A digital skincare app powered by machine learning to generate a personalized selection of product recommendations. The founders discovered an opportunity to offer meaningful skin-data insights & recommendations by crowdsourcing community posts. Myself and 4 other undergraduate students were invited to redesign their existing Android application to improve the usability.
In making these changes, our product people hoped to further revolutionize the way users think about skin care and encourage a more active community to crowdsource skin care data from
When we reviewed the low-fidelity items, our stakeholders wanted the team to emphasize the social media posting flow of the interactions more specifically because their machine-learning model relied on that behavior to source information. With that feedback in mind, we created wireframes to flesh out the interaction patterns with more detail.
The first 5 participants we tested with struggled to understand how social media posting fit into skin care health and product reviews. Our team pivoted and created paper prototypes to reframe the concept of social engagement around blogging and journaling.
We used the paper prototypes to get feedback from 3 more participants who validated the changes and were able to better describe the model of blogging as a means to document their skin care journey.
Laid the product foundation for our stakeholders’ startup app which launched in the Android store in late 2019. They are still in the process of recruiting beta testers to further improve the design and concept. Learn more about their product here.