Performance Analysis in ITSM is the continuous process of collecting, measuring, analyzing and reporting service and process metrics to assess health, efficiency and effectiveness of IT service delivery. With ServiceNow you can centralize telemetry, create performance analytics indicators, and combine operational and business KPIs into meaningful dashboards.

• Monitor service health and SLA adherence
• Identify process bottlenecks and root causes
• Optimize resource allocation and capacity
• Demonstrate business value and ROI
• Support continuous improvement & governance

Below are high-value KPIs to measure ITSM performance. Use ServiceNow Performance Analytics to automate their computation and visualization.

Tip: Segment KPIs by service, priority, CI, and team. Aggregate numbers hide hotspots — e.g., a small CI causing many P1 incidents.

• Authoritative timestamps: created_at, first_response_at, resolved_at, reopened_at, change_implemented_at — required for accurate MTTA/MTTR.
• Survey hooks: send CSAT/NPS/CES on close; store result with ticket id.
• Performance Analytics: Use ServiceNow Indicators, Data Collection jobs, and Scorecards to compute and persist KPIs.
• Integrations: Push monitoring/Apm metrics (NewRelic, Dynatrace) into ServiceNow for availability/incident correlation.
• Audit trails: Keep process audit logs for RCA and compliance.

Use these examples as a template to compute KPIs from your ticket table. Replace arrays with SQL queries.

<?php
// Sample: compute MTTR, MTTA, SLA compliance from ticket array
function compute_performance(array $tickets) {
    $closed = array_filter($tickets, fn($t) => !empty($t['resolved_at']));
    $closed_count = count($closed);
    $total_res_minutes = 0;
    $total_resp_minutes = 0;
    $sla_breaches = 0;
    $reopened = 0;
    $fcr_count = 0;
    foreach ($closed as $t) {
        $resMin = (strtotime($t['resolved_at']) - strtotime($t['created_at']))/60;
        $respMin = isset($t['first_response_at']) ? (strtotime($t['first_response_at']) - strtotime($t['created_at']))/60 : 0;
        $total_res_minutes += max(0,$resMin);
        $total_resp_minutes += max(0,$respMin);
        if(!empty($t['sla_breach'])) $sla_breaches++;
        if(!empty($t['reopened'])) $reopened++;
        if(!empty($t['first_contact_resolved'])) $fcr_count++;
    }
    $mttr = $closed_count ? ($total_res_minutes/$closed_count) : null;
    $mtta = $closed_count ? ($total_resp_minutes/$closed_count) : null;
    $sla_pct = $closed_count ? (1 - ($sla_breaches/$closed_count)) * 100 : null;
    $reopen_rate = $closed_count ? ($reopened/$closed_count)*100 : null;
    $fcr_pct = $closed_count ? ($fcr_count/$closed_count)*100 : null;
    return compact('mttr','mtta','sla_pct','reopen_rate','fcr_pct');
}
?>
                        

Executive Dashboard (one-pager)

• Availability (top services) — current & trend
• SLA Compliance (overall & by priority)
• MTTR / MTTA trends (last 12 months)
• CSAT & NPS trend
• Top incidents & blockers (top 5)
• Change success & emergency change rate
• Open risks and mitigation status

MSR (Monthly Service Report)

• Detailed KPI table (this month vs previous, MoM % change)
• Backlog, ticket aging distribution
• Major incidents & RCA summary
• Improvement actions and owners
• Budget vs actual (operational cost)

QBR (Quarterly Business Review)

• Strategic KPIs & business outcomes
• Automation ROI and productivity gains
• Roadmap & investments required
• Top risks & mitigation status

Case Study 1 — Enterprise Retailer

Problem: Frequent P1 incidents caused by configuration drift. Approach: Integrated monitoring with ServiceNow, created automatic incident creation & tagging, and added runbooks for auto-remediation. Result: P1 count reduced by 40% and MTTR improved from 6 hrs to 2.5 hrs within 4 months.

Case Study 2 — Managed Service Provider (MSP)

Problem: High reopen rates and poor CSAT. Approach: Mapped problem areas, reengineered triage flow, enforced mandatory root cause notes, and introduced pre-close CSAT prompts. Result: Reopen rate dropped from 12% to 4%, CSAT improved by 11 points.

Case Study 3 — Financial Services

Problem: Poor SLA compliance for critical apps. Approach: Implemented prioritized routing, on-call rotations, ServiceNow alert correlation and runbook automation for common remediations. Result: SLA compliance improved to 97% and incidents due to known errors reduced 35%.

Performance analysis must integrate RCA to move from symptoms to fixes. Recommended approach:

1. Trigger RCA after a P1 incident or recurring incident pattern.
2. Use 5-Why or Fishbone analysis and link findings to Problem Records in ServiceNow.
3. Track Time-to-Permanent-Fix as a KPI for Problem Management.
4. Validate fix by monitoring incident recurrence and related KPIs.

• Define Owners: Each KPI must have an owner and SLA for reporting cadence.
• Data Quality Rules: Enforce mandatory fields and timestamps in the ticket lifecycle.
• Alerts & Playbooks: Map KPI thresholds to automated alerts and runbooks.
• Continuous Improvement: Run monthly KPI review meetings and assign action items.
• Segment & Contextualize: Always present KPIs segmented by service, CI, priority, and team.

Recommended stack and visualization approach:

• ServiceNow Performance Analytics & Dashboards
• Integration with APM tools (Datadog, NewRelic, Dynatrace)
• BI layer for executive reports (PowerBI / Tableau connected to a reporting DB)
• Chart libraries (Chart.js / Highcharts) for web visualizations
• Automated report exports (PDF/DOCX) for MSR/QBR

1. Start with a KPI shortlist (MTTR, MTTA, SLA, CSAT, FCR) and instrument them correctly.
2. Use a central data model (CMDB) to correlate incidents with CIs & services.
3. Automate low-value tasks to improve MTTR and agent productivity.
4. Use predictive analytics to detect incident patterns and prevent outages.
5. Set up a monthly KPI review with clear owners and deadlines for improvement actions.

If you want, we can:

• Integrate this page to read live KPI aggregates from your DB / ServiceNow instance.
• Add interactive charts with Chart.js showing MTTR/MTTA/SLA trends.
• Provide SQL templates to compute monthly KPI aggregates from a typical ticket table (created_at, first_response_at, resolved_at, sla_flag, priority, ci).
• Generate exportable MSR/QBR templates (PDF) prefilled with your KPI values.

Tell me which option you prefer and share sample table schema or sample exports — I will extend this page accordingly.