• Focus diagnosis: the pipeline's own outcome classification diagnosed a Botox visibility gap in all five markets.
• Highest-reviewed competitors: in four of the five markets, the market's highest-reviewed comparable med spa (1,043–1,255 reviews) appeared in none of that market's Botox-related answers; in the fifth market it appeared.
• The most-reviewed subject: one subject held 1,363 reviews — more than any competitor in its comparison set (top rival: 1,099) — and appeared in none of its market's five Botox answers, while being cited for laser hair removal, its namesake service, the same day.
• What did appear: 100 distinct businesses were named across the 26 Botox-related answers (145 across all 45 answers). One Botox-related answer named no businesses at all.

Limitations

• Five med spas in five metros is an illustrative snapshot, not a representative sample; subjects were a convenience sample of highly rated practices (4.9–5.0 stars).
• One AI engine, one run per query, on a single date. AI answers vary between runs, engines, and dates; specific appearances should be treated as examples, not stable rankings.
• Service attribution of reviews is performed by an automated classifier and can contain errors; the same classifier was applied uniformly to all practices.
• Competitor selection is algorithmic (criteria above) and name-based chain filtering has known limits, disclosed where relevant.
• Review counts, ratings, and answers all drift over time; this page describes August 4, 2026.

Reproducibility

The method above is complete: the queries (except redacted entity controls), engine, geographic scoping, selection criteria, thresholds, and metric definitions are stated exactly as the pipeline executed them, and they match the dental study's methodology so the two studies are directly comparable. Because grounded AI answers are non-deterministic and public review data drifts daily, a re-run will not reproduce the specific answers — but it will test the same claim the same way. If you find an error in this methodology or its reporting, email [email protected]; the guide's corrections policy applies to this page.

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