Customer Support Analytics Dashboard

Author: Ashmita Aich
Tools Used: Tableau, Excel
Dataset Source: Kaggle

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Project Overview

This project analyzes customer support ticket data to identify patterns in resolution time, SLA adherence, backlog volume and shift-wise performance.
The goal is to uncover operational inefficiencies and support data-driven decisions in managing support resources and ticket prioritization.

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Objectives

• Track overall ticket volume by shift and priority
• Analyze SLA breach rates across ticket types
• Measure and compare average resolution time by support shift
• Monitor backlog of unresolved critical and high-priority tickets
• Enable shift- and priority-level filtering for deeper insights
• Visualize support performance KPIs in an interactive dashboard

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Data Cleaning (Excel)

• Removed irrelevant columns: gender, age, email
• Renamed unclear or inconsistent column names for clarity
• Created new calculated fields for analysis:
  • Resolution Status – based on whether the ticket was resolved
  • Resolution Time (hrs) – duration between ticket creation and closure
  • SLA Breach Flag – identifies tickets breaching SLA thresholds
  • Shift – derived from ticket creation timestamp
  • Status Categorized – grouped detailed status values into categories

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Dashboard Features (Tableau)

✅ KPI Cards

• Total Tickets
• Average Resolution Time
• SLA Breach Rate
• Unresolved Critical Tickets

Hourly Ticket Trend by Shift and Priority

Line chart showing ticket volume trends by hour, split by shift and priority. Reveals peak support hours and workload distribution.

Support Performance by Shift

Dual-axis chart comparing average resolution time and ticket count across Morning, Afternoon, and Night shifts.

SLA Compliance Rate by Channel

Bar chart comparing SLA adherence across support channels: Phone, Chat, Email.

Ticket Status Distribution by Priority

Clustered bar chart showing how ticket statuses (Open, Resolved, Pending) vary across priority levels.

Risk Analysis by Priority

Bar chart highlighting unresolved tickets at each priority level to assist in backlog cleanup.

Dynamic Filters

• Filter by Shift
• Filter by Priority

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Key Insights

• Afternoon shift receives the highest ticket volume
• Night shift has the longest average resolution time → potential staffing issue
• High-priority tickets have an SLA breach rate of 8%
• Over 30% of critical priority tickets remain unresolved
• Phone support performs best in SLA compliance, with a 96% rate

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Conclusion

The dashboard provides clear visibility into support operations, highlighting key issues like SLA breaches, delayed resolutions and unresolved high-priority tickets. These insights enable data-driven decisions to improve response efficiency, allocate resources better across shifts and enhance overall service quality.

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🔗 Project Links

• ** Tableau Dashboard:**
  Customer Support Analytics Dashboard

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