The Support Ticket Priority Dataset contains 50,000 synthetic helpdesk tickets generated across 25 companies of different sizes and industries. Each record represents a ticket with features such as company profile, region, product area, booking channel, customer sentiment, and impact indicators like downtime, customers affected, and error rate. The target variable is priority (low, medium, high), designed for classification tasks. The dataset is useful for benchmarking machine learning models, studying feature engineering, handling mixed categorical and numerical data, and analyzing class imbalance. It is fully artificial, ensuring no sensitive information, and provides a realistic environment for training and tutorials.

🎯 Core KPIs

1. Total Tickets – overall and filtered by company, region, or industry.
2. Tickets by Priority – count & % split of low, medium, high.
3. Average Downtime (min) – by priority, region, or product area.
4. % Tickets with Payment Impact – (payment_impact_flag = 1).
5. % Tickets with Security Incidents – (security_incident_flag = 1).
6. Tickets with Data Loss – volume & % of total.
7. Average Error Rate (%) – across tickets, segmented by product area.
8. Average Customers Affected – by priority and industry.
9. Sentiment Breakdown – % negative, neutral, positive sentiment tickets.
10. Ticket Volume Trend – by day_of_week or company.

Get the dataset here: https://www.kaggle.com/datasets/albertobircoci/support-ticket-priority-dataset-50k?resource=download

📊 Advanced KPIs

1. Tickets per 1000 Users – (Tickets ÷ org_users).
2. Incident Frequency Index – (past_90d_incidents ÷ past_30d_tickets).
3. Priority Escalation Ratio – (% high priority ÷ % low priority).
4. Runbook Coverage – % of tickets with has_runbook = 1.
5. Channel Efficiency – average downtime & error rate by booking_channel (web, email, chat, phone).
6. Industry Risk Score – weighted avg of security + payment + data loss flags.
7. Company Tier Performance – compare Basic vs Plus vs Enterprise tiers in priority distribution.

📈 Suggested Dashboard Pages

1. Executive Overview
   • Total tickets, priority distribution, incidents, downtime.
   • KPIs: high-priority % trend, avg downtime, security/data loss flags.
2. Operations View
   • Tickets by day_of_week, channel, product area.
   • Customer sentiment vs. priority.
   • Runbook coverage gauge.
3. Company & Region Analysis
   • Company-wise workload, incidents, downtime.
   • Regional comparisons (AMER, EMEA, APAC).
4. Risk & Impact Monitor
   • Tickets with financial/security/data loss impact.
   • Customers affected heatmap by product area.

⚡ These KPIs will make the Power BI dashboard actionable for:

• Executives (see risks, priority load, customer impact).
• Operations Managers (staffing, channels, downtime patterns).
• Data/ML Teams (monitor class imbalance, sentiment, feature influence).