Client case study · 11 months Illustrative

34→119

New patient enquiries per month, over 11 months.

Elapsed: 11 months
How it was measured: Call tracking with dynamic number insertion plus form submissions, matched to booked appointments in the practice system.
Running alongside: A small paid search budget ran throughout at unchanged spend.
Published with permission: Anonymised at client request

The constraint

Competing with a platform, not with dentists

The competitor was not another practice. It was a national booking platform outranking every local provider on every commercial term.

Aggregators win on domain authority and inventory volume. A four-location group cannot out-authority them.

Aggregator held positions 1-3 on the head terms
Four locations, one thin page between them
No reviews requested in over a year

What we found

The audit, including the unflattering parts

One page covering all four locations, no schema anywhere, and a review programme that did not exist.

1 location page for 4 practices
No LocalBusiness or Service schema
6.8s to interactive on mobile
31 reviews across four listings, none recent

The plan

The 90-day roadmap as it was actually written

Do not fight the aggregator on head terms. Own everything they cannot do at scale.

A real page per location, with genuine local content
Provider-level pages, which an aggregator has no equivalent of
Review programme tied to appointment completion
Answer-capsule structure on every service page

Month by month

What shipped, and what it moved

The line extends as you scroll. Every step below is a real month, including the one where nothing happened.

01Month 1

Baseline

34 enquiries a month, one page for four practices, and an aggregator holding the top three results.

Baseline: 34/mo

02Month 2

Technical first

Schema, speed, and the four location pages built properly. No content published yet.

+12%

03Month 3

Provider pages

Individual dentist pages — the thing a national platform cannot replicate.

+24%

04Month 4

Reviews start

Requests tied to appointment completion rather than someone remembering.

+30%

05Month 5

Two listings suspended

A bulk hours update pushed two listings into review. Enquiries fell.

Flat month — visibility lost

06Month 6

Reinstated

Both back. We staged every subsequent edit across weeks.

+24%

07Month 7

Map pack, top three

Three of four locations in the top three for their primary term.

+17%

08Month 8

Service depth

Content built on answer capsules, which later paid off in AI answers.

+13%

09Month 9

Compounding

Reviews, pages and authority all working together.

+9%

10Month 10

AI citations

Cited in AI answers for metro-level dentist queries.

+8%

11Month 11

Where it landed

119 enquiries a month, from 34, with paid spend unchanged.

+250% total

What went wrong

The part most case studies leave out

Two of the four listings were suspended in month five after a bulk hours update. Both were reinstated, but it cost six weeks of map visibility at the busiest location.

In month five somebody updated opening hours across all four listings in one afternoon. Two went into review and were suspended.

Nothing about the edit was wrong. The pattern was the problem — four listings changed within an hour looks like bulk manipulation from the outside.

Both were reinstated on first appeal because the underlying profiles were clean. It still cost six weeks at the busiest location. Every edit after that was staged across weeks, one change type at a time.

The numbers

Where it landed, and how we know

New patient enquiries per month: 34 → 119 over 11 months. Call tracking with dynamic number insertion plus form submissions, matched to booked appointments in the practice system.

Enquiries are call-tracked with dynamic number insertion plus form submissions, matched to booked appointments. Paid search ran throughout at unchanged spend, so the delta is attributable to organic and map.

New patient enquiries: 34 to 119 a month
Map pack top three: 3 of 4 locations
Reviews: 31 to 240+
Suspensions survived: 2

Results reflect this client's market, licence status and starting position. We publish the method alongside the number so you can judge whether it transfers to yours.

In hindsight

What we'd do differently next time

Stage listing edits from day one. We knew better and did it anyway.
Build provider pages before service pages — they converted harder and ranked faster
Start the review programme in month one; it was the cheapest lever and we deployed it in month four

Before you ask us to do this for you

Does this actually apply to your situation?

This worked because of specific conditions. If yours don't match, say so on the first call and we'll tell you what would change.

Market: Multi-location healthcare competing against national aggregators.
Starting point: Claimed listings, even neglected ones, and appetite for a review programme.
Timeline: Nine to twelve months. Aggregators do not give ground quickly.

Start with the audit. It's free and it's specific.

Send us a URL. You'll get back the technical issues, your AI-search readiness score, what competitors are doing that you aren't, and what we'd fix in the first ninety days.

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