Tracking 12 Clinics Separately Changed What They Fixed
A 12-location dental group tracking every clinic separately, using the toolkit to find where each one actually stood and fix the gaps.
Illustrative figures — sample data only.
The situation
The group had twelve clinics across three cities and no idea how any individual location was performing. Their reporting averaged everything into a single national number, which hid the fact that four clinics were invisible for their own city name while two were doing most of the work.
The first job was not optimisation. It was measurement — getting a clean, per-location baseline that nobody had ever produced.
What was done
Weeks 1–2 · Baseline every location separately
Each clinic was checked against its own city-level keywords rather than a national average. Running the same terms location by location exposed the spread immediately: positions ranged from 2 to 40 for the identical service keyword.
Week 3 · Fix the structured data
Nine of the twelve location pages carried either no LocalBusiness schema or schema whose address did not match the Google Business Profile. Each page was regenerated so the name, address and hours matched the profile exactly — the mismatches were doing more harm than the absences.
Week 4 · Rewrite titles that were being truncated
Every location page used the same 78-character title template, so Google was rewriting most of them and the city name was being cut. Shorter titles leading with the city and service were previewed before publishing.
Weeks 5–8 · Close the review gap
The two strongest clinics averaged four times the reviews of the weakest four. Direct review links replaced the previous “search for us on Google” request in follow-up messages, removing the step where most patients dropped out.
Ongoing · Re-check monthly, not daily
Positions were re-checked on a monthly cadence per location. Daily checking produced noise that triggered pointless changes; monthly checks made the real trend obvious and kept the team focused on the clinics that were genuinely stuck.
What actually moved the needle
Most of the gain came from two unglamorous fixes: making the schema match the Google Business Profile exactly, and measuring each location on its own rather than as part of an average. Neither required new content or link building.
The four invisible clinics were not being penalised. They were simply never measured, so nobody noticed they had the wrong address in their structured data for over a year.
Start With Your Own Baseline
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