Personalizing the Shopper’s Journey with REI Co-op

NC State Graphic & Experience Design seniors in Professor Armstrong’s studio worked with REI to explore possible avenues of personalizing the shopper journey. By leveraging machine learning and artificial intelligence mechanisms, businesses like REI can deliver personalized, efficient, accurate, and relevant shopping experiences to customers. Personalizing experiences have the potential to make interactions more “useful, usable and desirable” to customers in context, i.e. information and options are available when they’re desired during a customer’s shopping journey. Optimized interactions could improve REI’s relationship with customers every time they visit and shop. As the interface “gets to know” customers through data reflecting their behavior, the system should optimize the experience.

Creating these kinds of personalized shopping experiences, however, requires efforts across the REI ecosystem. For example, what kind of data would need to be collected not just from customers, but the vendors who supply products and experiences? How might that data be labeled and organized to efficiently and effectively feed the personalization system?

The goal of this nine week sponsored studio project—in collaboration with REI—was to explore both sides of this retail tension. Students moved through a human-centered research process to dig deeply into the topic and provide REI with multiple opportunities for reimagining their digital and omni shopping experiences through personalization.

N.C. State Bachelor of Graphic & Experience Design (BGXD) seniors  explored two core research questions in collaboration with REI:

How might we design an experience to deliver efficient, accurate, relevant, and personalized content to shoppers?

How might the resulting personalization then impact intake mechanisms and data curation across the REI ecosystem?

Prototypes & Scenario Videos


Student Designers: Kate Warren, Kristina Rozakis, Riley Becker, Michael Reed, ©NC State University


Student Designers: Camille Davis, Madeline Rabe, Anushka Srinivasan, ©NC State University


Student Designers: Jazz Moe, Jennifer Dowden, Joel Weiss, Sally Bui, ©NC State University


MASH, Student Designers: Grace Herring, Tucker Baumgartner, Peyton Tucker, ©NC State University

The Design Process

students and REI team members giving talks and participating in a group exercise using post it notes
MJ: not member, experienced biker, e-bike shopping, digital space. Taylor: member, novice biker, e-bike shopping, Omin shopping space. Mason: member, novice camper, tent shopping, digital space. Rebecca: not member, experienced camper, tent shopping, omni shopping space
Diagrams of various personas which include an image of a person and relevant details of their needs and desires related to the use cases
Student standing before large grids hanging on the wall. Students are filling in info by hand and adding post it notes to this large matrix of ML data possibilities
Several examples of user journey maps: large diagrams that include step by step the current shopping experiences of users
A diagram of user pain points. This exemplifies the kind of pain points the students gathered for the project
Lot of images of different types of data visualizations and outdoor gear shopping interfaces
lots and lots of early sketches created during the ideation phase of the project
Initial REI Crit: images of student faces in a zoom meeting and early sketches of concepts for the project
Round Two Critique: images of two low fi task flows storyboard style to demonstrate the type of work shown at the second critique with REI
Backcasting Exercise : Colorful diagram with post it notes that detail the kind of data needed for various proposed features and the resulting changes in information gathered from vendors
Hi-fi Prototypes & Scenario Videos: several images from the final prototypes: a MASH van, a compatibility rating pie chart, a custom avatar on a bike and a module based trip log detailing elevation climbed, gear used, distance covered