Customer Support Ticket Resolution

This dataset provides comprehensive logs of customer support tickets, including detailed issue descriptions, resolution times, outcomes, agent involvement, and customer satisfaction ratings. It enables analysis of support process efficiency, identification of bottlenecks, and development of NLP models for automated ticket classification and resolution prediction.

  • opened 25 times
  • last updated 21 Aug 2025
  • by GoMask
The brief that made it

Support process optimization and bottleneck identification

Sample rows

preview · 8 of 200 rows · all 17 columns
ticket_idstringresolution_outcomestringresolution_time_hoursfloatfollow_up_requiredbooleanticket_statusstringcustomer_idstringcustomer_namestringcustomer_emailstringticket_created_atdatetimeticket_closed_atdatetimeissue_categorystringresolved_by_agent_idstringresolved_by_agent_namestringcustomer_satisfaction_ratingintegerpriority_levelstringissue_descriptionstringresolution_summarystring
ABZ71QJ8unresolvedblanktrueopenCUST001Javier Martinezjmartinez@empresa.com2024-03-01T09:14:00ZblankbillingblankblankblankhighInvoice amount appears incorrect for March, please clarify charges.blank
K7Q2LMNPresolved0.75falseresolvedCUST002Samantha Leesam.lee@gmail.com2024-03-02T15:20:00Z2024-03-02T16:05:00ZaccountAGT101Priya Patel5mediumCannot reset password, link expired before use.Password reset link regenerated and sent successfully.
3FZ7QW8TunresolvedblanktruependingCUST003Ahmed Al-Sayedahmed@dxbtech.ae2024-03-02T11:45:00ZblankbillingblankblankblankurgentRefund not processed for cancelled subscription.blank
XQ8V4JLKresolved26.88falseclosedCUST004Anna Kowalskaa.kowalska@wp.pl2024-03-03T10:22:00Z2024-03-04T13:15:00ZaccountAGT104David Kim4lowUnable to update mailing address on profile page.Manual update performed in backend, customer notified.
P4H8LQW2unresolvedblanktrueopenCUST005Chloe Zhangchloe.zhang@alibaba.com2024-03-04T09:00:00ZblankbillingblankblankblankhighRecurring payment charged twice for same month.blank
N9K3XTR6resolved5.5falseresolvedCUST006Lucas Ferrarilucas.ferrari@mail.com2024-03-04T13:10:00Z2024-03-04T18:40:00ZtechnicalAGT105Maria Garcia5highApp crashes during login on Android devices.Latest app update resolved login crash issue.
R8M1QJ2Presolved21.75falseclosedCUST007Olga Ivanovaivanova.olga@skmail.ru2024-03-05T11:45:00Z2024-03-06T09:30:00ZaccountAGT109Chen Li4mediumTwo-factor authentication not working after phone update.2FA reset and tested for customer, issue resolved.
WB8N7KQ2escalatedblanktrueescalatedCUST008John Smithjohn.smith@smithcorp.com2024-03-05T14:00:00ZblanktechnicalblankblankblankurgentNetwork connectivity issues at branch locations.blank

What the 200 rows show

from the 200-row sample

Unresolved (resolution outcome) stands out: 69 of its 69 rows have follow_up_required = true, against 22 of 131 for the rest.

  • 46%follow_up_required = true
  • 0.93median resolution_time_hours
  • 3issue categories
  • 4priority levels
  • 5median customer_satisfaction_rating
Follow up required rate by resolution_outcomefollow_up_required = true
0%50%100%5%resolved6 of 115100%unresolved69 of 69100%escalated16 of 16
resolution_time_hours115 rows, in bands of 10
045908510100510404080resolution_time_hours →

Median 0.93, from 0.0 to 75.4.

ticket_status200 rows · 5 values
  1. resolved64
  2. closed51
  3. open40
  4. pending29
  5. escalated16
17 columns by typefrom the column list below
  • string 12
  • integer 1
  • float 1
  • datetime 2
  • boolean 1

Columns

17 columns in four groups
blueprint · 17 columns
columntypedescriptionexample
Text 12 columns
ticket_idstringUnique identifier for the support ticketuniqueABZ71QJ8
customer_idstringUnique identifier for the customer who raised the ticketCUST001
customer_namestringFull name of the customerJavier Martinez
customer_emailstringEmail address of the customerjmartinez@empresa.com
ticket_statusstringCurrent status of the ticketopen · pending · resolved · closed · escalatedopen
issue_categorystringCategory of the issue reported (e.g., billing, technical, account)3 categoriesbilling
issue_descriptionstringDetailed description of the issue provided by the customerInvoice amount appears in…
resolution_summarystringSummary of how the issue was resolvedoptionalPassword reset link regen…
resolved_by_agent_idstringIdentifier for the support agent who resolved the ticketoptionalAGT101
resolved_by_agent_namestringFull name of the support agent who resolved the ticketoptionalPriya Patel
resolution_outcomestringOutcome of the ticket resolutionresolved · unresolved · escalated · duplicate · invalidunresolved
priority_levelstringPriority assigned to the ticketlow · medium · high · urgenthigh
Numbers 2 columns
resolution_time_hoursfloatTotal time taken to resolve the ticket, in hours0 or more · optional0.75
customer_satisfaction_ratingintegerCustomer's satisfaction rating after resolution (1-5)1 to 5 · optional5
Dates and times 2 columns
ticket_created_atdatetimeTimestamp when the ticket was created2024-03-01T09:14:00Z
ticket_closed_atdatetimeTimestamp when the ticket was resolved or closedoptional2024-03-02T16:05:00Z
True or false 1 column
follow_up_requiredbooleanIndicates if a follow-up is required for this ticketoptionaltrue

Use it for

  • follow up requ…46%91 of 200 rowsfollow up required by…5%resolv…100%unreso…100%escala…

    A technology dashboard

    The follow_up_required rate, resolution_time_hours by resolution_outcome and a breakdown of ticket_status. Excel, Power BI or Tableau.

  • Why do 91 of 200 rows have follow_up_required = 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 tickets with customer_id, customer_name and customer_email to fill a screen in front of a buyer.

Not quite right?

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This dataset200 rows17 columns
Yours10,000 rows17 columns

blueprint · customer-support-ticket-resolution

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 17 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
  • Ticket type selected from 10 categories
  • Resolution time 5m-5d
  • Status: resolved, escalated, pending
  • Customer satisfaction score 1-5
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
customer-support-ticket-resolution

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