Project Details
Overview
I worked with a team of classmates to create a proposal for REI's digital web space to improve their shopping personalization experience, using machine learning and artificial intelligence.
Timeline
8 weeks
Team
Sally Bui
Joel Weiss
Jazz Moe
My Role
User research, wire-framing, interaction design, task management, and team organization.
Final Scenario Video
User Research
Persona (Provided by REI)
Name: Mason Maldonado
Ethnicity: Latino
Age: 23
Gender: Male
REI Member: YES for 3 months
Camping Interest: Entry/Novice
Wellness Interest: Mid-High/Intermediate
Location: Durham, NC
Scenario (Constructed from Interviews)
Mason is a Durham, NC native and a novice camper. He has gone camping before, but has relied on his friends in order to have the necessary equipment for each outing, and he wants to purchase a tent for himself for his own trips.
Mason wants to take trips that focus on his own wellbeing and hopes to buy gear that will serve him for years to come. He is used to scouring the internet for reviews and listings that fit his needs and budget, but he is new to camping gear and doesn't know where to look or what to trust.
Mason is familiar with REI since buying his membership in order to motivate him to take on adventures for his own sake. He aligns himself with a lot of REI's values and is excited to see how he can maximize the time he spends outdoors.
User Journey Map (As-Is)
As a group, we each interviewed 2 people that would fit the persona we were given. We did this to construct a scenario for our persona, Mason, as well as an as-is user journey map.
Ideation
Based on our research, we wanted to create a sense of community in REI's online spaces which currently felt detached from the consumer. Our interviewees also emphasized the importance of doing their own research before buying products. Our concept worked to combine these points, as well as many others, to create an ecosystem of reliable and community-based product reviews.
TrailMate Service Infrastructure
The TrailMate ecosystem would rely upon a continuous loop of consumers purchasing products after reading the AI-curated reviews. This would lead to the use of these products by new consumers, collecting new data to add to the databased of generated reviews.
App Wireframes
Inside the mobile application, the user would be able to see the data collected from their adventures and how it pertains to their REI products. The user could also add supplementary data such as photos, videos, notes, and voice memos.
Product Features
Trip Logs and AI Reviews
The main feature of TrailMate is the co-created trip logs that transform into AI-generated product reviews. The user-supplemented modules that reflect both passive and active data intake, housed within the TrailMate mobile app, track personalized data on REI products in use. With the permission of the user, this data would be shared to REI's website in the form of an AI-curated review, detailing the unbiased facts and human data into a visual narrative review.
Explore Pages
Another way users would be able to browse for products is through an explore page. This explore page would have two main tabs: location-based and Trail Mate-based. This would allow users to find products from the adventures and adventurers that they are inherently connected to.
Family Model
Because REI memberships apply to entire family units, we wanted to consider that use case. When used by families, the TrailMate app would track each family member's data to their products in a local dashboard that would belong to the lead member.
