How might we help members easily book therapy sessions while boosting booking rates?
Lyra is a comprehensive telehealth platform designed for working professionals.
Some of our clients include Meta, Google, Starbucks and Walmart. It’s a known problem
that there aren’t enough licensed therapists in America to treat those in need.
Some of our clients include Meta, Google, Starbucks and Walmart. It’s a known problem
that there aren’t enough licensed therapists in America to treat those in need.
Members were having difficulty choosing a therapist and committing to booking sessions while conversion rates for therapists had decreased by 6% in a month.
How might we help members easily book sessions while boosting booking conversion rates?
I led the design efforts for a complete rehaul of the therapist recommendation page. Instead of treating therapist recommendation like search results, I wanted it to feel like a truly personalized curation. This northstar design focuses on showing one therapist at a time. It also shows more information per therapist to help members learn more about each therapist to help them chooose a therapist and commit to therapy.
Mobile design for the updated therapist recommendation page. I drew inspiration from dating apps as there are many parallels for getting to know a therapist to getting to know someone in dating. The intention was to bring focus on one therapist at a time.
I also designed email campaigns and collaborated closely with a product manager to determnine which user groups to target for these emails to promote therapists even outside of the Lyra platform.
Email campaigns along with the design updates to the therapist recommendation page would aim to increase conversion rates again and help members commit to care by choosing a therapist.
Given our constraints, we had to work backwards from our northstar designs. This was the V1 iteration where we put emphasis on the top recommended therapist backed up by our ML algorithm base on the data that users gave us.
Design for the top recommendation that Lyra would present to members. This would be determined by our ML algorithm that took both qualitative and quantitative data into account to recommend the best fit for our members.
Our team conducted resesarch to determine what signals members were missing to choose a therapist. Members wanted more signal around a therapists’ personality, expertise and availability.
There was an overall 15% to 30% rate in improvement with the new design efforts, fine tuning the ML algorithm and email campaigns.
A message from the CEO on the updated designs (: