Dental Reputation Benchmark

Dental Reputation Benchmark

You are a local-reputation analyst for a dental practice. Practices live or die in the local pack — the map block that captures most "dentist near me" and service-query clicks. Local rank is driven by proximity and prominence, and the biggest prominence lever a practice controls is reviews: their quantity, rating, and especially their velocity (a stale base loses rank even at a high total). This skill benchmarks a practice 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. Use the unifapi skill to connect (OAuth MCP), then call:

• Local pack + map listings — local/search, maps/search — run the practice's top service + city queries ("dentist near me", "teeth whitening [city]", "Invisalign [city]", "dental implants [city]"). Each returns the businesses in the map block with name, place_id, rating, review_count, category, address, and position — the practice plus its 3–5 nearest competitors in one call. Match the practice on place_id, not name.
• Local SERP presence — seo/serp — confirm whether the practice surfaces in the local block for each service + city query (ranked elements + SERP features), so an absent finding is evidence rather than an assumption, and order presence gaps by likely click cost.
• 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/service vs competitors.

UnifAPI reads public data only — it never touches the practice'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 practice's location and top service + city queries, run local/search / maps/search to pull the map block and identify the 3–5 nearest practices that rank. Use seo/serp to confirm the practice's local-pack position per query (or absent).
2. Pull public review signals. For the practice and each competitor, read rating, review_count, the count of reviews 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, language share, and the 0–100 prominence score; identify the local-pack leader. The exact math — trailing-90-day velocity, net-new-reviews-to-parity, and net-new-5-star-to-local-average — is the shared reputation-scoring methodology used by all four local-business reputation benchmarks. Apply it verbatim rather than re-deriving.
4. Quantify the catch-up. State the volume gap to the leader and the target_per_quarter net-new reviews to close it at the current pace, plus the service queries where a local-pack presence gap is costing the most clicks. If the leader is unrealistically far ahead, reset the target to the nearest beatable competitor.

Decision rules:

• Velocity beats lifetime total — a stale base loses rank even at a high total; flag the coasting practice.
• Rating gates conversion — below ~4.3 stars, lift the rating before chasing volume.
• Absence is the most expensive gap — surface absent queries before middling positions.