Digital health companies adopt A/B testing playbooks designed for consumer apps with millions of daily users, then wonder why each test either needs six months to reach significance or gets decided on noise. Patient and provider populations are smaller, sales cycles take longer, and every idea passes through legal review before launch. Effective growth experimentation in this vertical requires a prioritization framework, a statistical approach designed for smaller samples, and a compliance process that preserves velocity. We build and operate that program.
The Challenge
Traditional A/B testing math doesn't work for a patient population in the low thousands
Most experimentation frameworks assume traffic volumes that let you hit statistical significance in days. A digital health product with 8,000 active patients and a 12% baseline conversion rate on a key action needs months to reach a reliable read on anything but the largest effect sizes, using fixed-horizon testing. Teams that don't adjust for this either wait so long that the test outlives its relevance, or they call winners early on noise and ship changes that quietly hurt retention. The fix isn't more traffic – it's a different statistical approach suited to the sample size you actually have.
Every test idea is gated by legal and compliance review, bringing velocity close to zero
In digital health, a change to a consent flow, a medication reminder, or anything touching PHI has to clear compliance before it ships, and that review typically happens one test at a time, from scratch, with no reusable framework. By the time an experiment clears review, the product context that motivated it has often changed, and the team has run one test in the time a comparable SaaS company ran ten. Growth experimentation without a compliance-aware process isn't slow because the ideas are bad – it's slow because every idea restarts the review clock.
Tests are prioritized by the loudest voice in the roadmap meeting rather than expected impact
Without a scoring framework, experiment backlogs fill with whatever the CEO saw a competitor do, whatever sales asked for last week, and whatever a PM is personally attached to. Scarce test slots – and in digital health they are genuinely scarce, given the sample size and compliance constraints above – get burned on low-impact ideas while higher-leverage hypotheses about activation or retention sit unrun. The cost of a bad prioritization call is higher here than in high-traffic SaaS, because you don't get to run five more tests next week to make up for it.
Every test is a one-off—nothing compounds, and the same questions are debated again each quarter
Without a shared learnings repository, a test result lives in one person's memory or a forgotten Slack thread, and the next PM who has a similar idea reruns a version of the same experiment because nobody could find the prior result. In a vertical where every test is expensive to run, failing to compound learnings is a direct cost – it means paying the same statistical and compliance overhead twice for information you already had.
How We Can Help
We begin by evaluating your current testing pace and win rate – how many tests ran over the past two quarters, how long each one took from idea to readout, and what share delivered a clear result rather than an inconclusive one.
We then create a hypothesis backlog and score it using a framework weighted around your constraints – expected impact on the metric that matters, confidence grounded in existing evidence, and effort that includes the compliance review lift, not only engineering time.
Since sample size is the binding constraint for most digital health products, we tailor the statistical approach accordingly – using sequential testing or Bayesian methods when fixed-horizon significance testing would take too long, along with guardrail metrics that prevent a test from quietly harming retention or, where relevant, a clinical or safety-adjacent outcome while it remains live.
For compliance, we create a library of pre-cleared test templates – patterns and change types that legal and compliance approve once, allowing individual tests within those patterns to move through review in days rather than weeks. Compliance remains part of the process; what disappears is the need to restart review from scratch each time.
For execution, we operate the testing program in sprints from the prioritized backlog, partnering with your <a href="/services/product/">product</a> and engineering teams to launch variants, watch guardrails, and call results with the statistical approach suited to your sample size.
Unlike a generic CRO or testing agency, we don't bring in a SaaS testing playbook and simply hope it holds up against your patient population size and compliance requirements.
What we deliver
- Prioritized hypothesis backlog scored on expected impact, confidence, and total effort including compliance lift
- Statistical testing framework fit for smaller patient or provider populations – sequential or Bayesian methods and guardrail metrics
- Library of compliance-pre-cleared test templates that cut per-test legal review time
- Test execution sprints run jointly with your product and engineering teams
- Shared learnings repository logging every test result and its implications for the next round of hypotheses
In digital health, experimentation is almost never constrained by traffic—it's constrained by compliance velocity and the discipline to apply the statistical method suited to your actual sample size. A team running eight well-prioritized, properly analyzed tests per year will learn more than one running thirty tests it cannot legitimately call.
