Work

Byrider enterprise mobile app

Role: Lead Designer
Duration: 9 months

Challenge: The current practice for field buyers kept track of bids and purchases by marking off line items on paper spreadsheet printouts. Auction days rapidly progress by the minute, which made the printouts quickly outdated–leading to errors and wasted time.

Byrider engaged my team to create an enterprise app to help buyers keep track of inventory and use previous data to predict success in a fast-pased environment.

What we did and why

We conducted discovery interviews with field buyers to understand each step in the bidding process. The goal was to emulate that in a digital experience to reinforce their steps on mobile.

Local auctions displayed upon entry in a list, which was sorted by All or Recent auctions. I incorporated a calendar to sort by date.

I worked with the data scientist and backend engineers to use machine learning to use previous auction data to forecast which purchases would be successful.

Research

We visited Byrider headquarters to test production builds at local auctions. Field Buyers volunteered to use the build in their daily auctioning activities, and point out successes and challenges. Using my research findings, I simplified the navigation and worked with engineers to combine six separate categories into three distinct lists. Limiting options was critical for success in a fast-paced environment.

We explored ways to facilitate searching, by incporating the. activity bar at the bottom. This allowed the buyers to scan a vehicle identification number (VIN), add auctions and vehicles to a Favorite list, or search by vehicle type.

Version 1 Wires/Mocks

Visual components

I designed device-independent components to reinforce a uniform experience between Byrider employees and supervisors. This allowed them to help each other learn common patterns. I incorporated the brand colors and prioritized the dark mode for use in full sunlight and low visibility.

Version 2 Mocks: Landing page and Calendar

Version 2 Mocks: Auction detail screen

Results

We launched an enterprise app that directly connected the buyers activity with the store inventory. This resulted in less paperwork and less mistakes made when translating purchases into active inventory.

Incorporating machine learning models provided better predictions for buyers to plan where best to spend their time and efforts.

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