AI-Optimised Patient Acquisition
New patient acquisition cost fell from £178 to £104 within 6 months; lead-to-consultation conversion up 29%.
6-week build and transition. Attribution model live from week 4. Full new campaign structure from week 6. Management team operating independently by month 4.
The challenge
Patient acquisition cost had doubled in two years with no improvement in lead quality — the practice was spending its way to lower margins.
The practice's marketing spend of approximately £8,500 per month was generating a new patient acquisition cost of £178 — up from £95 two years earlier. The agency running paid social had no aesthetics-specific audience models, no treatment-category creative strategy, and no attribution model that distinguished between consultation bookings and general enquiries. Spend was being optimised for click volume, not qualified patient conversion. Email and SMS to the existing patient base — arguably the highest-ROI channel available — were being used only for promotional campaigns with no personalisation or behavioural targeting. The data to do better was available; the capability to use it wasn't.
What we did
The approach
We replaced the generalist agency approach with an AI-optimised patient acquisition system built on treatment-category audience modelling, dynamic creative optimisation, and a reactivation programme targeting the practice's existing patient base. The system was designed to be owned and operated by the practice's management team, not dependent on ongoing agency management.
Key findings & actions
Treatment-category audience modelling
lookalike audiences built from the practice's highest-LTV patient segments, segmented by treatment category (injectable, skin, body) rather than demographic alone
Dynamic creative optimisation
AI-managed creative testing across treatment-specific ad sets, with automated budget allocation to top-performing creative and audience combinations
Attribution model
last-meaningful-touch attribution built specifically for the aesthetics conversion journey — distinguishing between enquiry, consultation booking, and treated patient — giving the practice a true cost-per-treated-patient metric
Existing patient reactivation
behavioural segmentation of the patient database identifying lapsed patients by treatment category and last-visit date, with personalised reactivation sequences by segment
Monthly performance reporting
single-page dashboard covering cost per treated patient by channel, conversion rates at each funnel stage, and reactivation programme performance — owned by the practice manager, not the agency
How we worked
Scope
Audience modelling, paid social restructure, attribution model build, existing patient reactivation, and management team capability transfer.
Timeline
6-week build and transition. Attribution model live from week 4. Full new campaign structure from week 6. Management team operating independently by month 4.
Operating model
Capability transfer was a design principle from the start — we built the system to be operator-owned, not agency-dependent. Fortnightly review calls for the first 3 months, transitioning to monthly by month 4.
Outcomes
What changed
New patient acquisition cost fell from £178 to £104 within 6 months; lead-to-consultation conversion up 29%.
New patient acquisition cost reduced from £178 to £104 within 6 months
a 42% reduction — against a 15% increase in total new patients treated
Paid social conversion from lead to consultation booking improved from 31% to 40%, driven by treatment-category audience specificity and improved creative performance
Existing patient reactivation programme recovered 87 lapsed patients in the first 90 days, generating approximately £52,000 in treatment revenue from a channel that had previously been unworked
Practice management team took full ownership of the attribution dashboard from month 4, reducing dependency on external agency reporting and enabling faster spend allocation decisions
Governance
Trust, collaboration & governance
Attribution model methodology documented and explained to the practice manager — no black-box reporting
Creative testing results shared in full, including what didn't work, to build the team's marketing judgment
No retainer dependency built into the engagement design — the system was handed over with full documentation
Patient data used in reactivation programme handled in compliance with applicable data protection frameworks
Reframe
The problem wasn't the channel — it was the absence of attribution from ad spend to treated patient.
Across every engagement, the goal is the same: engineer a system that makes better decisions — faster, more consistently, and at scale — than the process it replaces.
Start a discovery
Most engagements begin with a conversation about context.
We do not send a proposal before we understand the problem. Start by telling us about your decision context — we will identify the highest-leverage intervention areas before any scope is agreed.