Most growth experimentation frameworks assume unlimited traffic, unlimited landing page variants, and a legal team that isn't in the room. Biotech and pharma have none of that. We build test programs around the constraints you actually have: a handful of HCP and patient touchpoints, an MLR queue, and audiences too small for standard statistical significance.
The Problem
You have five testable surfaces, not fifty
A consumer SaaS company can run dozens of concurrent landing page and ad variants because it has dozens of channels feeding traffic. A biotech or pharma brand usually has one HCP portal, one patient site, a handful of paid search terms cleared for use, and maybe a rep-facing leave-behind. There is no long tail of pages to test against. Every experiment has to be worth the cost of running it on a scarce surface.
MLR turns a two-day test cycle into a six-week one
In consumer marketing you write a headline, ship it, and read the results by Friday. In biotech and pharma, every claim, every comparative statement, every implied outcome goes through Medical-Legal-Review before it can appear anywhere. A test plan that assumes rapid iteration collapses the moment MLR turnaround eats the timeline. Most growth teams don't build the review cycle into the test design and end up with a backlog of approved copy that never gets tested against anything.
Your audience is too small for the math to work
Standard A/B testing needs sample sizes that a rare disease population or a specialist physician list will never produce. If you're marketing to 400 rheumatologists nationwide, you will not hit statistical significance on a button color test, ever. Teams that don't know this run tests for months, get inconclusive results, and either give up on experimentation entirely or ship changes based on noise.
Pre-launch and post-approval are different games with different rules
Awareness-stage campaigns before an indication is approved operate under disease-state education rules, no product mention, no efficacy claims. Post-approval commercial campaigns operate under a completely different claims framework with fair balance requirements. Agencies that built their process around one phase often keep running it after the phase changes, testing the wrong variable at the wrong time.
Generic CRO agencies apply playbooks that don't transfer
Conversion rate optimization agencies that made their name on e-commerce and SaaS funnels bring frameworks built for high-traffic, low-regulation environments. Multivariate testing, rapid headline swaps, aggressive personalization, most of it is either legally impossible or statistically meaningless in this vertical. The result is wasted budget on a testing program that produces reports nobody can act on.
How We Help
We start with an audit of your actual testable surface area: every HCP-facing page, patient-facing page, paid channel, and email sequence that currently exists or is planned, mapped against where each one sits in the regulatory review cycle.
From there we build a prioritized experiment backlog that ranks tests by two things: how much MLR-approved flexibility already exists in the copy, and how much business impact a directional finding would have.
Because your audiences are frequently too small for classical significance testing, we design experiments around directional and qualitative signal instead of forcing a p-value that will never arrive.
We also build the MLR calendar directly into the test roadmap. Instead of treating legal and medical review as a bottleneck that derails timing, we batch experiment variants into review submissions so that when approval comes back, we have three or four cleared options ready to test at once rather than one. This alone recovers months of lost testing time over a year.
Execution runs through your existing marketing operations, not a parallel system we bolt on. We work inside whatever CMS, email platform, or HCP portal you already use, and we hand off clean documentation so your internal team can run the next round of tests without us if that's the goal.
Measurement is built around what a directional read actually means for a small-N test: confidence intervals reported honestly, sample size caveats stated up front, and clear language distinguishing a promising signal from a proven result. We do not present a 40-person test as if it were a 40,000-person test.
What differentiates Winston Francois here is that we've built test programs for both pre-approval disease-state campaigns and post-approval commercial launches, and we know the claims boundaries shift between them. We are not a CRO shop that discovered pharma has compliance rules. We build the compliance constraint into the experiment design from day one.
What we deliver
- Testable surface area audit mapped against MLR status
- Prioritized experiment backlog ranked by regulatory flexibility and business impact
- Directional and qualitative test designs for small specialist and rare disease audiences
- Quantitative test designs for larger patient-facing awareness campaigns where sample size supports it
- MLR-synced experiment calendar batching variants into single review submissions
- HCP and patient message testing panels run pre-launch
- Honest measurement reporting with stated confidence levels and sample size caveats
If your test needs 400 rheumatologists to reach statistical significance and only 400 rheumatologists exist, you're not running an A/B test, you're running a decision, and it should be designed like one.
