Top 34 to 1011+Geo-grid rank, before vs after (5 km radius)
The outcome
The situation
Google Ads had stopped making sense: ₹1,900 per lead, half of them asking for services the clinics do not offer. In their category people do not click ads for a stranger to treat their back; they call whoever the map shows first with real reviews.
What we found
Google Ads cost ₹1,900 a lead, and half of those leads asked for services the clinics do not offer.
Nobody picks a stranger to treat their back from an ad. They call whoever the map shows first, with real reviews.
A geo-grid audit showed the clinics ranked top 3 in only 11% of the map cells patients search from.
Where patients go
Nobody picks a stranger to treat their back from an ad. They call the first three on the map, with real reviews.
Illustration of the problem · requests and names are placeholders
The strategy
In a trust-heavy category, the map pack is the shop window. So the plan measured visibility the way patients see it, one grid cell at a time around each clinic, and then fixed the three things that move a cell: how complete the profile is, how many fresh reviews it carries, and whether there is a real page behind every treatment and location. The ad budget only came off once the map was doing its job.
Starting point
Move 01 · Measure
Move 02 · Rebuild
Move 03 · Reviews
Move 04 · Pages
Outcome
68%map cells ranked top-3, up from 11%
Execution
Each move below is a piece of the strategy above, with a drawing of the work as it ran.
Move 01 · Measure
A single rank number hides the problem. We ran a geo-grid around each clinic: a 9 by 9 grid of 81 search points across a 5 km radius, each one checked for map-pack position. Top 3 in only 11% of cells.
Move 02 · Rebuild
Every profile got its full service list, real treatment photos, practitioner bios and a post every week. Google ranks the listing that looks most alive and most complete, so we made all six look like that.
Illustration of the rebuild · names are placeholders
Move 03 · Reviews
A QR code at checkout asks every patient for a review while the visit is fresh. Every review gets a reply within 24 hours. Volume and recency compound, and a patient reading the profile sees a clinic that listens.
Back moving again after three sessions. Front desk sorted the next slot in a minute.
Writing
Illustration of the review loop · names are placeholders
Move 04 · Pages
The profile needs a page to send people to. We wrote one for each location and each treatment, built from the questions patients actually ask at the desk, not from a keyword list.
Location 2 · Back pain
Patients ask
Illustration of the page set · locations and treatments are placeholders
How it ran
Phase 1
Geo-grid around every clinic. Top 3 in 11% of cells.
Phase 2
All six profiles rebuilt with services, photos, bios and weekly posts.
Phase 3
QR at checkout, same-day replies, and the location and treatment pages go live.
Phase 4
Five months in: top 3 in 68% of cells, 3.2× the calls, Google Ads at ₹0.
The evidence
Each cell is a search point. Green is a top 3 map-pack position, amber is 4 to 10, red is 11 or lower.
Geo-grid rank, before vs after (5 km radius)
What carries over
A clinic can rank first on its own street and be invisible a kilometre away. The grid shows where patients cannot find you.
The review count grew because the front desk asked every patient, not because of a campaign.
Ad spend came off once organic calls had replaced it. The order matters.
Who led it
Co-founder · Organic growth
Runs the organic division: SEO, AI search visibility, Reddit and community marketing, and conversion. Six years of working out where buying decisions actually start.
What happens next
Free, no obligation. Your answers save as you go, so you can stop and pick up where you left off.
