Support anomaly library

Sample event

{
  "support_tickets_per_hour": [
    42,
    45,
    41,
    44,
    43,
    46,
    42,
    44,
    43,
    45,
    41,
    43,
    44,
    42,
    43,
    45,
    44,
    42,
    43,
    44,
    128
  ],
  "detected_value": 128
}

Stream: support_tickets_per_hour · detected_value: 128

Drafted corrective-action plan

Open an incident bridge, pull tier-1 agents from low-priority queues, and post a status-page update acknowledging the surge.

1. Affected systems: Customer Support Operations — inbound ticket volume is running at roughly three times baseline over the rolling hour.
2. Root-cause hypothesis: a recent product release introduced a user-visible regression that funneled new tickets into the help queue faster than triage can absorb.
3. Next three steps: (1) pull the last release notes and grep for the change most likely to drive support load, (2) rebalance tier-1 agents from low-priority queues to the affected queue and update the team on-call rota, and (3) draft a status-page entry so affected customers see the surge acknowledged in-channel.

Sample event

{
  "support_sla_breach_count": [
    3,
    2,
    4,
    3,
    2,
    5,
    3,
    4,
    2,
    3,
    4,
    3,
    2,
    4,
    3,
    2,
    5,
    4,
    3,
    2,
    18
  ],
  "detected_value": 18
}

Stream: support_sla_breach_count · detected_value: 18

Drafted corrective-action plan

Triage the breached-slice first, escalate to the on-call manager, and pre-empt customer outreach before the SLA clock closes.

1. Affected systems: Customer Support Operations — 18 open tickets will breach resolution SLA inside the current window without intervention.
2. Root-cause hypothesis: the ticket-spike surge above swamped the tier-1 queue and pushed past first-touch SLAs into the resolution window before triage could catch up.
3. Next three steps: (1) sort the open queue by SLA countdown and pull the breached-slice into a dedicated triage pod, (2) page the on-call manager with the breach forecast so outreach starts ahead of the clock, and (3) tie the breach back to the upstream ticket-spike alert so the combined signal lands in tomorrow's stand-up.

Sample event

{
  "support_first_response_minutes": [
    8,
    7,
    9,
    8,
    8,
    7,
    9,
    8,
    7,
    8,
    9,
    8,
    7,
    8,
    9,
    8,
    8,
    7,
    9,
    8,
    27
  ],
  "detected_value": 27
}

Stream: support_first_response_minutes · detected_value: 27

Drafted corrective-action plan

Top up the tier-1 rota for the affected hour, review the queue assignment rules, and add a temporary auto-reply to set expectations.

1. Affected systems: Customer Support Operations — median first-response time has drifted to 27 minutes, more than three times the 8-minute baseline.
2. Root-cause hypothesis: the recent product release increased inbound ticket volume without a matching rota adjustment, so the existing tier-1 agents are taking longer to land their first touch.
3. Next three steps: (1) top up tier-1 capacity for the next two hours from low-priority queues, (2) review the queue-routing rule that fired and tighten assignment by topic, and (3) ship a temporary auto-reply that sets the new first-response expectation while the rota catches up.

Sample event

{
  "support_escalations_per_day": [
    6,
    7,
    6,
    8,
    7,
    6,
    7,
    8,
    6,
    7,
    6,
    8,
    7,
    6,
    7,
    8,
    6,
    7,
    6,
    7,
    21
  ],
  "detected_value": 21
}

Stream: support_escalations_per_day · detected_value: 21

Drafted corrective-action plan

Cluster the escalated tickets by topic, loop engineering into the bridge, and refresh the tier-1 runbook for the dominant cluster.

1. Affected systems: Customer Support Operations — 21 tickets escalated to tier-2 today, well above the 6–8 daily baseline.
2. Root-cause hypothesis: a single defect cluster is generating a disproportionate share of escalations and the tier-1 runbook no longer catches it on first touch.
3. Next three steps: (1) cluster the escalated tickets by topic to identify the dominant defect, (2) loop engineering into the bridge so they see the same escalation cluster tier-2 sees, and (3) refresh the tier-1 runbook around the dominant cluster so the next occurrence routes back to first-touch resolution.

Sample event

{
  "support_csat_score_trend": [
    4.6,
    4.5,
    4.6,
    4.5,
    4.6,
    4.4,
    4.5,
    4.6,
    4.5,
    4.6,
    4.5,
    4.4,
    4.5,
    4.6,
    4.5,
    4.4,
    4.5,
    4.6,
    4.4,
    4.5,
    3.8
  ],
  "detected_value": 3.8
}

Stream: support_csat_score_trend · detected_value: 3.8

Drafted corrective-action plan

Pull the survey free-text for the slipping window, tag the recurring complaint, and add the pattern to the weekly review so it is not lost.

1. Affected systems: Customer Support Operations — CSAT score dropped to 3.8 over the rolling week, down from a 4.4–4.6 baseline.
2. Root-cause hypothesis: the first-response drift above stretched time-to-first-touch and dragged customer sentiment lower without any single catastrophic survey.
3. Next three steps: (1) pull the survey free-text for the slipping window and tag the recurring complaint, (2) tie the CSAT drop back to the upstream first-response-drift alert so the combined signal lands in the weekly review, and (3) schedule a CSAT retro once the response-time signal recovers so the team owns the sentiment impact downstream.

Sample event

{
  "support_ticket_backlog_age_hours": [
    12,
    10,
    11,
    9,
    10,
    12,
    11,
    10,
    9,
    11,
    12,
    10,
    11,
    9,
    10,
    12,
    11,
    10,
    9,
    11,
    58
  ],
  "detected_value": 58
}

Stream: support_ticket_backlog_age_hours · detected_value: 58

Drafted corrective-action plan

Page the on-call owner with the backlog aging flagged as CRITICAL, sweep the oldest slice first, and hold new low-priority intake until the oldest slice clears.

1. Affected systems: Customer Support Operations — average backlog age reached 58 hours, well past the 24-hour floor the team operates to.
2. Root-cause hypothesis: the combined upstream surge of ticket volume and first-response drift has slowed resolution faster than the queue can absorb new intake.
3. Next three steps: (1) page the on-call owner with the backlog-aging breach flagged as CRITICAL, (2) sweep the oldest backlog slice first to claw back SLA risk before new intake lands, and (3) hold new low-priority intake until the backlog recovers below the 24-hour floor so the breach does not recur into the next shift.