Alex Johnson

Overview

A mixed-methods study to identify friction points and improve activation for new Jira Mobile users. The goal was to understand where users dropped off and what "aha" moments were critical for long-term retention.

My Role

I was the lead researcher on this project. I planned the study, recruited participants, conducted interviews and usability tests, analyzed the data, and presented the findings to the product team.

Process & Methods

We combined qualitative and quantitative data to get a full picture:

• Interviews: 1-on-1 sessions with 12 new users to understand their goals and initial impressions.
• Surveys: An in-app survey deployed to 500 users to quantify key friction points.
• Usability Testing: Moderated tests of the onboarding flow to identify specific UI/UX issues.

Impact

The research led to a complete redesign of the onboarding flow, which resulted in a 15% increase in user activation and a 10% decrease in 7-day churn for new mobile users.

Overview

Foundational research to inform the redesign of Confluence's information architecture (IA). The existing IA was several years old and no longer scaled, leading to poor content discoverability.

My Role

As the lead researcher, I partnered with a UX Designer and Product Manager to map out the existing IA, identify user mental models, and test new IA structures.

Process & Methods

This project relied heavily on remote, unmoderated testing methods:

• Card Sorting (Open & Closed): We ran an open card sort with 50 participants to understand how they group topics, followed by a closed sort to validate our proposed categories.
• Tree Testing: We tested two new IA variations against the existing one (A/B/C test) with 150 participants to see which structure performed best for key tasks.
• User Interviews: Follow-up interviews with 8 participants to dig deeper into the "why" behind their card sort and tree test behaviors.

Impact

Our validated IA structure was implemented, leading to a 30% improvement in task success rate for finding content in Confluence. The findings also informed the creation of a new "Space" categorization feature.

Overview

An evaluative research study to assess the usability and effectiveness of the internal Atlassian Design System (ADS) for designers and engineers. We wanted to know what was working, what wasn't, and how to improve adoption.

My Role

I was the sole researcher for this project, collaborating with the Design System team. I handled all aspects of the research, from planning and execution to analysis and reporting.

Process & Methods

We used a two-phased approach to get feedback from both core user groups:

• Heuristic Evaluation: I conducted an expert review of the ADS documentation site against usability best practices.
• Interviews (Designers & Engineers): I conducted 1-on-1 sessions with 6 designers and 6 engineers to observe them using the design system to build a prototype component.

Impact

The findings were prioritized into a "Top 10" list of usability issues, which the ADS team used to backlog their next quarter of work. This included major improvements to component search and code snippet discoverability.