AI Customer Support Escalation Monitor

This dataset provides granular, timestamped records of AI-powered customer support escalations, including escalation reasons, agent interventions, resolution outcomes, and compliance flags. It enables managers to analyze operational patterns, identify bottlenecks, and monitor regulatory adherence for improved support efficiency and customer experience.

  • last updated 9 Dec 2025
  • by GoMask
The brief that made it

Operational analytics for AI customer support escalation patterns

Sample rows

preview · 8 of 85 rows · all 20 columns
escalation_idstringpriority_levelstringai_confidence_scorefloatcompliance_flagbooleanescalation_reasonstringrequest_idstringcustomer_idstringcustomer_namestringcustomer_emailstringrequest_datetimedatetimeescalation_datetimedatetimeagent_idstringagent_namestringintervention_datetimedatetimeresolution_datetimedatetimeresolution_statusstringresolution_notesstringtotal_resolution_time_minutesintegerservice_channelstringescalation_notesstring
ESC-1A2B3C4Dmedium0.63falsecomplexityREQ-1001CUS-001Jenna Moralesjenna.morales@acme.com2024-06-01T09:17:00Z2024-06-01T09:24:00ZAGT-021Sean Lin2024-06-01T09:26:00Z2024-06-01T09:43:00ZresolvedAgent clarified issue and provided solution.19chatAI unable to classify request due to ambiguous phrasing.
ESC-2F4G8H6Jhigh0.91truecomplianceREQ-1002CUS-002Marcus Fieldsmarcus.fields@innovate.tech2024-06-01T10:02:00Z2024-06-01T10:08:00ZAGT-014Grace Patel2024-06-01T10:10:00Z2024-06-01T10:55:00ZresolvedCompliance check completed, data not shared.47emailSensitive data sharing request flagged for review.
ESC-3J7K9L2Mmedium0.78falsesentimentREQ-1003CUS-003Evelyn Tranevelyn.tran@cloudify.io2024-06-01T11:15:00Z2024-06-01T11:18:00ZAGT-009Linda Gomez2024-06-01T11:20:00Z2024-06-01T11:39:00ZresolvedAgent provided reassurance and resolved issue.21chatCustomer expressed frustration in chat.
ESC-4N5O6P7Qhigh0.34falsesystem_errorREQ-1004CUS-004Aidan McCarthyaidan.m@stellarbank.com2024-06-01T12:03:00Z2024-06-01T12:15:00ZAGT-012Sofia Ivanova2024-06-01T12:18:00Z2024-06-01T13:06:00ZresolvedAgent retried request and resolved error.51webRequest processing failed due to backend error.
ESC-5R6S8T1Umedium0.59falsecomplexityREQ-1005CUS-005Priya Singhpriya.singh@healthplus.org2024-06-01T13:29:00Z2024-06-01T13:35:00ZAGT-018William Chen2024-06-01T13:37:00Z2024-06-01T14:16:00ZresolvedAgent clarified billing and resolved issue.41phoneComplex billing query not understood by AI.
ESC-6V7W8X9Yhigh0.82falsesentimentREQ-1006CUS-006Liam O'Haraliam.ohara@fastship.net2024-06-01T14:57:00Z2024-06-01T15:03:00ZAGT-015Emily Rivera2024-06-01T15:05:00Z2024-06-01T15:29:00ZresolvedAgent addressed concerns and de-escalated.26phoneNegative sentiment detected in call transcript.
ESC-7Z1A2B3Ccritical0.95truecomplianceREQ-1007CUS-007Olivia Carterolivia.carter@greengrids.com2024-06-01T16:20:00Z2024-06-01T16:22:00ZAGT-003Brian Nguyen2024-06-01T16:25:00Z2024-06-01T17:03:00ZresolvedContract reviewed, compliance confirmed.41emailRequest for confidential contract review.
ESC-8D4E5F6Gmedium0.42falsesystem_errorREQ-1008CUS-008Ruby Wallaceruby.wallace@artify.com2024-06-01T17:09:00Z2024-06-01T17:11:00ZAGT-011Carlos Jimenez2024-06-01T17:14:00Z2024-06-01T17:32:00ZresolvedAgent retried upload, issue resolved.21webFile upload failed due to server timeout.

