PEOPLE ANALYTICS
Reframing Employee Feedback Analytics Around Real HR Needs
COMPANY
Workleap
My Focus
Product Strategy
Problem Framing
Data-Informed Design
Team
Renee N. - Sr. Product Designer
Raquel L. - Sr. Product Manager
Catherine G. - Staff Engineer
Marie-C B. - Staff Data Scientist
YEAR
2026


Workleap Officevibe helps organizations understand how employees feel about their workplace. Written feedback is one of the richest signals available to HR leaders, yet they had no practical way to analyze it within the product. Instead, users relied on Customer Support for custom reports or spent hours wrangling exported data in Excel.
The project began as a tactical request to add advanced filters across our reporting suite, but Workleap needed more than another way to filter data. I reframed the question from “How might we add advanced filters?” to “What would make this report worth using?” I then led the redesign of the Feedback Report to help HR leaders understand what was happening, identify where to investigate, and make better people decisions.
As the sole product designer, I led the problem framing, user research, design, and validation, including cross-functional workshops that aligned leadership around a more valuable product direction.
I focused our research on how HR teams use employee feedback to evaluate managers and identify concerns, then prioritized opportunities against recurring customer requests. I also brought in the Staff Data Scientist early to validate the direction and explore how existing data and AI-generated summaries could provide more actionable insights.
I believed the original focus of adding filters to the Feedback Topics Report would only create a consistent filtering experience across Workleap reports without making the report more useful. Rather than push for a redesign based on opinion, I involved the teams closest to the report to assess its current value, uncover its broader potential, and build the evidence needed to support a change in direction.
Questioning the success signal
High usage suggested strong demand for feedback analysis, but not necessarily a successful experience. The disproportionate volume of support requests and custom quarterly meetings with Customer Success showed that HR leaders still needed help interpreting and supplementing the available data.
Building shared evidence
I brought the feedback report into a cross-department workshop to assess our product suite's features user and business value. Leaders independently reached the same conclusion: reports had the second-highest potential value of the features reviewed, but delivered little value in its current form.
Reframing the opportunity
This created shared ownership of the problem. The conversation shifted from 'whether we should add filters' to 'what HR leaders needed' from the report to help them understand where their gaps were.
Written feedback is one of the richest signals available to HR leaders, but its value depends on what happens next. When managers respond thoughtfully, they build trust and encourage employees to keep sharing. When feedback receives no response, employees can feel ignored and become less likely to participate again.
Across interviews with HR professionals, Customer Support requests, and recurring customer complaints, I found that HR leaders were trying to understand not only what employees were saying, but how managers were responding—and where feedback was being left unanswered.
With limited development capacity after a company reorganization and most of the project’s engineering effort committed to data migration, I partnered with the Product Manager to prioritize the HR needs that would create the most value for users and the business. I translated an initial blue-sky design into a north-star experience, then broke it into deliverable milestones based on the resources available.
Once I tested the concept against production data with our Data Scientist, we uncovered a critical constraint: most customers received very little written feedback even over six months. The original design depended on enough volume to reveal meaningful trends, which meant it could produce sparse or misleading results for majority of our customers.
Together, we explored how to make the experience useful across lower-volume organizations. I translated those data constraints into a revised design direction that surfaced signals customers could interpret reliably, even with limited feedback.
Learnings and Key Takeaways coming soon!

