Multi Site Performance Standardisation
A 2.5x revenue gap between best and worst locations couldn't be meaningfully addressed without knowing whether the gap was operational, commercial, or structural.
- Timeline
- Phased 18 month programme. Unified data platform live across all locations by month 6. Marketing centralisation by month 8. Full benchmarking and standardisation complete by month 14.
- Scope
- Data platform unification, group marketing intelligence, consultation protocol standardisation, performance benchmarking, and variance diagnostic framework across 12 locations.
- Model
- Central programme management team with location leads as implementation partners. Monthly group performance reviews introduced from month 7. Location general managers retained operational autonomy within the standardised framework.
The outcome
Group EBITDA margin improved from 19% to 27%; underperforming locations grew revenue by 28 to 45%; strategic investment closed at 2.4x.
Findings
What we built it around.
Unified data platform
single practice management system deployed across all 12 locations, integrating scheduling, clinical documentation, patient communication, and financial reporting into a consistent data model
Group level marketing intelligence
centralised AI marketing platform aggregating performance data across all locations, applying predictive targeting trained on the group's combined patient dataset, with dynamic budget allocation to highest performing channels
Standardised AI consultation protocol
facial analysis tools and outcome simulation deployed uniformly across all locations, with practitioner training calibrated to consistent protocol standards
Performance benchmarking layer
location level KPI dashboard with peer comparison, tracking acquisition cost, conversion rate, utilisation, revenue per patient, and 90 day retention, giving the central team the ability to see where individual locations deviated from group norms
Variance diagnostic framework
structured analytical methodology to separate commercial underperformance (addressable through intervention) from structural disadvantage (addressable through investment or exit decision)
Results
What changed.
Group level patient acquisition cost fell 34% within 12 months as centralised AI marketing replaced inconsistent local spend
The three underperforming locations improved revenue by 28%, 41%, and 45% respectively, as operational standardisation and group calibrated marketing replaced locally improvised approaches
Group EBITDA margin improved from 19% to 27%, representing approximately £3
2M in additional annual profit on a group revenue base of approximately £18M
The group received a strategic investment at a valuation 2
4x above the pre programme level; the investor specifically cited the group's proprietary patient outcome dataset and centralised AI marketing infrastructure as primary valuation drivers
Takeaway
Group EBITDA margin improved from 19% to 27%; underperforming locations grew revenue by 28 to 45%; strategic investment closed at 2.4x.
Medical Aesthetics
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