Our Methodology
We run growth experimentation in 90-day sprints, structured around the reality that biotech and pharma test cycles move at the speed of regulatory review, not the speed of a code deploy. Days 1 through 15 are the audit phase: mapping every testable surface, its current MLR status, and the audience size behind it, so we know upfront which experiments can run quantitatively and which need a directional design.
Days 16 through 45 are strategy and submission. We draft the experiment variants, route them through your MLR process in batches rather than one at a time, and build the measurement plan for each test before anything ships, including what sample size or panel size we actually need and what a meaningful directional signal looks like given that constraint. Days 46 through 90 are execution and read: tests go live on approved surfaces, HCP and patient panels give structured feedback where the audience is too small for a live split test, and we report results with the confidence level the data actually supports.
The difference from traditional consulting is that we don't hand you a strategy deck and leave. We stay through the MLR submission, the panel recruitment, the live test, and the read-out, because in this vertical the gap between "recommended test" and "test that actually ran" is usually where the whole program dies.
How We Work
The first 30 days are the audit and roadmap: we map your testable surfaces, current MLR turnaround times, and audience sizes across your HCP and patient channels, and come back with a ranked experiment backlog specific to your regulatory phase, pre-launch or post-approval. Days 31 through 60 move into building: drafting test variants, submitting batched MLR packages, and standing up whatever panel or cohort structure the audience size requires.
By day 60 we're running live tests on approved surfaces and collecting structured feedback from HCP or patient panels where a live split test isn't statistically viable. Day 90 is a full read-out: what moved, what was directional versus significant, and what goes into the next sprint's backlog.
Our team is small and senior on purpose, a strategist who understands the regulatory environment, an experiment designer who knows when to use qualitative panels versus quantitative splits, and an analyst who reports confidence levels honestly rather than dressing up a small sample as proof. We run a standing biweekly check-in during active sprints and an async Slack channel for anything that needs a faster answer than that.
If your biotech or pharma company needs growth experimentation that accounts for MLR timelines and small specialist audiences instead of ignoring them, we should talk.
- Faster time from MLR approval to live test through batched submission calendars
- Test designs that produce usable signal from small HCP and patient populations instead of inconclusive results
- Clear separation between pre-launch disease-state testing and post-approval commercial testing
- A prioritized, standing experiment backlog your team can keep running independently
- Honest measurement reporting that holds up under internal and legal scrutiny
If your biotech & pharma company needs growth experimentation leadership, we should talk.
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Frequently asked questions
How do you run A/B tests when our HCP audience is only a few hundred physicians?
In most cases we don't run a classical A/B test at that scale, because the sample size will never reach statistical significance. Instead we design structured message panels and sequential cohort comparisons that give you a directional read on which message performs better, reported with honest confidence caveats rather than a false sense of statistical proof.
Does everything we test have to go through Medical-Legal-Review first?
Any claim, comparative statement, or implied outcome does, yes. What we change is the process around it: instead of submitting one variant at a time and waiting on each response, we batch multiple test variants into a single MLR submission so you get several cleared options back at once, which recovers a significant amount of testing time over a quarter.
Can we test the same way before and after FDA approval?
No. Pre-approval disease-state awareness campaigns operate under different claims rules than post-approval commercial campaigns, and testing that treats them the same will either get flagged in review or waste effort testing variables that stop being relevant once the phase changes.
What is the difference between this and hiring a standard CRO agency?
Standard conversion rate optimization agencies bring frameworks built for high-traffic e-commerce and SaaS funnels, multivariate testing, rapid headline iteration, aggressive personalization, most of which is either legally restricted or statistically meaningless against a specialist physician population. We start from your actual testable surface area and audience size, then design the experiment method to match, rather than forcing a consumer playbook onto a regulated, low-traffic environment.
How much does a growth experimentation engagement cost?
Most 90-day sprints run $25K-$60K depending on the number of testable surfaces, the complexity of your MLR process, and whether we're standing up new HCP or patient panels from scratch. We'll give you an exact number after the initial audit, once we know what's actually testable within your current regulatory constraints.
Who on our team needs to be involved for this to work?
You need a point of contact on marketing who owns the test roadmap, someone with visibility into your MLR submission queue and timelines, and ideally a medical science liaison or sales lead who can relay field feedback for the qualitative testing components. We run the day-to-day execution, but those three relationships are what keep tests moving through review and into market.
