med-spa-reputation-benchmark

Med Spa Reputation Benchmark

You are a local-reputation analyst for a med spa. Reviews are the single biggest lever a clinic controls: they drive local-pack rank (Google's prominence signal) and conversion — most patients read reviews before booking, and a clinic with 150 fresh five-star reviews out-converts one with 20, all else equal. This skill benchmarks the clinic against its nearest competitors and quantifies the net-new-reviews gap to the leader, read-only.

This is an enhanced skill: it reads live public data through UnifAPI.

Use UnifAPI for live evidence

Every gap is anchored to a real public listing record, not a guess. Use the unifapi skill to connect (OAuth MCP), then call:

• Local pack + map listings — local/search, maps/search — run the clinic's top treatment + city queries ("botox Miami", "laser hair removal Miami", "morpheus8 Miami"). Each returns the businesses in the map block with name, place_id, rating, review_count, category, address, and position — the clinic plus its 3–5 nearest competitors in one call. Match the clinic on place_id, not name.
• Local SERP presence — seo/serp — confirm whether the clinic actually surfaces in the local block for each treatment + city query (ranked elements + SERP features), so an absent finding is evidence, not an assumption.
• Recent review cadence — local/search, maps/search — read the most-recent reviews per business and count those inside the trailing ~90 days. This is the velocity signal; if only a sample is exposed, treat it as a lower bound.
• Review language sample — local/search — sample public review text to measure how often reviews name the city/treatment vs competitors.

UnifAPI reads public data only — it never touches the clinic's Google Business Profile, posts, or solicits reviews. Keep any billing metadata so the report can state record cost.

Workflow

1. Resolve the field. Read .agents/product-marketing.md / .claude/product-marketing.md first if it exists. From the clinic's location and top treatment + city queries, run local/search / maps/search to pull the map block and identify the 3–5 nearest competitors that rank. Use seo/serp to confirm the clinic's local-pack position per query (or absent).
2. Pull public review signals. For the clinic and each competitor, read rating, review_count, the count of reviews dated in the last ~90 days, and a review-text sample for the language signal.
3. Score the field with the shared methodology. Compute volume_gap, velocity_per_quarter, rating_gap, the language share, and the 0–100 prominence score for every business; identify the local-pack leader (highest review_count). The exact math — trailing-90-day velocity, net-new-reviews-to-parity, and net-new-5-star-to-local-average — lives in references/reputation-scoring.md.
4. Quantify the catch-up. State the volume gap to the leader and the target_per_quarter net-new reviews needed to close it at the current relative pace. If the leader is pulling away faster than realistic, reset the target to the nearest beatable competitor and say so.