We knew people were confused by multiple pathways. Wanted to click directly on a tutor, but getting matched was better.
Current design prior to this project to the right.
Looked at a variety of minified search inspiration to try and design a more lightweight approach for users to get matched. Airbnb's old model of search was a key inspiration.
Started out the process sketching, exploring how we could make search feel more friendly and get more information from the students in one step, making it feel more lightweight than the current process of doing multiple steps.
Focused on whole page at first. Concept to have more interactive way of engaging with tutors and entice users to use the search bar.
Also explored having a more robust how the service works to educate up front.
Students liked the minified search bar, which they said was more enticing than the cards.
Explored different ways to explain how the service works before the search bar in the header area.
Project got de-scoped to just redesign header area and have that contain improved search and how it works info to make a cleaner A/B test with the current version.
This version explored tutor faces in the background to have users see the value prop of tutors right off the bat.
Learnings from launch
There was a drop in Opportunity rates. The data science team found that this was due to:
- Min. character count for description was not exposed
- Subtopic was unclear in live test (although tested well in UER)
- Plan was to launch again without subtopic and fixing all error states