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🚀 Built an Employee Retention Intelligence Dashboard in Tableau!
Many HR teams struggle to answer: Which department is losing talent & WHY?
I built a solution that tracks:
🔹 Attrition by Department
🔹 Reason for Leaving - Exit Insights
🔹 KPI Overview: 150 Employees | 32% Attrition Rate
Goal: Help HR identify retention risks & improve engagement strategy.
🛠️ Built with: Tableau Public | HR Analytics
🎥 Walkthrough: https://lnkd.in/g3bAtN2c
🔗 Live Dashboard: [https://lnkd.in/gcUNTuVj]
Feedback welcome! 👇
#Tableau #HRAnalytics #PeopleAnalytics #DataVisualization #DashboardDesign #TableauPublic #OpenToWork
Thanks for the support! 🙏
🎥 Full Walkthrough: [
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🚨 AI FEATURES DON’T CREATE RETENTION.
COMPOUNDING VALUE DOES.
Most AI note-taking apps compete on:
🎙 faster capture
📝 better summaries
🏷 smarter tagging
🔎 stronger search
Useful, yes.
But none of that guarantees users come back.
The bigger opportunity is when the product starts getting more valuable because the user has already used it before.
In this concept, AI doesn’t just process a note.
It notices:
→ which ideas keep returning
→ which topics appear across different contexts
→ which thoughts may deserve action
→ when an old note becomes relevant again
That changes the product from an archive into a system that learns from accumulated behavior.
And that creates a much stronger growth loop:
More input → more context → better resurfacing → higher perceived value → more input
That’s the kind of mechanic I’d test for retention.
📊 Product targets
• 30%+ faster capture flow
• 40%+ increase in resurfaced-note engagement
• 20%+ improvement in weekly return rate
• 2× higher note-to-action conversion
• 50% less manual organization
The interesting AI products won’t win because they generate more.
They’ll win because every interaction makes the next one more valuable.
👉 BOOK A MOBILE APP STRATEGY SESSION
https://calendly.com/asol_design/book-diagnostic-call-linkedin-clone
What I like most here is the idea of AI becoming more valuable with time, not just more capable in the moment. If every new note improves how older thoughts are connected and resurfaced, the archive stops being storage and starts becoming product intelligence
Designed Booking Analytics dashboard case study.
The goal was to make complex booking data easier to understand and act on, without making the dashboard feel overwhelming.
I focused on:
→ Clear information hierarchy
→ Easy-to-scan analytics
→ Simple data visualization
→ Practical SaaS dashboard patterns
→ A clean, structured UI
I’ve documented the full design process and thinking behind it in the case study.
Check it out
Pairing the status pie chart with the daily bookings bar right below it is smart, you get composition and volume at a glance without a third chart competing for attention.