What the 85 rows show

from the 85-row sample

Critical (priority level) stands out: 13 of its 13 rows have compliance_flag = true, against 3 of 72 for the rest.

  • 19%compliance_flag = true
  • 0.71median ai_confidence_score
  • 2resolution statuses
  • 4service channels
  • 27median total_resolution_time_minutes
Compliance flag rate by priority_levelcompliance_flag = true
0%50%100%0%low0 of 73%medium1 of 377%high2 of 28100%critical13 of 13

Critical (priority level)'s mean ai_confidence_score is 0.93, against 0.56 for low, 0.63 for medium and 0.65 for high.

ai_confidence_score85 rows, in bands of 0.1
081636810151615120.20.61ai_confidence_score →

Median 0.71, from 0.27 to 0.99.

escalation_reason85 rows · 5 values
  1. complexity24
  2. system_error20
  3. sentiment19
  4. compliance16
  5. other6
20 columns by typefrom the column list below
  • string 13
  • integer 1
  • float 1
  • datetime 4
  • boolean 1

Columns

20 columns in four groups
blueprint · 20 columns
columntypedescriptionexample
Text 13 columns
escalation_idstringUnique identifier for each escalation eventuniqueESC-1A2B3C4D
request_idstringUnique identifier for the original customer service requestREQ-1001
customer_idstringUnique identifier for the customer who initiated the requestCUS-001
customer_namestringFull name of the customeroptionalJenna Morales
customer_emailstringEmail address of the customeroptionaljenna.morales@acme.com
escalation_reasonstringPrimary reason for escalation (e.g., compliance, complexity, sentiment)compliance · complexity · sentiment · system_error · othercomplexity
escalation_notesstringDetailed notes or comments about the escalationoptionalAI unable to classify req…
agent_idstringUnique identifier for the human agent who intervenedoptionalAGT-021
agent_namestringFull name of the human agent who handled the escalationoptionalSean Lin
resolution_statusstringFinal status of the escalation (e.g., resolved, unresolved, pending)resolved · unresolved · pendingresolved
resolution_notesstringAdditional notes or comments about the resolutionoptionalRecord sent by agent.
service_channelstringChannel through which the request was received (e.g., chat, email, phone)chat · email · phone · web · otherchat
priority_levelstringPriority level assigned to the escalation (e.g., low, medium, high, critical)low · medium · high · critical · optionalmedium
Numbers 2 columns
ai_confidence_scorefloatConfidence score (0-1) from the AI system regarding the escalation decision0 to 1 · optional0.63
total_resolution_time_minutesintegerTotal time in minutes taken to resolve the escalation from escalation to resolution0 or more19
Dates and times 4 columns
request_datetimedatetimeTimestamp when the original customer request was created2024-06-01T09:17:00Z
escalation_datetimedatetimeTimestamp when the request was escalated by the AI system2024-06-01T09:24:00Z
intervention_datetimedatetimeTimestamp when the human agent started handling the escalationoptional2024-06-01T09:26:00Z
resolution_datetimedatetimeTimestamp when the escalation was resolved2024-06-01T09:43:00Z
True or false 1 column
compliance_flagbooleanIndicates if the escalation involved a compliance issueoptionalfalse

Use it for

  • compliance flag19%16 of 85 rowsmean ai confidence sc…0.56low0.63medi…0.65high0.93crit…

    A technology dashboard

    The compliance_flag rate, ai_confidence_score by priority_level and a breakdown of escalation_reason. Excel, Power BI or Tableau.

  • Why do 16 of 85 rows have compliance_flag = true?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable escalations with request_id, customer_id and customer_name to fill a screen in front of a buyer.

Not quite right?

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blueprint · ai-customer-support-escalation-monitor

Behind this dataset

Same schema. As many rows as you need.

These 85 rows came out of a blueprint — 20 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Each escalation must include a clear trigger reason.
  • Escalation timestamps must precede resolution timestamps.
  • Mandatory agent intervention for escalations flagged as compliance-related.
  • Escalation must track both AI and human involvement stages.
  • All resolved escalations must log resolution outcome.
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
ai-customer-support-escalation-monitor

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