Card Sort Study

Yan Vishnepolsky

Data Scientist
User Researcher
UX Researcher
ChatGPT
Jira
Tallwave
Chase

Pricing Encyclopedia Redesign, 2022

As part of my role as the Principal User Researcher at Tallwave, I led a UX team of three UX designers in research to redesign a pricing encyclopedia application, known as the "Fare Rules" application. This application is used by customer service agents across the travel industry to determine customer costs. Tallwave's client is among the top three US banks. The project significantly reduced call handling time for customer service agents and created substantial cost savings.

Leading into the Project...

Task: Redesign the Fare Rules application for a top US bank to have a visual interface.‍
Context: A Fintech/Travel industry application determining customer costs. This study targeted a feature of a larger software. This feature, the Fare Rules application, was a command-line (like DOS) interface used by customer service agents (our users) to look up Fare Rules, or the rules by which customers were charged for travel.‍

Approach to Research

Problem: Poor discoverability of information in the command-line interface.
Because the discoverability of information was poor, I chose to do a card sort, and because this was formative research, I chose to do an open card sort with Optimal Workshop.‍
An issue with the previous design was that the text (before our redesign) was all in uppercase.‍
Research Environment & Challenges: The card sort took place during the early stage of discovery (formative research), and it later became apparent that if more information about Fare Rules had been available (client was not aware of it at the time), the card sort approach would have been different.‍
Method: A formative open card sort study was conducted with 11 participants using Optimal Workshop. The research plan (study design), can be found below. ‍

Results

5 Groups: Identified via the card sort via k-clustering
70-80% Correlation: User Agreement about Groups
2 Minutes: Reduction in call handling.
Outcome: Improved information architecture, efficient Fare Rules management, reduced workload on customer service, and enhanced customer experiences, based on quantitative Ease of Use scores.
After the Project: As is typical during formative research, this study was followed by a usability test, which yielded additional data and suggested integrating the dashboard and other features.
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