Is this a real clinic case study?

No. This is a simulated proof-of-method asset built with synthetic sample data. It is not a real clinic, patient, ad-account, booking, revenue, ROI, medical or compliance claim.

What does the diagnostic show?

It shows how AICS would map paid ads, WhatsApp referrals and web forms into response SLA, booking/show, source-attribution, no-show follow-up, owner-assignment and consent-evidence queues before a clinic increases spend or automation.

Can this guarantee appointments or ROI?

No. The metrics are synthetic arithmetic outputs, not real media performance or patient-growth results. A real deployment would need approved clinic evidence before any outcome could be published.

Simulated proof asset · India Healthcare GrowthOS

Simulated India specialty clinic ad-to-appointment diagnostic

This no-fake-client proof asset shows how AICS can inspect patient-growth leakage before a clinic spends more on Google, Meta, WhatsApp referral follow-up or website campaigns. It is synthetic only: no real clinic, patient, medical outcome, DPDP compliance, ad-platform result, booking, revenue or ROI claim is made.

View diagnostic package Request fit check View India clinic checklist Back to proof hub

Important claim boundary: this page is a simulated proof-of-method demonstration. It is not a customer case study, not a testimonial, not a patient outcome, not legal, privacy, medical or compliance advice, not DPDP attestation, not ad-platform certification, and not a revenue, booking, ranking or ROI promise.

Synthetic enquiries768Google, Meta, WhatsApp referral and web-form rows

15-min contact coverage38.0%476 sample enquiries missed the fast-response window

Booking rate31.2%from synthetic enquiry to booked appointment

Show rate72.1%synthetic booked appointments that showed

Consent-evidence gap378records need proof before automation

Owner-assignment gap358enquiries without explicit staff ownership

Source-attribution gap136mostly WhatsApp referral attribution gaps

Paid sample CPL₹226synthetic arithmetic only; not real media performance

Diagnostic method

What an owner can inspect before scaling clinic marketing

The diagnostic converts lead rows into operational queues: response SLA, booking handoff, show/no-show follow-up, consent evidence, source evidence and owner visibility.

Channel	Leads	Spend	15-min contact	Booking rate	Show rate	Owner-gap leads	Source-gap leads	Consent-gap records
Google Search	274	₹68,500	43.1%	34.3%	74.5%	0	0	102
Meta Ads	244	₹48,800	25.8%	23.4%	64.9%	244	0	145
Website Form	114	₹0	30.7%	30.7%	68.6%	114	0	68
WhatsApp Referral	136	₹0	55.9%	39.7%	77.8%	0	136	63

Before diagnostic

Paid and organic enquiries are reviewed when staff are free, not by SLA.
WhatsApp referrals work socially but lack source evidence for reporting.
No-shows are visible after appointment review, not as an operating queue.
Automation begins before every contact has consent/notice evidence.
The owner sees spend and lead volume, but not leakage by status and staff owner.

After diagnostic operating rule

Every paid lead creates an SLA timer, owner assignment and same-day closure reason.
Every enquiry has source recorded yes/no before channel reporting is trusted.
No-show follow-up gaps are counted weekly and pushed into callback queues.
AI/SMS/WhatsApp automation is blocked or human-reviewed when consent evidence is missing.
The owner dashboard shows missed SLA, source gaps, consent gaps and unresolved queues before budget increases.

Evidence needed before publishing any real clinic outcome

A real proof asset would require written clinic approval, de-identified enquiry exports, source and ad-spend evidence, appointment-status history, consent/notice artefacts, exclusion handling for clinical/sensitive requests, before/after dates, change logs and legal/compliance review before any patient, privacy, DPDP, ad-performance, revenue or ROI statement.

View ad-to-appointment package Request diagnostic fit check View DPDP healthcare package

Reproducibility

Internal synthetic artifact: /home/agent/.hermes/aicloudstrategist/case-studies/simulated-india-specialty-clinic-ad-to-appointment-2026-07-12/. The generator regenerated this report with input SHA256 d1aa781ef3523358d43f6aed7056679895f66047f77df3caace25fa288344128 and report SHA256 0604ef928dd4593d1516f84e99c6287ff5cb9694e803ae220c6634f020ec69f3.

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