I led the redesign and optimization of Ideal Image’s self-scheduling booking flow to reduce friction and improve conversion across a complex, multi-service, multi-location business. With hundreds of clinics, varying treatment pathways, and both new and returning clients entering the funnel, the existing booking experience created unnecessary drop-off. The challenge was to simplify decision-making while preserving the clinical accuracy, eligibility requirements, and location-specific availability needed to safely schedule treatments.

To solve this, the booking flow was re-architected around clarity, progressive disclosure, and intent-based pathways. The experience guided users through treatment selection, eligibility cues, location matching, and appointment availability in a logical, step-by-step sequence. We introduced cleaner UI patterns, clearer language, and smarter defaults to reduce cognitive load, while ensuring the flow worked seamlessly across devices and supported both first-time consultations and repeat treatments. The result was a scalable framework that balanced consumer ease with operational constraints at the clinic level.

Following implementation, the self-scheduling flow drove measurable improvements in engagement and conversion, increasing the percentage of visitors who successfully booked appointments without assisted support. The new framework established a foundation for continued experimentation, personalization, and optimization—allowing Ideal Image to better capture high-intent demand, reduce dependency on call centers, and give clients a faster, more confident path from discovery to appointment.