Proprietary model · DACH aesthetics data · continuously learning
Zovi AI Labs is the research engine behind the platform. We turn the behaviour of real patients into precise decisions: which reward, to whom, in which moment. Quietly, automatically, and sharper every single day.
Purchase history, treatment rhythm, response patterns, seasonal behaviour. Most clinics never see any of it. Zovi reads hundreds of these signals at once and translates them into a single question: what brings this patient back, and when?
From each patient’s personal rhythm, Zovi infers when their next treatment is due, long before the patient thinks of it themselves.
200 bonus points
Anna
15% off HydraFacial
Maria
Free brow shape
Lena
Points, a discount, or an exclusive offer. The incentive is tuned to value and likelihood, never blasted to everyone.
Not too early, not too late. Zovi picks the window with the highest probability of conversion, per person rather than per campaign.
When Zovi senses fading engagement, it intervenes before the patient drifts away. Retention beats win-back.
We do not expose which variables the model weighs. That is the proprietary core. But here is what the output looks like.
Here is how the model works in the background, with zero effort from the clinic.
A HydraFacial every 6 weeks. Zovi remembers.
Zovi knows Anna returns for a HydraFacial roughly every 6 weeks. Her next window is opening soon.
The expected booking window approaches, yet there is no booking. The model raises her risk.
Zovi automatically sends a personalised coupon at the optimal moment, relevant rather than pushy.
Anna books. Revenue secured, loyalty renewed, and the model learns from the outcome.
No staff member had to remember. No list, no manual send. That is the difference between an app and an engine.
of consumers are more likely to buy with a personalised experience
Epsilon
average revenue lift from personalisation
McKinsey
profit increase from just a 5% lift in retention
Bain, Harvard Business Review
more likely to buy from brands with relevant recommendations
Accenture
Zovi is not a static rulebook. Every nudge sent, every booking, every silence flows back into a proprietary dataset that grows with each clinic and each patient. Modern language models translate those patterns into decisions, and into the right message, in any language and in any tone.
Aggregated, anonymised behaviour from the DACH aesthetics market. An edge no one can copy.
Language models turn raw signal into personalised offers and copy that converts.
More data leads to better predictions, more revenue, and more data again. The lead widens on its own.
HydraFacial + Vitamin C
Matched to your skin
Unlock the Conversational AI module and your patients get guided advice right inside the app. They describe their skin or concern, Zovi analyses it and recommends the right treatment from your own catalogue, booking included, in two taps.
Patients describe pigmentation, fine lines or acne, and the AI makes sense of it.
Instead of confusion, a clear recommendation from your treatment catalogue.
Patients feel guided, and guided patients book.
“71% of consumers expect personalised interactions, and 76% get frustrated when they do not happen.”
McKinsey, 2021
Too much choice paralyses. A well-timed, relevant suggestion removes the decision and makes the patient feel understood. That is where conversion is born. Not in the discount, but in the feeling of being guided.
Zovi AI Labs runs quietly behind every clinic on the platform and gets better every single day.
For competitive reasons, Zovi does not disclose which variables the model weighs. Statistics cited are drawn from publicly available industry research for context. Actual results vary by clinic.