A smarter patient-acquisition model for fertility clinics
Send fertility, IVF, and gynaecology traffic to neutral ground first, where patients can learn, engage, and build readiness before booking.
Built for fertility clinics that want better-prepared enquiries, better patient journeys, and more started cycles from low- and medium-intent traffic.
Powered by Fertility OS, your clinic's own AI brain →
One network. Four front doors on neutral ground.
Patients enter wherever their question lives. The network meets them there, instead of pulling them onto a clinic website they are not ready for.
Trying to conceive, fertility testing, what to do first.
Irregular cycles, PCOS, endometriosis. Questions that start with a symptom, not a treatment.
IVF, IUI, success rates, what a cycle involves.
Egg freezing, timing, planning ahead.
One operating layer behind all four. One editorial standard across every surface.
Not every fertility visitor is ready for a clinic website
Many fertility and gynaecology visitors are not looking for a clinic yet. They are looking for clarity. When they land on a clinic website too early, they often do not convert, or even engage.
Symptom-Led
Exploring symptoms without knowing if fertility is the issue
Exploratory
Researching options but not ready to approach a clinic
Treatment Evaluators
Comparing pathways and weighing decisions before committing
Action-Ready
Ready to engage but need the right entry point and experience
Low- and medium-intent traffic usually needs a different experience before it becomes clinic-ready.
Patients ask more honest questions on neutral ground.
On neutral ground, patients ask more openly, about symptoms, fertility basics, treatment options, and what to do next. That matters most in fertility, where uncertainty and hesitation are common long before anyone contacts a clinic.
Use a neutral knowledge layer before clinic handoff
Route different traffic types into the right neutral experience first, then bring patients into the clinic journey when they are ready.
Purpose-built for fertility clinics
This model is built around four dedicated fertility surfaces, not one generic healthcare destination.
Route every traffic type into the right experience
Search traffic, paid social, and educational campaigns each need a different landing experience. The destination should match what the user is trying to do.
These users usually need education, readiness support, and next-step clarity before provider selection.
These users are symptom-led first, not clinic-ready first.
These users are closer to evaluation or action and need a more direct treatment-stage journey.
These users are planning ahead, not treating a problem. They need a different conversation from someone trying to conceive now.
Education Ads
Myth vs fact, symptom education, egg freezing basics, PCOS awareness
Fertility.ai or Gynaec.ai
Build trust, answer questions, and create qualified retargeting pools
Generic Category Ads
Trying to conceive? Confused between IUI and IVF? Irregular cycles?
Fertility.ai, Gynaec.ai, or IVF.ai
Move users from awareness into structured evaluation
Branded & Higher-Intent Ads
Clinic campaigns, doctor campaigns, IVF consultation campaigns
IVF.ai or clinic handoff
Support action and reduce booking friction
Your doctor gets a patient summary before the first consultation
When a patient books, the attributed clinic receives a structured summary of what the patient said in their own words, so the first consultation starts informed, not cold.
Irregular periods, difficulty conceiving for 14 months
Irregular cycles (35–45 days), mild pelvic discomfort
14 months of active trying
Read about IUI vs IVF, explored AMH testing
IVF success rates for age 32, cost range for single IVF cycle, whether lifestyle changes could help first
Requested appointment, asked about doctor availability
Female doctor preferred, evening consultation slots
It records and organises. It never interprets, never grades, and never recommends a next step.
Patient consent is captured at the point of booking. Summary is shared only with the attributed clinic.
Care Attribution
Send your paid traffic to the network through your own entry URL. The patient learns on neutral ground. Your clinic appears once they are engaged, and the booking routes back to you.
How Care Attribution changes the dynamic
Referral URLs
Personalised entry URLs for each clinic. Attribution activates across pages, sessions, and domains.
Editorial badges
A warm, non-commercial badge, styled like a journal credit, not an ad. Six configurable styles.
Persistent attribution
Returning patients still see clinic branding, even without the original URL. Bookings route to the attributed clinic.
