Curb.Health is an AI-driven SaaS platform using Just-in-Time Adaptive Interventions (JITAI) to help individuals, clinicians, and employers reduce alcohol-related harm.
Ahead of launch, Curb.Health deployed a static waitlist landing page to validate demand and build an early adopter base.
- Visitors could not articulate what the product did or who it was for.
- High bounce rates on key informational sections.
- Negligible waitlist conversion.
The core problem: The experience failed to communicate differentiated value across three distinct B2B/B2C segments.
This wasn’t just a UI issue—it was a failure of product positioning that threatened the company’s ability to seed its machine-learning model with real user data.
Goal: Redesign the marketing site and onboarding flow to drive a measurable increase in waitlist sign-ups, while educating users on a complex AI value proposition.
Primary: Waitlist conversion rate (sign-ups / unique visitors).
Secondary: Engagement depth (scroll depth, time on page).
Tertiary: Navigation completion rate (finding segment-specific info).
Constraint: 3-week sprint from discovery to high-fidelity delivery, including sensitive user recruitment.
I led end-to-end UX strategy, embedding continuous QA at every stage to de-risk decisions and ensure the solution was both ethical and commercially viable.
Actions:
Stakeholder alignment: Conducted workshops with founders and clinicians to define personas, map the “leaky” funnel, and lock KPIs.
Heuristic audit & analytics review: Identified that 78% of users never scrolled past the hero; CTAs were generic (“Learn More”).
Competitor benchmarking: Analysed onboarding patterns from Calm, Headspace, and Omada Health to benchmark trust signals and value prop delivery.
Bias-mitigated user research: Recruited outside the client’s network to avoid skewed feedback. Conducted n=12 interviews across all three segments.
“Have we validated the problem with unbiased data?”
Outcome: Triangulated qualitative themes with analytics. Pivoted from “addiction” to “cravings” language based on user sentiment analysis.
Actions:
Card sorting & IA redesign: Created a hub structure allowing users to self-select as Individual, Clinician, or Employer.
A/B Prototype Testing (n=18):
- Prototype A: Refined version of original layout.
- Prototype B: Persona-first navigation with tailored outcomes (e.g., “Reduce absenteeism” for employers).
Moderated usability tests: Measured task success and time-to-comprehension.
“Does Prototype B outperform A on comprehension and intent?”
Outcome: Prototype B achieved 2x faster comprehension of product purpose. Users reported higher psychological safety due to confidentiality microcopy.
The work followed a Design Thinking process anchored in the Double Diamond framework, moving from discovery to delivery with clear checkpoints.
Actions:
High-fidelity UI design: Simplified onboarding to a single, persistent CTA (“Join the Waitlist”).
UX Writing & Trust layering: Added 24/7 support badges, psychiatrist accreditation, and plain-English AI explanations.
Responsive QA: Cross-device testing to ensure frictionless mobile experience (predicted traffic source).
“Is the flow technically feasible and legally compliant?”
Outcome: Collaborated with engineering to ensure tracking parameters were in place; legal sign-off obtained on data-privacy copy.
The redesigned experience launched on schedule. Within 30 days, the platform demonstrated statistically significant uplifts across all key SaaS metrics:
Exceeded growth target; provided critical user base for ML model training.
Users actively sought segment-specific information.
Users successfully found content relevant to their role.
Increased dwell time and exploration.
- De-risked launch: The client moved from “uncertain value prop” to a validated onboarding narrative.
- Investor confidence: The uplift in conversion was referenced in subsequent fundraising discussions.
- Foundation for iteration: Established a UX benchmark and analytics baseline for future growth experiments.
This project was not simply a reskin. My role required navigating ambiguity, commercial constraints, and ethical complexity:
Product Strategy: Converted a vague brief into a testable hypothesis and KPI framework within 48 hours.
Ethical Research Leadership: Designed a trauma-informed recruitment strategy to avoid bias and protect vulnerable participants.
Cross-functional Influence: Negotiated with clinical stakeholders to adopt non-stigmatising language; partnered with engineering to ensure tracking fidelity.
Commercial Acumen: Framed design decisions in terms of CAC reduction and activation lift, not just aesthetic improvement.
The final solution positioned Curb.Health clearly as an AI‑powered health SaaS platform that delivers:
Onboarding is a product feature, not a marketing asset. It directly impacts activation, data quality, and retention. Treat it with the same rigour as core features.
Validation is continuous, not a milestone. By embedding QA checkpoints (problem fit, solution fit, technical feasibility), we never wasted a sprint cycle.
Language is a conversion lever. In sensitive contexts, destigmatising vocabulary is not “soft” UX—it is a direct driver of conversion and trust.
Speed requires structure. A 3-week sprint is only possible when research is sharply scoped and decisions are tied to measurable outcomes.
This project transformed Curb.Health’s onboarding from a source of user confusion into a high-performing SaaS acquisition layer. By treating the waitlist as a conversion funnel rather than a holding page, we delivered a 40% uplift in sign-ups and gave the business a validated narrative to scale.
More importantly, it demonstrated that in AI-driven healthcare, trust is the conversion metric. When users understand how the machine learning helps them—and feel safe engaging with it—they convert. That is the standard for senior UX leadership in 2024.
✦ Want to talk about how I can bring this level of commercial UX impact to your team? ✦
