Sesame: Revenue Through Cognitive Load Reduction

The Real Problem

In the United States, quality healthcare remains inaccessible for millions. High costs, opaque pricing structures, and complex insurance systems deter people from seeking medical care, even with serious symptoms. Sesame had established product-market fit with a direct-to-consumer healthcare marketplace, but faced challenges scaling adoption and optimising the conversion funnel. The system was organised by provider types, but patients don't think in provider types. They think in symptoms.

• 400,000 users were searching by symptoms while the system was organised by provider specialties
• Decision paralysis at checkout, too many choices with not enough guidance
• Users dropping off at the provider selection stage because they couldn't evaluate options meaningfully
• No quality indicators or trust signals to help users make confident decisions

Data-Psychology Integration

I analysed search patterns across 400,000 users and discovered the fundamental disconnect: patients primarily searched for symptoms ('my knee hurts') while our system was organised by provider types ('orthopaedic surgeon'). This wasn't a UI problem, it was an information architecture problem rooted in the gap between patient mental models and medical taxonomy. I identified decision paralysis as the key conversion barrier and designed guided frameworks that reduced cognitive load at critical decision points.

The Solution

I redesigned the core user journey using Experience Flow, starting from the moment a user realises they have a health concern, not from the moment they land on the website.

• Symptom-based entry points, translating patient language into provider matches
• Guided decision framework reducing choice overload through progressive filtering
• Contextual provider cards surfacing relevant credentials per symptom
• Strategic quality indicators and social proof building trust at decision moments
• Streamlined checkout reducing steps from 7 to 4

Validation at Scale

I conducted 50+ unmoderated testing sessions with users across diverse demographics, validating design hypotheses and uncovering critical gaps between patient needs and system organisation. I developed a cross-platform consistency framework maintaining information hierarchy across iOS, Android, and web while adapting to platform-specific interaction patterns, building trust through familiarity in a domain where trust is paramount.

Working Across Time Zones

Working remotely between Berlin and New York, I developed asynchronous communication protocols and comprehensive documentation that maintained momentum despite significant time zone challenges. I collaborated with stakeholders to deliver a user-researched strategy and designed MVP features within 2.5 weeks, demonstrating rapid delivery capability within strict business requirements.

Results

The redesigned experience delivered meaningful impact, small conversion improvements that translated into thousands more people receiving healthcare they otherwise wouldn't have accessed.

• 27% improvement in search success rate through symptom-based entry points
• 3% checkout conversion increase overall (4.2% on mobile)
• 22% quarterly revenue increase through improved user experience
• 18% expansion of service availability across US markets
• 23% of converted users reported they had previously delayed care due to cost
• 50+ unmoderated testing sessions validating hypotheses across diverse demographics

What I Learned

Small conversion improvements (3%) have massive real-world impact when applied to essential services like healthcare. Post-appointment surveys revealed that 23% of converted users had previously delayed care due to cost concerns, meaning our design improvements directly helped thousands of people receive medical attention they might never have sought. The gap between patient language and medical taxonomy is where most healthcare UX fails. Meeting users in their language rather than asking them to learn yours is the entire point of Experience Flow.