CUROLOGY
Designing a routine-building experience that helped patients understand and choose non-RX products alongside their prescription
+7% routine completion | +20% non-RX products added | -3% customization drop-off
OVERVIEW
Product
Curology, a dermatology telehealth platform providing accessible prescription skincare for acne and other skin concerns.
Project
Curology had recently launched non-RX products. Patients now had to decide whether to add these products alongside the prescription their provider had created for them. Patients weren't required to add them, but the business wanted patients purchasing through Curology rather than sourcing similar products elsewhere.
Role
Product Designer: Led end-to-end design process from research to final launch
Team
PM, Growth, Content Design, Legal, Engineering, and Research
Timeline
2 months
THE PROBLEM
New patients were being introduced to an entire product category, non-RX skincare, that many didn't even know Curology offered, on top of the prescription their provider had just created for them. The business wanted patients choosing Curology's non-RX products over sourcing similar products elsewhere, but patients needed to be able to opt in freely, not feel pushed.
My early assumption was that this was an information overload problem: too many new products, introduced too suddenly. Research showed the real problem went deeper than that.
RESEARCH
Understanding Our Patients
In partnership with a User Research Manager, I conducted five remote moderated interviews and usability sessions with patients to understand how they navigated the routine-building experience. Four themes emerged.
Theme 1: Product Understanding
"I'm still a little unfamiliar with skincare ingredients."
"I don't always know what each product actually does for my skin."
Theme 2: Trust & Credibility
"Were these actually recommended by my provider?"
"How do I know these recommendations are right for my skin?"
Theme 3: Pricing Transparency
"I wasn't sure what I would actually be charged."
"I kept looking for my final total before making decisions."
Theme 4: Decision Fatigue
"I would like more guidance when building routines because it feels a little overwhelming."
"There are a lot of choices to make."
Two of these mattered more than I expected, together. Trust & Credibility (was this really from my provider?) and Decision Fatigue (why do I need this at all if my provider already gave me a prescription?) compounded each other.
Layered together, they meant the real problem wasn't just "make this trustworthy." It was "explain why this exists at all," and do both without feeling pushy about a category the business needed patients to adopt.
CHALLENGES
Challenge #1: Building trust without overstating provider involvement
Providers and Legal pushed back on framing non-RX suggestions as provider-recommended, since these products hadn't gone through the same individualized clinical review as the prescription itself.
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"Provider recommended" language: We tested this against the alternative, and it performed better with patients, it directly answered their skepticism. But Legal and Clinical asked us to scrap it, since it implied a level of individual provider review that hadn't actually happened.
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Skin-concern-based framing, tied to intake data: Ties the suggestion to something true, the patient's own intake answers and treatment goals, rather than implying individualized provider review.
My call: Ship the lower-performing but accurate version. Skin-concern framing tested slightly worse, but it was the version Legal and providers could actually stand behind, and I wasn't willing to ship language that outperformed on a claim we couldn't back up.
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Ruled out: Implied a level of individual provider review that hadn't actually happened.
Challenge #2: Personalizing suggestions without a full recommendation engine
Patients wanted non-RX suggestions to feel personalized, the same way their prescription formula was. Engineering flagged true formula-level matching as too complex for the timeline.
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Dynamic, formula-matched bundling: Would have mirrored how the prescription itself was personalized. Ruled out as too large a build for the available timeline.
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Rule-based tagging using existing intake data: Reuses skin-type and concern tags patients already provided at intake. Far lower engineering lift.
My Call: Ship the tag-based version for v1, and treat true dynamic matching as a future investment once the simpler version validated the concept.
DESIGN SOLUTIONS
Both challenges pointed to the same principle: build trust and personalization on what was actually true, not on implied endorsements or systems we didn't have time to build. That shaped three shipped changes:
"Matched to Your Skin" Recommendations
Non-RX suggestions reframed around skin concerns and treatment goals, not implied individual provider endorsement.
Ingredient Education
Lightweight, inline education addressing the Product Understanding theme, without interrupting the customization flow.
Transparent Pricing
Pricing surfaced intentionally throughout customization, addressing the Pricing Transparency theme.
IMPACT
Reframing recommendations around real skin data, rather than an unverified provider endorsement, gave patients a reason to trust the experience without overstating what had actually happened clinically. Combined with clearer ingredient education and upfront pricing, customization became something patients could move through with confidence instead of second-guessing.
+7%
Routine completion
More patients completed the customization flow
+20%
Non-RX products added
More patients added non-RX products to their routine
-3%
Customization drop-off
Fewer patients abandoned the experience
REFLECTION
The hardest decision on this project came down to a single moment: "provider recommended" language tested better with patients, but Legal and Clinical asked us to scrap it since it implied a level of provider review that hadn't actually happened. I shipped the lower-performing version anyway, because it was the one we could stand behind. That decision was the sharpest version of a tension that ran through the whole project: what the business needed versus what patient trust required. I chose accuracy over what tested best, on the reasoning that an unverified claim would cost more long-term than a slightly lower conversion number would short-term.
This also reinforced how important it was to understand the actual problem before committing to a solution. Patients weren't simply asking for more information, they wanted information that felt relevant and connected to their actual treatment. Looking back, one thing I'd want to revisit is the "why do I need this at all" question research surfaced. We addressed the trust half of the problem, but never directly tackled the necessity half, why a patient with an already-personalized prescription should still consider non-RX products. That's the natural next problem to solve.