A personalized shopping assistant that helps repeat marketplace customers document their product and brand selections and aversions.
Repeat marketplace customers struggle to recall their product and brand selections/aversions in an oversaturated market.
Throughout the project, I relied heavily on the design thinking process, a methodology that aims to uncover user needs and preferences in order to create innovative and effective solutions. I broke down my approach into three main phases:
Research & key insights
To gain a deeper understanding of user behavior and preferences, I conducted ethnographic interviews, consumer shop-a-longs, focus groups, and external brainstorming sessions. By analyzing the resulting data, I was able to generate key insights and assumptions about the initial challenge at hand.
Based on the insights gathered in the first phase, I generated three core concepts for addressing the challenge. After receiving feedback from stakeholders, we decided to move forward with two of the concepts: a mobile app and a browser extension.
Prototyping & testing
To test the viability of the two concepts, I created low-fidelity prototypes and tested them with users in real-time. Through this process, I discovered that users preferred the browser extension over the mobile app. Armed with this insight, I was able to focus on refining the stand-alone browser extension concept.
A browser extension that uses machine learning algorithms to track users’ preferred product and brand selections making it easy for them to remember and repurchase products they love in the marketplaces they frequent.