This document describes the process of creating a new infrastructure cost/usage optimization tool for Electronic Arts (EA). The user experience (UX) team conducted stakeholder interviews, domain research, and created a glossary list to establish a shared understanding. They used Figjam and Google Spreadsheet to take notes during interviews and synthesize findings. Finally, they created user stories, hypothetical scenarios, and sample screens to demonstrate what a new solution could look like. The project helped EA scope the problem space of infrastructure usage and cost optimization and set a favorable beginning and establishing a shared understanding across the organization.
💬 Stakeholder Interview & Findings
🖥 Conceptual Wireframes
Before creating an interview plan, we held several discussions with our client, Josh, to gain a better understanding of his job, workflows, current tools, as well as pain points and goals for the project. Since cloud and infrastructure optimization is a highly technical domain, we also conducted parallel domain research and created a glossary list to establish a shared understanding and serve as a help document for future team members.
Additionally, we studied the wider structure of EA and the particular groups involved in the infrastructure cost optimization workstream. Five key stakeholder groups was identified for the interview ****and we went on to script the interview plan for them.
Through the discussion with Josh, we created a sample persona for the target user of the new infrastructure cost/usage optimization tool, communicating clearly his tasks, goals and pain points.
During the interview process, I used two methods to take notes. First, I used Figjam to take down full, raw notes and related quotations. Second, I used Google Spreadsheet to shorten and summarize the participants' answers into a compact table.
While Figjam allowed us to elaborate and easily make visual connections between the notes; the table version allowed other stakeholders, such as the project manager, clients, and other teammates, to get a quick overview without tediously going over the raw notes. The side-by-side display in the table also helped us contrast and combine different ideas to recognize emerging patterns.
While experimenting with Figjam as a research note-taking tool, I found that having all notes on one surface improved comprehension by reducing the need to scroll or flip pages, which can burden your short-term memory. However, one still needs to navigate the space mindfully to avoid becoming overwhelmed or lost.
The Figjam board that we use to take raw interview notes. We color-code the notes to categorize them: red for pain points, green for wants and needs, and purple for domain-specific terms that participants mentioned during the interview. Otter.ai was used to make the transcription process easier and faster.
The Google sheet to summarize each interview. The red texts are where I highlight a pain point and the green texts are where I highlight a need.