ROI scenario · Toronto dental clinic
What a missed-message workflow could be worth to a Toronto dental clinic.
This is an illustrative model, not a customer case study. It uses ordinary assumptions for a single-location Toronto clinic: 20 patient messages a day, a quarter of them unanswered, and a conservative share of those conversations recovered by an AI receptionist.
- Illustrative model
- Not a customer result
- Assumptions shown
- Updated October 2026
Read this first
This is an illustrative planning model, not a customer case study. No clinic data was used and no result is promised. Every assumption is shown so you can replace it with your own numbers.
The scenario
A profile, not a customer.
- Clinic type
- Single-location general dental
- Market
- Toronto, Ontario
- Channels
- WhatsApp + website chat
- Front desk
- Two staff, shared with in-person patients
The problem
The queue nobody owns.
The front desk is busiest when patients are in the clinic. A WhatsApp message that arrives at 10:15am may not get a reply until the afternoon, and an after-hours message waits until the next morning.
The clinic does not have a lead problem. It has a response-time problem: the patient who does not get a quick answer often books elsewhere, and nobody records the loss because the conversation never became an appointment.
The approach
Three steps, no PMS migration.
01
Import the clinic website so the assistant knows the services, dentists, hours and published FAQs.
02
Route WhatsApp and website chat to one assistant that answers routine questions and captures booking requests.
03
Keep the front desk as the confirmation point: the team reviews requests, confirms the time and records the appointment in the PMS.
The model
Before and after, with the assumptions visible.
| Metric | Before | After (model) |
|---|---|---|
| Patient messages per day | 20 | 20 |
| Unanswered share | 25% | 10% (model assumption) |
| Missed messages / month | 150 | 60 |
| Bookings recovered / month | — | 18 (model assumption) |
| Revenue recovered / month | $0 | $4,500 |
Monthly revenue recovered
$4,500
At a $250 average appointment and a 30% recovery assumption.
Annualised
$54,000
Same assumptions for 12 months.
Messages still handled by a human
60/month
The assistant reduces the queue; it does not eliminate the human role.
Caveats
What this model does not prove.
- This is a planning model, not a customer result. No clinic data was used.
- It assumes a share of unanswered messages would have booked at the average appointment value.
- ClinicBot handles WhatsApp and website chat; it does not answer phone calls or write into a PMS.
- Your actual result depends on your volume, response time, patient mix and front-desk follow-up.
FAQ
Toronto single-location dental clinic: common questions
Replace the model with your own numbers.
Use the calculators with your clinic data, then test the assistant on your own website before you decide.