/analytics

name: "PagerDuty Analytics"
description: >
  PagerDuty Analytics: MTTA/MTTR/MTTE/MTTS definitions, incident and
  interruption counts, aggregation levels (account, service, team, escalation
  policy), time-range constraints, and benchmark tables for interpreting the
  numbers.
when_to_use: >-
  When reporting on incident response performance, SLA compliance, or on-call
  workload from PagerDuty data. Use when:
  pagerduty analytics, mtta, mttr, incident metrics, pagerduty reporting, service performance,
  incident frequency, on-call load, or operational metrics.

PagerDuty Analytics

Overview

PagerDuty Analytics provides data-driven insights into incident response performance. Key metrics include Mean Time to Acknowledge (MTTA), Mean Time to Resolve (MTTR), incident frequency, and responder workload. These metrics help MSPs identify operational bottlenecks, measure SLA compliance, and demonstrate value to clients.

Anti-triggers

MTTA and MTTR measure how fast responders reacted to a page. They are not the same number as contractual SLA attainment on customer tickets, and reporting one as the other misstates compliance to a client.

• **SLA attainment against a customer agreement** — first-response and

resolution targets, business-hours clocks, and breach counts are `freshdesk-sla-business-hours` or `halopsa-contracts`.

• **Ticket volume, backlog, or technician utilisation** — those come from

the PSA, not PagerDuty; use `halopsa-tickets` or `connectwise-psa-tickets`.

• **Per-shift incident load and burnout risk** — Rootly models on-call

health directly; use `rootly-oncall`.

Key Concepts

Core Metrics

| Metric | Description | |--------|-------------| | **MTTA** | Mean Time to Acknowledge -- average time from incident trigger to first acknowledgement | | **MTTR** | Mean Time to Resolve -- average time from incident trigger to resolution | | **MTTE** | Mean Time to Engage -- average time from trigger to first responder engagement | | **MTTS** | Mean Time to Start -- average time from trigger to first status change | | **Incident Count** | Total number of incidents in the time period | | **Interruptions** | Number of off-hours notifications that interrupted responders |

Aggregation Levels

Analytics can be aggregated at different levels:

• **Account** -- Overall account performance
• **Service** -- Per-service performance
• **Team** -- Per-team workload and performance
• **Escalation Policy** -- Policy-level response metrics

Time Ranges

All analytics queries require a time range:

• `since` -- Start of the analysis period (ISO 8601)
• `until` -- End of the analysis period (ISO 8601)
• Maximum range varies by endpoint (typically 6 months)

API Patterns

Get Incident Analytics

pagerduty_get_analytics_incidents

Parameters:

• `since` -- Start of date range (ISO 8601)
• `until` -- End of date range (ISO 8601)
• `urgency` -- Filter by urgency (high, low)
• `service_ids[]` -- Filter by service
• `team_ids[]` -- Filter by team

**Example response:**

{
  "data": {
    "mean_seconds_to_acknowledge": 180,
    "mean_seconds_to_resolve": 3600,
    "mean_seconds_to_engage": 120,
    "mean_seconds_to_first_ack": 180,
    "mean_seconds_to_mobilize": 300,
    "total_incident_count": 42,
    "total_interruptions": 8,
    "up_time_pct": 99.5
  },
  "filters": {
    "since": "2026-03-01T00:00:00Z",
    "until": "2026-03-27T00:00:00Z"
  }
}

Get Service Analytics

pagerduty_get_analytics_services

Parameters:

• `since` -- Start of date range (ISO 8601)
• `until` -- End of date range (ISO 8601)
• `service_ids[]` -- Filter to specific services

**Example response:**

{
  "data": [
    {
      "service_id": "PSVC123",
      "service_name": "Payment API",
      "mean_seconds_to_acknowledge": 120,
      "mean_seconds_to_resolve": 2400,
      "total_incident_count": 15,
      "total_interruptions": 3,
      "up_time_pct": 99.8
    },
    {
      "service_id": "PSVC456",
      "service_name": "Auth Service",
      "mean_seconds_to_acknowledge": 300,
      "mean_seconds_to_resolve": 7200,
      "total_incident_count": 27,
      "total_interruptions": 5,
      "up_time_pct": 98.9
    }
  ]
}

Common Workflows

Monthly Performance Report

1. Call `pagerduty_get_analytics_incidents` for the past month 2. Call `pagerduty_get_analytics_services` to break down by service 3. Calculate trends by comparing to the previous month 4. Highlight services with degrading MTTA/MTTR 5. Report on total incident count and interruption frequency

Service-Level SLA Compliance

1. Get analytics for the target service over the SLA period 2. Compare MTTA against the acknowledgement SLA (e.g., < 5 minutes) 3. Compare MTTR against the resolution SLA (e.g., < 4 hours) 4. Calculate uptime percentage 5. Flag any SLA violations

Identify Problem Services

1. Get per-service analytics for the past 30 days 2. Sort by `total_incident_count` descending to find noisiest services 3. Sort by `mean_seconds_to_resolve` to find slowest-to-resolve services 4. Cross-reference with service dependencies to assess impact 5. Recommend alert tuning or architectural improvements

On-Call Workload Assessment

1. Get analytics filtered by team for the past month 2. Review `total_interruptions` to measure off-hours impact 3. Compare workload across teams to identify imbalances 4. Review escalation frequency to assess coverage gaps 5. Recommend schedule adjustments for better distribution

Trend Analysis

1. Query analytics for multiple time periods (e.g., each of the last 6 months) 2. Track MTTA and MTTR trends over time 3. Identify whether incident response is improving or degrading 4. Correlate changes with team size, tool changes, or process improvements 5. Present trends in a summary table

Metric Interpretation Guide

MTTA Benchmarks

| MTTA | Assessment | |------|------------| | < 1 min | Excellent -- likely automated acknowledgement | |