Simulated proof asset · Updated 2026-07-10
Clinic WhatsApp/call response SLA benchmark
A transparent, simulated benchmark for owner-led dental and healthcare clinics that want to quantify missed enquiry leakage before buying automation.
Honesty label
This is a simulated internal benchmark. It is not client work, not a testimonial, and not a claim that any real clinic achieved these numbers. It shows the diagnostic model AICloudStrategist can use before a paid engagement.
What is being tested
Many clinics lose patient enquiries when WhatsApp replies, missed-call callbacks, or treatment-plan follow-ups happen hours later. This model tests the value of moving to a simple daily SLA: every lead logged, first response within 5–15 minutes, and follow-up reason recorded.
Scenario output
| Scenario | Monthly enquiries | Current SLA | Target SLA | Booking rate before → after | Incremental bookings/month | Estimated monthly upside |
|---|---|---|---|---|---|---|
| Conservative single-location dental clinic | 120 | 240 min | 15 min | 18% → 23% | 6.0 | ₹18,840 |
| Moderate multi-chair clinic | 220 | 180 min | 10 min | 20% → 27% | 15.4 | ₹78,540 |
| High-intent implant clinic | 90 | 120 min | 5 min | 24% → 34% | 9.0 | ₹128,700 |
Aggregate model output
- Total simulated enquiries/month: 430
- Baseline booked consults/month: 87.6
- Target booked consults/month: 118.0
- Incremental booked consults/month: 30.4
- Combined estimated monthly upside across all three scenarios: ₹226,080
Evidence checklist for a real clinic
- 30 days of enquiry counts by channel: calls, WhatsApp, website forms, Google Business Profile.
- Timestamp samples for first response and first follow-up, excluding patient health details.
- Booked consult count by lead source.
- No-show and treatment-plan follow-up counts.
- Consent/notice screenshots for WhatsApp follow-up, appointment reminders, and review requests.
- One-page before/after dashboard with source, response SLA, booked consult, and follow-up status.
7-day pilot implementation
Map lead sources, create a consent-safe enquiry log, add three templates, review SLA daily, give the owner one visibility summary, fix the top leakage source, then package before/after diagnostic evidence.
Reproducible artifact
The source model and generated markdown report are stored internally at /home/agent/.hermes/aicloudstrategist/case-studies/simulated-clinic-whatsapp-sla-2026-07-10/. Inputs are explicit in scenarios.csv; calculations are deterministic and simple enough for an owner to challenge before paying.