Glints: 📈 Analytics Features for Customers
A basic analytics tool for premium plan clients to see job performance in a clear way. Designed, developed and released for enterprise customers in only two weeks.
Role
Product Designer
Platform
Website
Duration
27th March - 14th April
Collaborator
Jenny Tjhio
Product Manager
Tung An
Design Lead
Afzal
Senior Engineer
Kamran
Senior Engineer
Andi Awwal
Senior QA Engineer
Mila
QA Engineer
Overview
This feature was made for premium clients, giving two types of insights: Applications and Conversion. Recruiters can see the number of applications received, processed, or expired, and also track job reach, clicks, and applications. Filters allow them to check data by job or by time period.
Problem Statement
Enterprise clients did not have a quick way to check job performance inside the platform. They had to rely on manual reports, which slowed down hiring decisions.
Discovery
Before this feature, enterprise clients could only export a CSV report to see job performance. The report was just a plain table without charts or summaries, which made it hard to understand trends at a glance.
From feedback with account managers and client support teams, we found:
Clients had to manually filter and calculate metrics from the CSV.
The data felt “hidden” because it required extra steps outside the platform.
Many clients didn’t download the report regularly, so they missed important hiring trends.
Competitive Analysis
To understand how other platforms present similar insights, I reviewed competitors like JobStreet, Kalibrr, and Dealls.
Key takeaways:
Applications Trend: Show application growth over time with clear visualizations (Kalibrr, Dealls).
Conversion Data: Combine reach, clicks, and apply metrics in one view (Dealls, JobStreet).
Sort by Time: Provide fixed date filters for easy comparison (all platforms).
1st Design Process
Based on the competitive analysis, I moved to ideation and created the first design iteration. My goals were to:
Present job performance data in a way that is easy to read and interpret.
Highlight key metrics at the top (total applications, viewed applications, processed applications, expired applications).
Use clear visualizations for trends over time.
Include a conversion funnel to show how reach translates into applications.
Provide a sortable job list so users can compare performance by role.
This first iteration focused on combining trend charts and conversion data in one place, inspired by competitors but tailored to our data and client needs.
Stakeholder Design Review
After completing the first design iteration, I presented it to stakeholders including the C-Level, Tech Lead, Product Manager, and Principal Product Manager.
The design was approved with minimal changes, as they agreed the approach was clear, aligned with user needs, and addressed the original problem effectively.
Challenges
After the first design review, everyone liked the design because it was simple and straight to the point. But when it came to implementation, I faced a big problem:
No Matching Chart Library
I couldn’t find any chart library that matched my “valley-style” conversion funnel design. I spent two full days searching with no luck.Frustation and Doubt
At that point, I felt frustrated and realized I should have validated the feasibility earlier—especially since the final design had already been approved by important stakeholders. I asked my manager if we should just use an existing library, even if it looked worse and didn’t match the original design.
Breakthrough
I didn’t give up. I kept searching until i found a small, lesser-known library. Surprisingly, it was free, customizable, and even had a beautiful horizontal funnel style with smooth curves. It wasn’t my original “valley” style, but it looked great and fit our needs perfectly.
2nd Design Iteration & Decision
In the second design iteration, I switched to the latest UI library after confirming with a senior developer that it was technically feasible. I also refined the locked and empty states to make them cleaner and more consistent across the feature.
During a flow review, a senior backend engineer asked how the “10% higher,” “10% lower,” and “no change” labels would be calculated. I explained that they compare the current performance metric with the previous one using a simple percentage formula. This discussion helped align the design with the data logic, preventing confusion later in development.
Before merging the feature from the release branch to staging, I collaborated with the Senior QA Engineer to test and resolve any endorsement cases that didn’t match the intended design. We ensured the feature only worked in the correct scenarios, making it ready for release five days later.
Results
Secured a 10 Millions IDR Deal
Client purchased a 6-month subscription plan worth 10M IDR after we offered a free trial and built this analytics dashboard for them.
Scaled Feature Release
The analytics dashboard was rolled out across all subscription plans to be purchased by users.
Key Takeaways:
Design can unlock real business value.
What started as a rushed feature to satisfy one enterprise client ended up securing a 6-month paid deal and proving how design can directly contribute to revenue.Scalability matters as much as speed.
Even under a tight two-week deadline, designing with flexibility in mind allowed this feature to expand beyond one client and roll out to all subscription plans.
Collaboration turns challenges into opportunities.
From aligning with backend engineers on data logic to QA testing edge cases, every iteration showed me how cross-functional teamwork ensures a design not only looks good but also works reliably in production.
See potential here?
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