Digital health companies collect contact data across a clinical record system, billing platform, patient or member app, and marketing tool – yet none of them agree on who a person is or which stage they're in. Lifecycle marketing layered on top of that mess delivers the wrong message to the wrong segment at the wrong moment. We build the CRM and data infrastructure – segmentation logic, hygiene, and automation architecture – that everything else relies on.
The Challenge
The same person appears as five separate records across your systems, with no one responsible for reconciling them
A patient or member typically enters your ecosystem through an app signup, gets a separate record when they complete onboarding, another when billing kicks in, and possibly a fourth from a referral or employer benefits feed. Without a defined identity resolution process, these records never merge cleanly, which means segmentation logic built on top of them is working with fragmented, sometimes contradictory data. Marketing sends a re-engagement email to someone who already converted through a different record, and the team can't explain why until someone manually audits the CRM.
Your segmentation relies on stale or self-reported fields rather than real behavioral and clinical-adjacent data
Most digital health CRMs segment users by static intake fields – condition type, signup date, plan tier – because that data is easy to access, not because it's the right signal. Behavioral data that actually predicts churn or engagement, like session frequency, care-plan adherence, or feature usage, often sits in a separate product analytics tool that was never piped into the CRM. The result is segmentation that looks sophisticated on paper but doesn't correlate with what actually drives outcomes, so campaigns built on top of it underperform even when the messaging itself is good.
Each new automation is created as a one-off workflow because there's no underlying data architecture to support it
Without a clean data model, each new lifecycle automation – onboarding, renewal reminder, re-engagement – gets hand-built by whoever is available, using whatever fields happen to be accessible at the time. That produces a sprawl of disconnected workflows that break silently when an upstream field changes, and no one on the team can say with confidence which automations are still running correctly. Technical debt in the CRM compounds every quarter it goes unaddressed, and eventually a rebuild becomes unavoidable and far more expensive than doing it right the first time.
PHI and consent handling within your CRM was added after the fact and fails to stand up to scrutiny
Digital health companies often start with a standard marketing CRM never designed for health data, then retrofit consent flags and data-handling rules once compliance raises concerns. That retrofit is usually incomplete – consent status doesn't sync reliably across tools, and it's unclear which fields count as PHI under your specific data-sharing agreements. This becomes a real liability the moment a security review, a BAA negotiation, or an audit asks a specific question about where health data lives and who can access it, and the honest answer is 'we're not entirely sure.'
How We Support You
We begin with a comprehensive audit of your existing data architecture – every system containing a contact record, how those systems connect (or don't), and where duplication and drift occur.
Next, we design the identity resolution and segmentation strategy – one source of truth for who a contact is, based on a defined hierarchy of source systems, along with a segmentation model driven by behavioral and engagement signals rather than static intake fields alone.
For execution, we rebuild or reconfigure the CRM platform itself – establishing deduplication rules, sync logic across systems, and a documented data dictionary defining what each field means and where it comes from.
We also manage the consent and PHI governance layer directly – consistent syncing of consent status across every connected tool, clear labels for PHI-adjacent fields, and access controls aligned with what your compliance team actually must demonstrate during an audit.
For measurement, we treat data quality as a metric – duplicate rate, field completeness, and sync latency across systems – alongside the downstream campaign results that rely on it.
What sets this apart from a typical CRM implementation vendor is that we build around the specific realities of health data – PHI boundaries, multi-system identity resolution, and consent complexity – instead of installing a generic marketing CRM template and leaving compliance to identify issues after launch.
What we deliver
- Data architecture audit – full map of every system holding contact records, where duplication and drift are occurring, and which fields are PHI-adjacent
- Identity resolution model – a defined source-of-truth hierarchy that merges fragmented contact records into one accurate profile
- Behavioral segmentation framework – product usage and engagement data piped into the CRM so segments reflect real behavior, not static intake fields
- Reusable automation architecture – workflow components built once and reused across campaigns instead of custom-built each time
- Consent and PHI governance layer – consistent consent-status sync, field-level PHI tagging, and access controls built with your compliance team
- Data quality reporting – duplicate rate, field completeness, and sync latency tracked as ongoing infrastructure health metrics
Every digital health company eventually notices its lifecycle emails are underperforming and points to the copy. Nine times out of ten, the copy is fine – the segment receiving it is wrong because the CRM beneath it never resolved who's actually who.
