'The Numbers Don't Add Up'
Metrics didn’t match manual counts, destroying confidence in all analytics.
Business Impact:
Users defaulted to manual tracking, ignoring the value of our dashboard
Original
Redesigned
Context: Why This Mattered
Our dashboard was supposed to prove “Hey, look what our platform can do for you!” However, 99% of users ignored it completely. This created some serious issues:
We promised 'powerful analytics', delivered 'meh.'
New users land on dashboard, see nothing useful, bounce
Can't prove value when nobody engages with the data
What We Discovered: The Root Causes
3000+
Sessions Analysed
15+
User Interviews & Feedback Reviewed
User research and data analytics identified four critical root causes for the 0.1% engagement rate
'Answering the Wrong Questions'
Dashboard didn’t answer questions recruiters were asking (e.g., “apps per job” vs. the needed “apps per source”).
Business Impact:
Users fail to see the values they are paying for, risking churn.
'Interesting, but Now What?'
Data was “interesting” but not actionable. Users saw a number and didn’t know the next step.
Business Impact:
Low engagement made it hard to prove that analytics actually provided value.
'Can't Dig Deeper'
No filters meant power users couldn’t answer specific business questions.
Business Impact:
Power users churned to competitors with better analytics.
Strategic Trade-offs
Through cross-functional discussions with Product, Engineering, and Customer Success, we aligned on success criteria and assessed the risks of a major re-launch.
Rebuild vs. Patch
With 0.1% engagement, we had nothing to lose. Convinced leadership to invest 3 months instead of shipping quick fixes.
Trust vs. Bells and Whistles
Leadership wanted AI features. We successfully pivoted the roadmap to data transparency first. Can’t upsell features if users don’t trust your data.
Filters vs. Custom Dashboards
Deprioritized “drag-and-drop” widgets in favor of saved filters to solve the core pain point faster with less dev overhead.
Design Direction
The redesign focused on three strategic pillars:
Transparency
All metrics must be explainable to build trust and confidence
Customization
Enable recruiters to filter and segment data according to their specific needs
Actionability
Analytics must connect to workflows to create a complete user flow
Design Solution
Information Architecture
Ranked modules by actual session frequency and a manual audit of custom report requests.
“Must-have” metrics moved to the top, secondary data was grouped below to reduce cognitive load.
Building Trust
Introduced metric definitions via tooltips.
Design rationale
When users said “the numbers don’t match what I counted,” the issue was often that we count these numbers differently. Making these definitions explicit and always accessible restored confidence in the data.
The Filter System
Enabled users to segment data by time, company, department, and more
Design rationale
Filters directly addressed the “Can’t dig deeper” pain point and became our highest-impact feature. The filter design prioritized speed and clarity: persistent placement, instant application, and consistent with the rest of the platform.
Micro-interaction
Added skeleton states to provide immediate feedback during backend queries, improving perceived performance.
Strategic Visual Refresh
Included visualizations to make insights scannable & signify the big re-release
Design rationale
Besides the functional purpose of making data patterns more scannable, the stark visual change was a psychological reset for users who had spent months ignoring the old dashboard.
Contextual Actions
Connected KPIs to relevant workflows for deeper analysis
Design rationale
This solved the “Interesting, but now what?” problem. Contextual links transformed passive data consumption into active workflow engagement, making the dashboard a launchpad rather than a dead end.
Scalable Framework
Built reusable components aligned with the design system for scalability
Design rationale
Every chart type, tooltip pattern, and filter component was built as a reusable design system element, so as to ease future dashboard expansion while maintaining consistency.
Impact & Validation
30×
Engagement Boost
Increase in active dashboard usage (0.1% → 3.5%).
50%
Filter Interaction
1 in 2 clicks are now filter-based, proving “intentionality.”
300%
Scroll Rate Increase
Users are exploring the full depth of data, not just the “fold.”
Filters drove 50% of the engagement increase.
We transformed the dashboard from a “snapshot” into a tool. This proved that “customization” was the missing link in user value.
Contextual workflow links drove the other 50% of the increase.
Users were seeing data and acting on it. This validated our “Actionability” principle. Users no longer saw data as a dead end.
Visual refresh restored user interests
The new visual / visualizations successfully sparked users’ curiosity. By making the dasshboard visually distinct from the “broken” version, we encouraged users to explore the entire page.
Organisational Impacts
Standardised Dashboard Framework
My dashboard components are now the org-wide standard, used across 2 product squads.
Data Culture Shift
Introduced quarterly data review and influenced leadership to promote metrics in future product briefs.
Key Takeaways
- Data without transparency is worthless. Users needed to understand how metrics were calculated before they would believe what they showed.
- Users relied on filtering and segmenting data to make sense of it. A “snapshot” obstructed this sense-making loop.
- Connecting analytics to workflows transformed the dashboard from “interesting” to “essential” in users’ daily routines.
- Resisting AI predictions and advanced features in favor of getting basics right first was the right call. Trust couldn’t be built on a shaky foundation.