Attribution persists across sessions. A patient who returns weeks later still sees your clinic, and still books with you.
Six styles, from subtle to editorial
Each clinic chooses their default. Each campaign can override it.
On-page Bar
A sticky bottom bar with clinic name and logo. The most common choice.
On-page Corner Badge
A floating badge in the bottom corner. Compact when idle, expands on hover to show full details.
On-page Card
An editorial card embedded in content flow. Shows clinic name, logo, and a link to book.
On-page Minimal
Just the logo. No text. Expands on interaction. Presence without visual weight.
In-chat Header Pill
A persistent pill in the chat header. Stays visible throughout the conversation once it appears.
In-chat System Card
A one-time system card injected into the chat thread. Lower persistence, higher narrative weight.
One link. Full control.
Clinics build a referral URL with the parameters they need. Everything else is automatic.
| Parameter | What it does | Example values |
|---|---|---|
clinic | Which clinic's branding to show | northside-fertility |
style | Badge visual treatment | bar, corner, card, minimal, chat-header, chat-card |
text | Badge wording | with-care-from, supported-by, reviewed-by, your-specialist, guided-by |
src | Traffic source for analytics | meta, google, instagram, whatsapp |
cid | Campaign name for analytics | Any string the clinic defines |
trigger | When badge appears | second_pageview, scroll_depth, chat_interaction, immediate |
chat_trigger | When in-chat badge appears (chat-header and chat-card styles only) | immediate, after-3, after-6 |
Only clinic is required. Everything else is optional and has sensible defaults. The chat_trigger parameter applies only when style is set to chat-header or chat-card.
How the network stays neutral
Neutrality is not a claim. It is a set of rules the network runs on.
Distance, not popularity
Clinics are listed in distance order. Never by popularity, never by commercial relationship.
Accuracy is the gate
Verified data accuracy decides whether a clinic appears. Commercial status does not.
Education comes first
The badge appears only after the patient has engaged. Direct and organic visitors see a fully neutral experience, with no clinic present at all.
Attribution is consented and visible
The patient can see which clinic supported their reading, and the badge is never disguised as editorial endorsement.
The network does not tell a patient which clinic to choose. It answers the question they came with.
See exactly how your traffic performs
Every clinic gets a full analytics dashboard.
Funnel metrics
Total visitors → qualified conversations → readiness reached → booked. Full visibility from ad click to appointment.
Intent progression
Track how patients move from symptom-led → exploratory → evaluating → action-ready. See where your ad targeting is working.
Conversation insights
Anonymised, aggregated view of what patients ask most, common questions, objections, cost concerns, procedure hesitations.
Content consumption
Which topics and articles your referred patients engage with most. Surface demand signals and service gaps.
Drop-off analysis
Where patients disengage and what content preceded the drop-off. Identify what is blocking conversion upstream.
Benchmarks
See how your funnel compares against anonymised network averages.
All data is anonymised. No access to other clinics' data or direct/organic traffic. No raw conversation exports, only summaries and aggregates.
What fertility clinics gain from this model
Better lead quality
Mixed-intent traffic gets nurtured before clinic handoff, so enquiries arrive structured and better-prepared.
Better patient readiness
Patients reach the clinic with more clarity about symptoms, options, and next steps.
Better ad efficiency
Educational and generic paid traffic gets routed into environments designed to nurture before handoff.
Better patient experience
Users are guided in the right tone for their stage, not pushed too early into action.
Better revenue potential
Better-prepared patients and a clearer handoff give more enquiries a path to a started cycle.
Better first consultations
Your clinicians receive a structured summary of the patient's own words before the appointment, concerns, questions, readiness signals, preferences.
See how Medicly.ai can support fertility clinic growth through neutral patient journeys
Combine Fertility.ai, Gynaec.ai, and IVF.ai with Medicly's operating layer to improve patient readiness, conversion quality, and clinic handoff.