UX Designer
Feb–Jun 2022
What I did
User Research
Information Architecture
Visual Design
In 2004, Yelp, a crowd-sourced business review platform, was founded and quickly rose to popularity. But with that popularity came controversy regarding the legitimacy and ethics of their reviews. Reviews are unsolicited. Therefore, just about anyone can create an account and leave a review on any business listed on Yelp. So I took it upon myself to design an improved reviews section system to instill more trust in Yelp reviews.
My solution had a 100% task completion rate while users expressed that overall the reviews felt more trustworthy.
Research & Understanding
User pains
Yelp users expressed that business pages were difficult to navigate and that finding the information they were looking for was not always easy. As a result, it lowers user's level of trust in the brand and they opt to use a competitor app, leading to lost revenue.
With that, I decided that there was a business opportunity in improving business pages to drive consumers to make a purchase from a business faster and easier.
User interviews
To ensure that my problem was on the right track, I conducted 5 user interviews with users that have used Yelp in the past month. To my surprise, my problem was wrong.
While most users agreed that the navigation and hierarchy of business pages could use work, the bigger issue was feelings of distrust in the reviews.
Reframing the problem
So back to Figma I went to reframe my design question to...
How might we improve the reviews section to instill more trust in the reviews?​​​​​​​
Story map
• The navigation tabs allows users to find the information they're looking for 2 times faster.
• 5/5 users expressed that they liked the sub-rating score system.
• The prototype had a 100% task completion rate while users expressed that overall the reviews felt more trustworthy.
What I would have done differently
If I had more time, I would have conducted another round of usability tests to dive deeper into user's thoughts on the filter by user feature. I am interested to see if I could uncover new insights around the idea of filtering users.