Optum: Cloud Product Contextual Inquiry Study
Problem
A product used by software engineers within Optum’s internal cloud platform was infamous for its lack of functionality, leading to a decline in engagement with the product and an overall loss of dev productivity. I was asked by the Product Owner to run a study that would identify key features to be improved or added to the product road map.
Business Goals
Decrease time on task per session with the cloud product
Increase average sessions per day with the cloud product
Confirm and/or add product features to the dev road map
Study Duration
5 weeks
Study Presented
01/06/2023
Company
Optum
Cross Functional Team Members
Product Owner
Lead Software Engineer
Software Engineer
Methodologies
User Interviewing
Contextual Inquiry Demonstration
Heuristics
Sample roadmap- does not reflect product in this case study
Impact
Using a combination of the interviewing, contextual inquiry, and heuristics to improve the functionality of an internal cloud platform product, I submitted 9 recommendations to the Product Owner and software engineering team.
As a result of the implementation of my recommendations:
A time savings of 20 minutes per session was realized
Software Engineer users averaged 500 sessions a day before improvements, and increased to an average of 1500 sessions a day after implementation of my recommendations
Features to improve on the product road map were either confirmed or added based on my recommendations
Research
The feature of focus for the study was comparable to another widely used product by the general population, so there was a user expectation that it would function conventionally. With this comparable product in mind, I wanted to understand the mental model users had for the internal Optum feature, and how the feature could be improved to satisfy that mental model.
The best way to understand the mental model users had for this feature was to use the interview methodology paired with a contextual inquiry component by asking participants to show me how they used the feature by demonstrating past behavior. I collaborated with the PO to come up with research questions, as well as define the user segments that were important to understand feature use. The PO wanted to gather feedback from users in 3 groups based on how many times they used the feature within the past 90 days from the start of the study.
High Volume Users: 90+ uses
Intermediate Users: 6-89 uses
Low Volume Users: 1-5 uses
I wrote a moderator guide that asked interview questions related to job title and responsibilities as they related to use of the feature, current feature use behavior, how the feature solves user problems, and how the feature does not solve user problems.
After asking the interview questions, I had participants share their screen and demonstrate how they used the feature by showing me past behavior, asking follow up questions when appropriate based on their behavior as I observed them using the feature.
During sessions, many times without being asked, participants reported pain points and compared the Optum feature with the well-known product used by the general population. This was a clear indication how the feature could be improved and allowed me to present recommended changes that would meet the needs of SEs for productivity and the security of company information to the PO and other team members.
What I Learned
This was my first study at Optum, and I really enjoyed working with the PO who was responsible for the feature of the cloud product that I ran the study for. In my previous UXR role, I had been trained to use Confluence to construct my study plan. At Optum, I was given a study plan example in a Word document that I templated for all of my Optum studies.
I learned:
I preferred putting together my study plan in a Word document
Sharing it was easier as team members didn’t need to create an Atlassian account to access a Confluence link
Readability was improved compared to the non-linear layout of the Confluence study plan I had been taught to construct