Meridian Dental GroupSample customer
Healthcare · Multi-clinic · 18 clinics · India · Metro clusters
Demo Lab / modelled programme scenario
Location-level AI visibility across eighteen clinics
A multi-location dental group built a per-clinic view of how assistants answer local recommendation prompts, then fixed the entity and review signals behind the weakest sites.
Sample dataModelled scenario — the company names and numbers here are illustrative examples of how a CiteRank programme is reported. They are not real CiteRank AI customers, and no result shown has been measured for a named client.
The challenge
- —Local recommendation prompts returned aggregator directories rather than the group's own clinics.
- —Listing data drifted between locations, so engines could not resolve them as one brand.
- —No way to compare performance clinic-by-clinic for the marketing budget conversation.
What we ran
- —Location-split prompt sets covering treatment, price and 'near me' intents.
- —Entity-health review of every clinic listing, profile and schema block.
- —Quarterly source programme targeting the local publications engines quoted most.
Before / after
Metric movement over four months
| Metric | Before | After | Change |
|---|---|---|---|
| Clinics named in local prompts | 5 of 18 | 15 of 18 | +10 clinics |
| Entity Health Score | 44 | 82 | +38 pts |
| AI Mention Share vs local set | 9% | 34% | +25 pts |
ROI summary
What it was worth
+180/mo
New-patient enquiries citing an AI assistant
Self-reported at intake across all locations.
₹-9.6 L/yr
Marketing spend reallocated from directories
Directory listing renewals not repeated.
3 months
Payback period
Programme cost against enquiry value.
Illustrative modelling on sample data — not a projection of results for any specific brand.
Coverage
ChatGPTGeminiPerplexityAI OverviewsCopilotMeta AI
Vertical benches currently run 6 engines; full tracking covers all 8.
More scenarios
Modelled scenario
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