Employees struggled to find the right collaborator across a large organization
The current process was a maze of Workday directories and time-consuming interviews, leaving many feeling lost and frustrated.
The current process was tedious, with no guarantee of accomplishing the goal.
Activities and outputs
User interviews (10 users)
Current process mapping
Here’s the problem statement I came up with using the 5Ws (Who, where, what, why, when)
Our users face challenges when trying to identify the right subject matter expert for cross-functional projects. Their current process involves using Workday, which has no project-related information. The results are time-consuming and often inaccurate.
We identified an opportunity to enhance collaboration through “Search”
Our data scientists revealed a wealth of information hidden within employee recognitions. When a user types in something like a project name, team name, or employee’s name, the system interprets the meaning behind the words. It then compares this to the recognition data in the system, helping the search results become more relevant and valuable.
What if we could transform our recognition product from a basic name lookup to an expert-finding system? It already held records of our collective knowledge and experience.
This simple question ignited a journey to reimagine Aspire's search functionality. User interviews revealed the current shortcomings.
The current state of the search was NOT supporting Keyword search
Insight from user interviews:
“I need to find people with expertise for my new project, but I’m not sure how to find them.”
— Participant 1, VP Engineering
Can you imagine finding the perfect collaborator for your project with just a keyword search?
Our data scientists' analysis was a key part of the discovery phase. It gave us confidence that we could expand the search capabilities, a technical solution that will make the search feature more powerful and user-friendly.
For example, if someone is searching for all recognitions related to a project, the system can find the people who were involved, even if the search terms don’t match perfectly.
Our Data scientist’s analysis of the search capabilities
Activities and outputs
Internal systems analysis
Data scientist consultations
Getting stakeholder buy-in was crucial for turning our vision into reality
Stakeholders were skeptical at first. They didn’t see real value in the enhanced feature. Their resistance pushed us to dive deeper, gathering more evidence to support our vision.
I studied the market and competitors’ products, and I realized this was an existing gap among recognition products.
I spoke to 5 sales reps to see if they found any value in our recognition product's keyword search feature. They were not sure, so they started asking prospects in calls.
Sales reps reported that prospects saw a clear differentiator when they heard about a recognition product that could facilitate cross-functional collaboration.
Competitive analysis of other recognition products’ search capabilities
Activities and outputs
Competitive market analysis
Sales rep interviews
Prospect feedback collection
Connecting user issues to business opportunities won over our stakeholders’ buy-in
After stakeholders saw the evidence and realized there was a demand for this feature in the market, they gave us the green light to add it to the roadmap.
To build a prototype, I designed a user flow that mapped out the screens and actions the user needed to perform to find and connect with the right expert.
User flow for the new search feature
I designed a Lo-FI prototype, tested it with 10 internal users, and collected their feedback, which I then presented to my team.
Activities and outputs
Low-fidelity prototyping
User testing sessions (10 users)
User Flow
Here’s how most users Interacted with the prototype
Participants were asked to search for the keyword ‘Workday.’ The prototype then pulled up many recognitions containing the keyword.
They were impressed by how quickly and easily they could find related info and said that they would definitely utilize this feature.
Participants asked if there was a way they could confirm the employee’s subject matter expertise when they got to their profile page so they could feel more confident about contacting them.
Reflecting on user feedback, I recommended we add a section called I’m a resource for to the employees’ profile page, where employees could update their areas of expertise or past projects.
Employees’ new profile page
The user could connect with the other employee through email, phone or chat.
I recommended that we hyperlink Teams for easy chat access because user testing revealed that chat was the users’ preferred contact method within the company.
Users were then directed to Teams’ new chat window to contact the selected employee.
Activities and outputs
Interactive prototype
Hi-Fi Design
Final product video
The launch was more than just a technical achievement; it was a cultural shift towards more efficient, knowledge-driven collaboration across the company.
Final Product video
Impact and results
The ripple effects extended beyond our organization. We had not just improved internal collaboration; we had strengthened our product's position in the market.
Our sales team reported increased interest from prospects, with the enhanced Aspire feature becoming a key differentiator in pitches.
The Organization observed a 25% increase in cross-departmental collaboration.
The leadership team was impressed by the initiative and its results, recognizing our team's efforts in a company-wide announcement.
Our team received a shout-out from leadership for enhancing our product
Employees struggled to find the right collaborator across a large organization. The product team saw an opportunity to bridge this departmental gap.
Deliverables
Stakeholder interviews
Data analysis
User journey map
Mockups
Prototypes
Executive presentations
Final specs
User testing
Role
UX Designer
Year
2024
Duration
1 month
Team
UX/UI Designer (me)
Data Scientist, Software engineer,
Data Analyst,
User Researcher