Our Methodology
We deliver growth experimentation through a 90-day sprint that establishes an ongoing program. The first 30 days set the baseline and backlog: we audit prior test velocity and win rate, create and score the hypothesis backlog, and design a statistical framework suited to your actual population size. Days 30 through 60 establish the compliance and execution layer: pre-cleared test templates, guardrail metrics, and the first two or three tests advancing through the pipeline. Days 60 through 90 demonstrate how the program compounds: we complete the first full readout cycle, record results in the learnings repository, and use them to re-prioritize the following quarter's backlog.
Unlike a generic CRO agency, we don't view sample size as an inconvenience solved by extending timelines – we design the statistical method around it from the outset and integrate the compliance relationship into the process rather than treating legal review as an outside blocker. We judge the program on velocity (tests reaching a clean result each quarter) and win rate versus your pre-engagement baseline, not the total number of tests launched.
Our Process
The opening 30 days focus on diagnosis and structure. We examine your previous several quarters of testing – what ran, how long each test took, and how many generated an actionable result – then create the scored hypothesis backlog and select a statistical method appropriate for your population size. By the end of this phase, you have a candid view of your true testing velocity and a ranked list of what should run next.
Days 30 through 60 establish the machinery: test templates pre-cleared for compliance and developed with your legal or compliance function, guardrail metrics set with product and clinical stakeholders where relevant, and the first tests moving into the pipeline. We need access to product analytics, an established relationship with the person responsible for compliance sign-off, and engineering capacity to ship variants.
Days 60 through 90 complete the first full cycle – tests go live, guardrails are monitored, and results are called with the statistical method selected for your sample size rather than a generic significance calculator. We document results in the learnings repository and use them to re-rank the next quarter's backlog in real time, allowing the program to compound before the sprint ends.
We hold a weekly test-review session with product, engineering, and compliance stakeholders, supported by async readouts when tests finish. Most engagements extend beyond the initial 90 days into an ongoing testing program, typically lasting 3-6 months for the first complete build, with the option to continue into ongoing operation.
- Higher test velocity – more tests reaching a clean, actionable result per quarter
- Fewer false positives from significance calls that ignored real sample size
- Faster compliance review through reusable, pre-cleared test patterns
- A compounding learnings repository that shortens future hypothesis development
If your digital health company needs growth experimentation leadership, we should talk.
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Frequently asked questions
What does a growth experimentation program cost for a digital health company?
We offer the initial 90-day build and first program cycle as a fixed-price engagement, generally $20K-$45K based on the number of product surfaces and user segments included and the amount of upfront compliance-template work required. Continuing program operation after the initial cycle is typically offered at a lower monthly retainer.
How quickly will we get results from a growth experimentation engagement?
The hypothesis backlog and statistical framework are generally complete by day 30, with the first tests entering the pipeline around day 45-60 after compliance templates receive clearance. Because sample sizes are smaller, individual test readouts may still require several weeks to produce a reliable call through sequential or Bayesian methods – that's expected, and it's precisely why we design the framework for your real population rather than forcing a quicker but less reliable answer.
How will the experimentation team collaborate with our current product and engineering staff?
We manage the prioritization and statistical framework, while your product and engineering teams are needed to ship variants, and access to your compliance function is required to call results. Rather than introducing a separate process, we integrate with your existing sprint cadence, and the learnings repository is built for direct use by your PMs – not only by us.
How is Winston Francois different from a conventional CRO or testing agency?
A typical CRO agency often uses a fixed-horizon significance framework designed for high-traffic consumer products while treating compliance as another team's concern. We shape the statistical method around the actual size of your patient or provider population and incorporate the compliance relationship from day one, preventing tests from stalling while they await an unplanned review.
How is ROI measured for a growth experimentation program?
We measure program-level velocity – tests that achieve a clean, actionable result each quarter – and compare win rate with your pre-engagement baseline instead of counting raw test volume. We also measure the average time between hypothesis and compliance clearance, because that is usually the largest available lever in digital health testing programs.
Which type of digital health company is best suited to this service?
This service suits companies with $5M-$100M in ARR, an established product, and sufficient patient or provider volume for meaningful testing, even when that volume is well below consumer SaaS scale. It is particularly well suited to teams that have previously run tests only to receive inconclusive or conflicting results.
