Administration experience, Workhuman
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Workhuman, a SaaS business, provides recognition programs where colleagues award each other points for good job performance or life events.The Recognition Program Managers (RPMs) were facing significant challenges in managing large volumes of awards that were stuck in the approval process.
Due to the complexity of the data involved, including multiple filtering criteria such as countries, departments, people, roles, award values, and text data, RPMs struggled to manage these awards efficiently. This impacted the user experience and, more critically, affected the subscription renewals and client retention rate since RPMs were often decision-makers in renewing Workhuman services.
My responsibilities
Current System Data Analysis
This helped us identify user behavior patterns:
Users frequently bounced from the report page, indicating that the current filtering system wasn't fulfilling their needs.
We were able to identify the most commonly used actions and data types, which helped us prioritize filter options in the redesign.
Heap
Analytics
User interviews (6 users)
Users needed to filter by Approver, Nominator, and Recipient.
They wanted a way to quickly switch between search results to perform multiple actions in succession.
Figjam
Userlytics
Business Canvas Workshop with Stakeholders & Engineers
We clarified:
The user outcomes we aimed to achieve.
Hypotheses about user behavior.
Key metrics to track success (bounce rates, filtering accuracy).
User benefits and potential challenges
Engineering redlines
Miro
Ideation and Design
We conducted a collaborative session with designers to brainstorm different design approaches. Key exercises included:
User flows to map how users would navigate the filtering system.
Storyboarding to visualize the user journey, particularly focusing on the approval and actioning process for blocked awards.
Later we built a first prototype.
Figma
Miro
+1M Postits
Testing
Tested with 6 users. Plus we run a A/B testing with 50+ users.
SUS Score Round 1: 81.25 points
SUS Score Round 4: 93 points
Maze
SUS Score
A/B testing
Pain points & solution
Overwhelming Volume of Data: With users needing to filter through large amounts of data across multiple fields like countries, departments, roles, and award values, the existing filtering system was insufficient
We implemented a customizable filter system where users could add or remove filters as needed, providing greater flexibility. This approach, inspired by systems like Jira and Heap, allowed users to tailor their filtering experience to their specific needs.
In the initial design, filters were split into two sections, causing confusion and frustration. Users expected all filters to be in one place and found it difficult to navigate between the separate sections.
We redesigned the filtering interface to present a unified filtering system, consolidating all search options into a single, cohesive layout.
Users needed to filter by role (Approver, Nominator, Recipient) across different search criteria like employee number, email, and name.
We introduced a search field with a drawer system where users could search by an individual’s name, email, or employee number, and then choose their role in the approval chain
Design system Components
Date picker
Selector+dropdown
Prototype
KPIs
+16
Retention rate
+3pts
NPS Score
95 pts
SUS Score