Most people don't trust reviews they read online, especially on Amazon.com where fake reviews run rampant. This is especially prevalent on reviews for women's fitness apparel since size and fit can vary so much.
• Just 32% of consumers trust reviews that they read online.
• 10 information sources is the average # of sites-clicked before users make a purchase.
• 92% of consumers read reviews before making a purchase.
Design a review platform where people trust all reviews unconditionally through a verification process. Collect the measurements of users so that better clothing options get presented based on review data.
Wireframes Round 1
The initial strategy for collecting measurement data was through an onboarding process. I had a hypothesis that people would be dissuaded from using the app if it asked for personal details too quickly.
I created a prototype and put it in front of people at a UX meetup, which confirmed my theory. Users expressed discomfort when presented with these options during the onboarding process.
Wireframes Round 2
I moved the question about asking for measurements to the review process since it would make more sense to ask for the information in this context. Toggling the visibility of this info in the review would reassure users who are skeptical about sharing their measurements, but still allow us to collect the data.
If users can't find what they're seeking on the first or second click, there's a high risk of abandonment. My recommendation was to partner with a local retailer and acquire purchasing data to email direct review links to customers. By offering a 15% discount in exchange for a review, it would generate a faster userbase and give the platform more credibility.
Despite direct links being the primary method of engagement, there would need to be a way to search/filter for products. The survey data helped guide the IA for the most desirable qualities sought in women's fitness apparel.