Our Methodology
We deliver CRM and lifecycle infrastructure work through a 90-day sprint. The first 30 days focus on audit and architecture: we map every system containing contact data, identify which fields are PHI-adjacent, and design the identity resolution and segmentation model with your compliance stakeholders involved from day one. Days 30 through 60 focus on building: we implement deduplication and sync logic, feed behavioral data into the CRM, and establish the reusable automation architecture that future campaigns will use. Days 60 through 90 cover validation and handoff: we test the consent and PHI governance layer against actual audit questions, set up data quality reporting, and document the system so your team can manage and expand it independently.
This is infrastructure work rather than campaign work, and we approach it accordingly – the sprint delivers a system, not a collection of emails. Unlike a generic CRM implementation vendor, we treat the PHI and consent governance layer as a core part of the architecture instead of a compliance box checked at the end, because in digital health that governance layer defines what the CRM is permitted to do with the data it stores.
Most engagements extend beyond the first 90 days in an advisory role as your team uses the new system and runs into edge cases – a new data source requiring integration, a new consent obligation from a partner agreement, or a new segment needing behavioral signals that are not yet connected. We remain involved throughout that stabilization period rather than delivering documentation and disappearing.
Our Working Approach
The first 30 days focus on audit and architecture design. We map every system containing contact data, measure the duplication and drift problem, classify PHI-adjacent fields alongside your compliance team, and design the identity resolution and segmentation model before changing any live system. You receive a clear view of precisely how much campaign underperformance comes from data quality versus messaging.
Days 30 through 60 focus on building. We implement deduplication logic, connect behavioral data sources currently isolated from the CRM, and create the reusable automation architecture. We need CRM admin access from your team, engineering help with any API integrations, and a compliance contact who can confirm the PHI classification throughout the process rather than reviewing it only at the end.
Days 60 through 90 cover validation and handoff. We stress-test the consent and governance layer using realistic audit scenarios, launch the data quality reporting, and guide your team through operating the new architecture. Initial engagements generally last three to four months because of the integration and validation work required, with many clients retaining us in an advisory capacity as new data sources or campaigns are introduced.
Our cadence includes a weekly working session with your marketing ops and compliance stakeholders together, because decisions in this work regularly affect both. Async updates monitor integration progress and data quality metrics between sessions.
If your digital health company needs lifecycle & crm leadership, we should talk.
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Frequently asked questions
What does a CRM and lifecycle infrastructure engagement cost?
Pricing varies based on the number of systems requiring integration, the complexity of your PHI and consent requirements, and whether we're rebuilding the CRM platform or reconfiguring an existing platform. A single-platform cleanup involving straightforward data sources costs less than a multi-system identity resolution project covering a clinical record system, billing, and a member app.
How soon will we benefit from fixing our CRM data architecture?
Data quality gains – including duplicate reduction and accurate segmentation – become visible within the first 60 days as identity resolution and sync logic are activated. Improvements in downstream campaign performance typically emerge over the next one to two send cycles, once lifecycle marketing runs against clean, accurate segments rather than fragmented ones.
How does your team collaborate with our existing marketing ops and compliance staff?
We collaborate directly with the person managing your CRM platform day-to-day and with your compliance or security team on the PHI and consent governance layer – both must participate from the beginning rather than being included at the end. We require CRM admin access, visibility into your BAAs and data-sharing agreements, and engineering assistance for any system integrations involving API work.
How is Winston Francois different from a typical CRM implementation vendor?
A standard CRM vendor rolls out a generic marketing automation template and treats compliance as a separate matter for your legal team to address after launch. We embed PHI classification, consent governance, and multi-system identity resolution directly into the architecture because, in digital health, those constraints establish what the CRM can legally do with the data before any campaign is built.
How is ROI measured for a CRM and lifecycle infrastructure engagement?
We measure data quality directly – duplicate rate, field completeness, and sync latency across systems – as the main infrastructure metric because that is what this engagement directly controls. We also track downstream impact: segment-sizing accuracy compared with previously fragmented data, plus the reduction in manual data-cleanup hours your team had been absorbing.
Which type of digital health company is the best fit for this service?
This service is designed for digital health companies in the $5M-$100M ARR range operating multiple systems – a clinical or care-delivery platform, billing system, and member-facing app – that have never been formally consolidated into a single contact record. It's a strong fit if your team already believes segmentation is inaccurate or a compliance review has identified unclear PHI handling within your marketing stack.
