Emulate Study Manager
Addressed a critical friction point to improve user satisfaction and retention.
What is Emulate?
Emulate offers a humane alternative to animal drug testing in the cancer, respiratory, and cosmetics markets. Their platform combines instruments and software to create microenvironments that allow researchers to model disease pathology and the effects of drugs on specific organs.
Contribution
UX/UI, User Interviews
Tools
Sketch, Visual Studio Code
Timeline
3 weeks
Team
Mariya Sitnova
How it works
Organ chips house human cells and can mimic both chemical and mechanical forces to closely emulate human biology.
Zoë houses organ chips and can control a great number of conditions such as temperature, media flow rate, flex, and strain to mimic biological conditions.
Study Manager is a companion software dedicated to assist with guidance, calculations, and documentation for experiments run with Emulate’s products.
The Problem
Despite high initial adoption of Study Manager, many clients were abandoning the app after conducting just a few experiments. This resulted in a retention rate of less than 70%. These trends raised concerns about the sustainability of client relationships in the long term as Study Manager was a major offering for Emulate.
Discovery
I conducted user interviews to try to understand where and why users were abandoning Study Manager. To keep my approach lean, I worked with internal engineers and scientists.
The interviewees provided valuable feedback regarding the positive and negative aspects of their experience with Study Manager.
Barrier to Entry
Study Manager provided numerous features that significantly reduce researchers' workload, including calculations, data generation, and Zoe cloud control. However, I discovered that users were experiencing a critical issue at the start of each experiment.
Before Study Manager, researchers relied on software such as Excel and PDFs of protocols to conduct their experiments. While Study Manager offered several valuable features, manually inputting protocols proved to be a great point of friction. Many users were deciding that it was impractical to transition to Study Manager.
Current Experience
This process had to be repeated between 9 and 24 times depending on the protocol.
How might we empower users to start their experiments more efficiently?
I explored a few potential design approaches; all of which involved automating the process of translating protocols into Study Manager.
I knew from user interviews that researchers initiated experiments with deliberate and specific plans in mind. They needed to find their predetermined protocol, not browse and decide which one they needed. This meant the user experience needed to prioritize quick selection over discovery.
THE CHALLENGE
Progressive disclosure
a series of questions to narrow down which protocol a user needs
Full page view
a dedicated page displaying all of the available protocols
Dropdown menu ✅
a modal with a dropdown list of the available protocols
Search ✅
users can directly input which protocol they need from the start
Design options I considered
I opted to proceed with a solution that included a dropdown menu modal and search functionality. Since we only had nine protocols available at the time, scanning the items would be quick. Users could also directly search a protocol from the outset if preferred.
Constraints
Due to time and resource constraints, I strategized with my Product Design Manager to break up this project's scope into phases. This initial phase would be an MVP solution where we would focus on delivering the value of automation and in a V2 we could explore progressions and edge cases based on user reception.
PDF to Study Manager
I learned through dev feasibility meetings that we could automate the protocol input by translating the protocol PDFs into JSON files, including only the required information for Study Manager. I completed this for each protocol before the feature was implemented.
Final Solution
Users could now auto-load entire protocols into Study Manager with a few clicks.
Next Steps & Reflection
After launch, we observed an increase in CSAT from 71% to 86%.
With the launch of our MVP complete, my focus shifted towards monitoring user feedback and progressing to a V2 iteration. During my earlier discovery phase, I emphasized extending this feature to better accommodate edge cases such as custom protocols.