x.ai User Research
As a tech-focused organization, x.ai was awash in customer behavior data, but lacked qualitative insights about customer motivations or frustrations. I sought to round out our understanding of our customers’ interactions with our product through qualitative, and evaluative research.
For our first large-scale qualitative study, I conducted hour long interviews with customers within their first 6 weeks of using x.ai. Our goal was to gain better understanding of the first stages of the customer experience, and learn how x.ai fit into their existing routines and workflow. The result was a findings report which the Product Team used to prioritize projects and bug fixes in the coming quarter. We continued to reference this research for multiple quarters, eventually using it to inform an improved preference setup flow on my.x.ai, the customer portal.
Customer Segment Personas
The second study I led helped us to gain greater understanding of the needs of recruiting professionals and how x.ai could meet their daily workflow needs. The output was 3 user personas which helped the Product team identify role-specific feature needs.
In addition to my.x.ai, our UI-based companion portal, we have a number of internal tools which I aided in designing. One of those tools, the Vertical Console, is used by our AI Trainers to tag meeting information for machine learning models. I worked closely with our VP of AI Training to conduct bi-weekly usability studies with our remote trainers in order to increase the usability of the system and reduce trainer errors. The result was a set of UI redesigns and recommendations which helped reduce trainer errors as demonstrated by their weekly performance reports.