Real-Time Propensity Modeling Delivers 50% Customer Growth for Fast-Scaling MedTech Client
How real-time propensity modeling gave ad platforms the conversion signals they needed without exposing protected data.
THE CLIENT
A high-growth MedTech company scaling aggressively across digital channels, with a business model that requires converting a high volume of leads into paying customers.
Our Results
50%
Increase in new customers
62%
Increase in lead-to-customer conversion rate
21%
Reduction in customer acquisition cost
Challenge
Navigate a privacy-constrained environment to deliver the conversion signals a fast-scaling MedTech company needed to fuel algorithmic campaign optimization.
Modern campaign optimization is algorithmic. Platforms like Meta and Google continuously learn from the behavior of users who interact with ads, submit forms, and ultimately convert, refining targeting and bidding in near real time. The quality of those signals determines the quality of the outcomes.
For healthcare and MedTech organizations, this creates a fundamental tension. Sharing the conversion data that powers platform optimization risks exposing sensitive prospect and patient information, putting companies in direct conflict with HIPAA and other privacy requirements. The result, for many organizations in this space, is a measurement gap that forces a choice between performance and compliance.
Our client was facing this challenge at the worst possible time: a period of aggressive, sustained growth that demanded strong campaign optimization to scale efficiently. They were not generating leads in small numbers and hoping for the best. They were scaling fast, and the accuracy of their conversion signals would determine whether their customer acquisition economics held as they grew.
Approach
Build a HIPAA-compliant lead scoring and propensity modeling system that could send real-time quality signals directly to advertising platforms without exposing patient or prospect data.
From Lead Generation to Customer Acquisition
Most digital marketing programs for MedTech companies are built around lead generation. But leads don’t generate revenue. Customers do. Our first step was to reframe the measurement objective entirely: rather than optimizing for lead volume, we would optimize for lead quality as a predictor of customer conversion.
To make this possible, we needed to know which leads were actually becoming customers. We achieved this by integrating client CRM data with Compass, our HIPAA-compliant marketing data warehouse. This integration created a closed loop between marketing activity and downstream revenue outcomes, allowing us to track not just when a lead was submitted, but when it converted into a paying customer and tie that conversion back to the originating campaign.
Building the Propensity Model
With a reliable lead-to-customer dataset in place, we worked with our client to analyze historical conversion patterns and identify the lead characteristics most predictive of downstream purchase. This analysis became the foundation of a lead scoring framework grounded in actual customer behavior rather than proxy metrics.
We then built a propensity model that assessed each lead submission algorithmically at the moment of form completion, assigning a conversion likelihood score in real time. Critically, this score was not just an internal diagnostic. It was designed from the outset to be actionable at the platform level.
Closing the Loop with the Advertising Platforms
To operationalize the propensity signal, we implemented a server-side data delivery architecture that transmitted each lead’s score to our advertising platforms at the moment of submission. This gave Meta, Google, and other platforms a granular, real-time quality signal to guide campaign optimization, replacing the blunt lead-count signals those platforms had been working with previously.
The entire system was designed and operated within our HIPAA-compliant infrastructure, ensuring that the richness of the signal never came at the cost of privacy compliance.
Outcomes
Propensity-powered optimization delivered 50% new customer growth with a 21% reduction in acquisition costs.
The impact was measurable within weeks and continued to build as the platforms learned from the improved signal quality.
Within six weeks of implementation, lead quality as measured by our propensity scoring framework improved by 150%. The platforms were no longer optimizing toward form completions indiscriminately. They were learning to find the people most likely to become customers.
The downstream effect on business performance was significant:
- 50% increase in new customers — the primary measure of business growth, reflecting more qualified leads moving through the full conversion funnel.
- 62% increase in lead-to-customer conversion rate — driven by a combination of higher-quality inbound leads and more efficient platform targeting.
- 21% reduction in customer acquisition cost — a direct consequence of the platforms allocating spend toward higher-propensity prospects rather than optimizing for raw lead volume.
The result was a program that scaled more efficiently as it grew. Better signals produced better targeting, which produced better leads, which produced more customers at lower cost. For a company in aggressive growth mode, that compounding dynamic was exactly what was needed.
Let’s talk
Want to see what real-time propensity modeling could do for your marketing?
Our team will walk you through what an implementation looks like for your funnel, your CRM history, and your compliance environment. No sales pressure, just a real conversation about whether this is the right capability for your business.
