Modeling Drives Efficiency & More New Patients
Bayesian marketing mix model identified opportunities for smarter use of marketing dollars and incremental hospital growth within a set budget.
-
30%
Improvement in cost per acquisition efficiency
At a Glance
- Client
- Large Hospital Network
- Engagement
- Marketing Mix Modeling
- Project
- Dual Patient Decision Path Map
- Focus
- Increase Marketing Efficiency
- Duration
- Two Years
Overview
The work turned spend decisions into a repeatable model for growth.
A large hospital network needed a clearer way to grow marketing-driven new patient acquisition inside a fixed budget.
We built a connected marketing mix modeling system that surfaced the truth in the system: two different patient decision paths, each with different timing and response patterns.
This gave a defendable view of channel ROI, diminishing returns, and a mix that could drive more new patient volume with less waste.
The Proof
The hospital network shifted spend with more confidence and better visibility into marginal gain. Those gains came from a model the team could explain, defend, and use again as the budget moved. They improved cost per acquisition efficiency over two years all while reducing overall working media spend.
-
8%
Reduction in working media spend
-
2
Patient decision paths modeled
The Goal
Increase the number of marketing-driven new patients using the existing budget, while also quantifying how a sister brand’s marketing influenced hospital new patient volume.
The Challenge
The hospital network needed to grow acquisitions without adding spend. They also needed a clearer read on which channels actually drove new patients, how returns changed as spend increased, and how outside factors like COVID-19 affected demand.
The absence of that clarity made incremental budget decisions harder to defend.
The Solution
We collected historical marketing and operations data, then added relevant contextual inputs, including regional COVID-19 case counts. Using a Bayesian marketing mix model, the team analyzed new patient volume patterns and identified two underlying patient types: one with a longer decision process and one that moved faster.
The model quantified channel-level ROI, diminishing returns, and the optimal mix for maximizing new patient volume. With that architecture in place, the hospital network shifted channel mix and made incremental spend decisions based on predicted marginal gains across channels.
