Digital health marketing analytics faces a barrier that standard growth reporting does not: the data with the strongest marketing signal is often the most restricted from leaving your systems. We create measurement infrastructure that honors that constraint while still giving you a clear view of what is working.
The Challenge
Your strongest attribution data sits precisely where you cannot share it with an ad platform
Standard growth marketing attribution relies on passing event data – what page a user viewed, what action they took – back to ad platforms to optimize targeting and measure ROI. In digital health, much of that event data is PHI-adjacent or PHI itself the moment it touches a health condition, insurance status, or clinical action, and sending it to a third-party ad platform creates real compliance exposure. Most marketing teams either strip out the signal entirely, crippling optimization, or quietly send more than they should.
You report business metrics and clinical outcome metrics separately, even though your stakeholders need to see them together
A payer, employer, or health system partner does not just want to know your marketing generated leads – they want to see engagement and outcome data that justifies the clinical and financial case for the partnership. Marketing teams that only report growth funnel metrics, and clinical teams that only report outcome data, leave the connection between the two implicit, when the partnership case is actually built on showing how they relate.
Board and investor reports often rely on growth metrics that do not reflect how digital health businesses truly create value
Generic SaaS growth metrics like MAU or signup-to-paid conversion do not capture what matters in digital health – eligible member activation, clinical engagement depth, retention through a full care episode, or payer/employer channel pipeline. Reporting a growth dashboard built for a different business model to a board that understands this category's real value drivers reads as either naive or evasive, even when the underlying performance is strong.
No one owns the reconciliation of marketing data with clinical and claims data, so the systems diverge and neither report is fully trusted
Marketing analytics typically lives in a martech stack, while clinical and claims data lives in a separate, more locked-down system for good compliance reasons. Without someone actively reconciling the two, teams end up with two versions of the truth – a marketing dashboard and a clinical operations dashboard – that never quite agree, and leadership loses confidence in both.
How We Can Help
We begin by auditing which data can legally and safely move into which systems, partnering with your compliance and engineering teams to identify exactly where PHI boundaries exist in your current tracking setup. This establishes the true limit on attribution granularity before we design anything, rather than uncovering those limits after a tracking implementation has already been built and found non-compliant.
We then design a measurement architecture that captures the maximum marketing signal within those actual boundaries – server-side tracking, data clean rooms, aggregated and de-identified event data, and first-party modeling methods that allow you to optimize channels without sending PHI to third-party platforms. It is a different toolkit from the standard growth analytics stack, purpose-built for this constraint.
We create a unified reporting layer that links marketing funnel metrics with downstream engagement and outcome data, allowing payer or employer partnership conversations to present the entire story – not only "we generated leads," but "leads converted to eligible members who engaged with the product and showed measurable outcomes," which is the real case a partner needs to evaluate.
For board and investor reporting, we develop dashboards around metrics that genuinely represent value creation in digital health – eligible activation rate, engagement depth, retention through a care episode, and B2B2C pipeline health – rather than applying a generic SaaS metrics template that does not fit how the business actually operates.
We put one reconciliation process in place across marketing and clinical/claims data, ensuring both teams use the same underlying numbers even when the systems remain appropriately separate for compliance purposes. This closes the trust gap created when two dashboards quietly report different results.
Unlike a standard marketing analytics engagement, we build the complete measurement architecture around the actual compliance boundary for this category, rather than implementing a conventional martech stack and hoping it passes compliance review.
What we deliver
- Data flow audit mapping exactly where PHI boundaries sit across your current marketing tracking setup
- Privacy-safe measurement architecture using server-side tracking, clean rooms, and aggregated modeling
- Unified reporting layer connecting marketing funnel data to downstream engagement and outcome metrics
- Board and investor dashboards built around digital-health-specific value metrics, not generic SaaS templates
- A reconciliation process aligning marketing and clinical/claims data without violating system separation requirements
The digital health teams achieving the strongest marketing performance are not those with the most data – they are the ones that learned to optimize within the PHI boundary instead of quietly bypassing it or abandoning attribution altogether.
Our Methodology
We deliver data, reporting, and analytics work for digital health through a 90-day sprint. During the first 30 days, we audit your existing data flows against actual PHI boundaries and identify any compliance exposure already present in your tracking setup. From days 30 through 60, we build the privacy-safe measurement architecture and unified reporting layer that links marketing with outcome data. During days 60 through 90, we launch board-ready dashboards and the marketing-clinical reconciliation process, while training your team to maintain both moving forward.
Unlike a general marketing analytics agency that installs a standard martech stack, we architect everything around the real PHI boundary in this category from the outset. We partner directly with your compliance and engineering teams instead of treating them as a final approval gate. Success is measured by whether your team trusts the numbers enough to act on them and whether the reporting withstands compliance review, not simply by how complete the dashboards appear.
Our Working Process
During the first 30 days, we assess your current tracking implementation against actual PHI boundaries, collaborating with compliance and engineering to identify both the limitations and compliant routes to stronger attribution.
From days 30 through 60, we develop the privacy-safe measurement architecture and unified marketing-to-outcomes reporting layer. We need engineering resources from your team to implement server-side tracking updates, along with access to the owners of both marketing and clinical/claims data so the reconciliation process is built correctly.
From days 60 through 90, we launch board and investor dashboards based on digital-health-specific metrics and complete the reconciliation process, training your internal team to manage it. Most engagements last three to six months because of the engineering lift involved in compliant tracking infrastructure, although reporting improvements become visible well before the full architecture is finished.
Our cadence includes a weekly working session with marketing and engineering, along with a monthly reconciliation review to verify that marketing and clinical/claims figures remain aligned.
- Compliant attribution that actually improves channel optimization
- A single reporting layer connecting marketing performance to outcome data
- Board and investor dashboards built around metrics that reflect real value creation
- Marketing and clinical teams working from reconciled, trusted numbers
If your digital health company needs data, reporting & analytics leadership, we should talk.
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Frequently asked questions
What does a data and analytics engagement cost for a digital health company?
Pricing depends significantly on your existing tracking infrastructure and the amount of engineering required to create compliant, privacy-safe measurement. Implementation costs less for companies that already have a data warehouse and engineering support than for those beginning with a standard, unchanged martech stack.
How soon will we see results from an analytics engagement?
The data-flow audit and initial architecture design are generally completed within 30 days. Because of the engineering work required, fully implementing privacy-safe tracking and the reconciliation process usually takes an entire quarter, although clearer reporting often emerges well before the infrastructure is completely finished.
How will your analytics team collaborate with our engineering and compliance staff?
We partner directly with your engineering team to implement server-side tracking and clean-room methods, while working with compliance to validate every element of the architecture against actual PHI boundaries before launch. Dedicated engineering time is required throughout the build phase.
How is Winston Francois different from a typical marketing analytics agency?
Most marketing analytics agencies deploy a conventional tracking stack designed for unrestricted data movement. From day one, we architect around the actual PHI boundary in digital health, treating compliance and engineering as essential partners instead of final-stage approvers.
How is ROI measured for a data and analytics engagement?
We assess whether your team's reporting decisions truly stand up – whether the new architecture improves channel optimization and whether marketing and clinical data remain reconciled over time. No matter how complete it appears, a dashboard that nobody trusts is not a result.
What kind of digital health company is best suited to this service?
This service is suited to digital health companies with active marketing spend that are either under-tracking to remain compliant or over-tracking in ways that introduce genuine exposure, typically those with 5M to 100M ARR and at least basic engineering resources available. Begin with a strategy call and review of your existing tracking implementation.
