Data Analytics Dashboard for Team Performance
Visualize team performance and identify bottle necks
🧭 Overview
The Team Performance Dashboard was designed to monitor and visualize the daily operations of technical and customer support teams.
The goal was to provide real-time visibility into support efficiency, case resolution, workload balance, and customer satisfaction, empowering leadership to make data-driven decisions faster.
This project transformed fragmented reports and manual tracking into a centralized, visual analytics experience.
Project Context
Customer and technical support teams are the backbone of service organizations. However, their performance reporting was manual, fragmented, and non-visual.
Leaders and team managers lacked a real-time understanding of:
- Staff workload
- Case resolution trends
- SLA compliance
- Customer satisfaction
The Team Performance Analytics Dashboard addressed these gaps by presenting actionable insights at a glance.
🎯 Objective
Design a real-time, interactive dashboard that:
- Provides a unified view of support team KPIs.
- Displays live data on staff availability, ticket volumes, and case aging.
- Tracks customer satisfaction trends visually.
- Enables managers to make proactive staffing and escalation decisions.
- Delivers clarity through color-coded, minimal visualizations.
👤 My Role
Role: UX Designer
Responsibilities:
- Defined user goals, data hierarchy, and visualization logic.
- Conducted interviews with support agents and managers to map workflows.
- Created wireframes and prototypes for real-time data interactions.
- Partnered with developers to ensure feasibility of analytics visualizations.
- Presented multiple design iterations to leadership for validation.
🧩 The Challenge
- Manual reports created using Excel or ticket exports were time-consuming and error-prone.
- No single platform to monitor agent workload or team productivity.
- Difficult to identify patterns in performance decline or service delays.
- Data was numeric-heavy with no visual context, making insights hard to extract.
The challenge was to visualize complex operational data in a simplified, story-driven manner.
🎯 UX Goals
- Simplify data interpretation through clear visual patterns.
- Provide real-time, trustworthy data accessible to all roles.
- Design flexible modules to scale across regions and teams.
- Present actionable insights instead of static numbers.
- Improve team transparency and performance awareness.
🔍 Research & Insights
Methods
- Observation Studies — Shadowed support agents during live issue resolution.
- User Interviews — Conducted with customer support staff, team leads, and senior managers.
- Workflow Mapping — Identified bottlenecks between ticket creation and resolution.
- Data Analysis — Studied existing performance logs to define KPIs.
Key Insights
- Most managers relied on end-of-day summaries, losing real-time visibility.
- Agents often lacked feedback on how their workload and resolution time compared to peers.
- Leaders needed an at-a-glance dashboard to understand team morale and stress load.
- Visual indicators (like colors and trend lines) improved data comprehension significantly.
👥 Personas
Persona 1: Anita – Support Agent
- Age: 28
- Role: Technical Support Executive
- Goals:
- Resolve customer tickets quickly and accurately.
- Track personal performance and SLA adherence.
- Receive feedback on productivity and satisfaction levels.
- Frustrations:
- Lack of visibility into workload balance and daily performance.
- Manual updates to Excel sheets after every shift.
- Quote:
“I want to know if I’m performing well — without spending half my day filling out reports.”
Persona 2: Ravi – Team Lead
- Age: 35
- Role: Support Team Manager
- Goals:
- Monitor agent performance in real time.
- Allocate cases efficiently based on availability.
- Identify trends in customer sentiment and recurring issues.
- Frustrations:
- No consolidated view of who’s available or overloaded.
- Difficult to compare team metrics across shifts.
- Quote:
“I need to see who’s online, how the team is performing, and where issues are piling up — all on one screen.”
Persona 3: Meera – Operations Head
- Age: 42
- Role: Senior Manager, Customer Experience
- Goals:
- Evaluate support efficiency across multiple teams.
- Identify risks early and make staffing adjustments.
- Present team insights to leadership in review meetings.
- Frustrations:
- Reports are data-heavy and not presentation-ready.
- Lacks real-time visibility during critical business hours.
- Quote:
“By the time I get the report, the problem has already passed. I want to make decisions in real time.”
🧠 Design Strategy
Vision: Transform raw performance data into actionable, human-readable insights.
Approach:
- Information Hierarchy: Separate global KPIs (team level) from individual agent data.
- Color Logic:
- 🟢 Green = Performing Well
- 🟠 Amber = Needs Attention
- 🔴 Red = Critical Performance
- Data Storytelling: Use trend lines, satisfaction meters, and workload heatmaps to tell the performance story visually.
- Responsiveness: Dashboard accessible on large displays and tablets for on-floor use.
🧪 Process & Approach
The design process followed a lean UX cycle:
- Observation & Interviews → Identify patterns and pain points.
- Sketching & Wireframes → Low-fidelity drafts to test layout concepts.
- Wireframe Iterations → Refined based on stakeholder feedback.
- Visual Design → High-fidelity screens.
- Usability Testing → Evaluated data comprehension and layout clarity.
- Development Handoff → Delivered specifications and guided visualization logic.
Design Process: Observe → Sketch → Wireframe → Iterate → Visualize → Validate
🎨 Design Evolution
1️⃣ Early Wireframes
- Designed to define key performance clusters (Team KPIs, Agent Availability, CSAT).
- Prioritized real-time tracking with clear status indicators.
- Tested color-coding and visual grouping for ease of reading.
2️⃣ Final Dashboard
- Modular layout for scalability and customization.
- Unified top-level KPIs (SLA, Tickets Resolved, Customer Happiness Index).
- Activity heatmap visualized staff online/offline patterns.
- Introduced comparison view between shifts and individual agents.
Visual Enhancements:
- Dynamic tooltips for data explanation.
- Trend arrows for performance movement (↑/↓).
- Dashboard auto-refresh every 60 seconds.
🚀 Outcome & Impact
| Metric | Before (Manual Reports) | After (Analytics Dashboard) | Improvement |
|---|---|---|---|
| Data visibility | End-of-day Excel files | Real-time dashboard | ⚡ Instant updates |
| SLA monitoring | Manual tracking | Automated live metrics | ⏱️ 75% faster insights |
| Report preparation | 2 hrs/day | Automated visualization | 💡 Saved 8 hrs/week |
| Decision-making | Reactive | Predictive | 🔄 Shift to proactive management |
Business Outcomes:
- Enhanced operational transparency across teams.
- Improved SLA compliance by 20%.
- Boosted team engagement through visibility and recognition.
- Strengthened leadership’s ability to course-correct in real time.
💬 Learnings
- Dashboards are successful when they speak the user’s language, not the data’s.
- Real-time data requires collaboration between UX, data, and engineering.
- Balancing detail with simplicity is key — show only what matters at the moment.
- Early stakeholder feedback helps align design with business KPIs.
✨ Conclusion
The Team Performance Analytics Dashboard transformed static reporting into an actionable, visual, and scalable system.
It empowered the organization to see, understand, and act on performance insights faster than ever.
“When teams can see their data clearly, performance improves naturally.”