A worked example of the return on investment from an AI receptionist at an aesthetic clinic — with every assumption stated, so you can swap in your own numbers.

Making the ROI Case

Every technology investment in a clinic should be evaluated against a simple question: what is the measurable return, and how quickly does the investment pay for itself?

For an AI receptionist, the costs are well-defined and the revenue side can be estimated from a few numbers you already know. Below is a worked example. Every figure in it apart from the subscription price is an assumption — replace them with your own.

The Cost Side

A fully deployed AI receptionist system for an aesthetic clinic costs approximately:

Platform subscription: $88–288/month depending on features and volume
Setup time: a few hours of an owner's or manager's time (an assumption — it depends on how many services you add)
Ongoing management: 1–2 hours per month for reviewing conversations and updating service information

No technical staff. No developer costs. No infrastructure investment.

For comparison, a part-time receptionist focused primarily on WhatsApp management costs $1,200–2,500/month in Singapore, $800–1,500/month in Malaysia.

Revenue Recovery: After-Hours Bookings

The most direct revenue recovery comes from after-hours inquiry capture.

Hypothetical clinic — every number here is an assumption, not measured data: 30 inquiries per day across Telegram, Instagram DM and Messenger, 40% arriving after hours (12 inquiries). Without automation, none of those 12 get a reply before morning. Assume an after-hours inquiry converts at 30% with an instant reply and at 5% when it waits until morning; these rates are illustrative, not observed.

After-hours bookings without AI: 12 × 5% = 0.6 bookings/day
After-hours bookings with AI: 12 × 30% = 3.6 bookings/day
Additional bookings per day: 3 bookings
Average appointment value: $250
Additional monthly revenue: ~$22,500 (open 30 days a month)
Annual recovery: ~$270,000

These figures follow entirely from the assumptions above. Receptys does not yet have customer data to validate them — halve the conversion rates and the result halves too.

Cost Reduction: Staff Time

Assume front desk staff at a busy clinic spend 2–4 hours per day on message management, at $15–20/hour. That is $30–80 of labour cost per day — just for messaging.

If the AI handles most of that volume — say 70–80%, again an assumption — it recovers roughly $20–60/day in staff time cost.

No-Show Reduction

Automated reminders are designed to cut no-shows by making forgetting less likely. How much they help varies by clinic, and Receptys has no customer data to quote.

To keep the example going, assume a clinic with 15 daily appointments and a 12% no-show rate — 1.8 no-shows a day — and assume reminders prevent a third of them. That is 0.6 appointments a day, worth $150 at a $250 average ticket.

The ROI Summary

Pulling the assumptions above together for the hypothetical clinic ($250 average ticket, open 30 days a month):

AI receptionist monthly cost: $168 (mid-tier plan)
After-hours booking recovery: +$22,500/month (3 extra bookings a day)
Staff time cost reduction: +$600–1,800/month
No-show reduction value: +$4,500/month (0.6 appointments a day)

Every line except the subscription price is an assumption, and the after-hours line dominates. The two inputs that matter most are how many after-hours inquiries you receive and how many of them would book with an instant reply — measure those before you decide.

AI Receptionist ROI: A Worked Example for Clinics