Finance-Technology Support Interactions

This dataset provides detailed logs of support ticket interactions for financial technology products, capturing issue types, escalation events, resolution times, agent assignments, and customer satisfaction ratings. It enables in-depth analysis of support efficiency, automation opportunities, and escalation trends, making it valuable for optimizing customer service operations and product support strategies.

  • last updated 29 Jul 2025
  • by GoMask
The brief that made it

Analyzing support team performance and resolution efficiency

Sample rows

preview · 8 of 200 rows · all 21 columns
ticket_idstringstatusstringescalation_levelintegerescalatedbooleanassigned_agent_idstringsubmitted_datetimedatetimecustomer_idstringcustomer_namestringcustomer_emailstringproduct_idstringproduct_namestringissue_typestringprioritystringassigned_agent_namestringescalation_datetimedatetimeresolution_datetimedatetimeresolution_time_minutesintegercustomer_satisfaction_ratingintegerfollow_up_requiredbooleanissue_descriptionstringresolution_summarystring
TK00000001resolved0falseAGT00122024-03-04T09:22:00CUS000101Patricia Leepatlee99@example.comPROD001FinFlow Mobile BankingloginhighOwen Fisherblank2024-03-04T10:07:00455falseUnable to log in using Face ID after app update.Face ID reset and login functionality restored.
TK00000002escalated2trueAGT00212024-03-05T13:41:00CUS001203Samantha Patelspatel@example.netPROD002PayStream WallettransactionurgentRachel Kim2024-03-05T14:05:00blankblankblanktrueTransaction failed but amount debited from account.blank
TK00000003in_progress0falseAGT00032024-03-05T15:12:00CUS000309Jonathan Wujwu_309@example.comPROD003InvestSuite ProaccountmediumDavid NguyenblankblankblankblankfalseCannot update beneficiary details in profile.blank
TK00000004resolved1trueAGT00152024-03-06T08:03:00CUS001055Linda Walkerlwalker55@example.comPROD002PayStream WalletbillinghighJordan Evans2024-03-06T08:44:002024-03-06T09:12:00694falseMonthly subscription charged twice in March.Refund processed for duplicate charge.
TK00000005in_progress0falseAGT00202024-03-07T10:48:00CUS000802Miguel Sanchezmiguel.sanchez@example.orgPROD005SafeTransacttechnicalurgentPriya ChawlablankblankblankblanktrueApp crashes on opening after system update.blank
TK00000006open0falseblank2024-03-07T12:30:00CUS001303Harriet Olsenh.olsen@example.comPROD004LoanManager LiteaccountmediumblankblankblankblankblankfalseUnable to close loan account online.blank
TK00000007resolved0falseAGT00122024-03-07T14:01:00CUS000203Jessica Reedjessica.reed@example.netPROD001FinFlow Mobile BankingloginhighOwen Fisherblank2024-03-07T14:47:00464falsePassword reset email not received after multiple attempts.Manual password reset completed and email verified.
TK00000008open0falseblank2024-03-08T11:36:00CUS001201Yusuf Hassanyhassan@example.comPROD006QuickPay ExpresstransactionmediumblankblankblankblankblankfalseTransfer to external bank delayed over 24 hours.blank

What the 200 rows show

from the 200-row sample

Escalated status stands out: 32 of its 32 rows have escalated = true, against 8 of 168 for the rest.

  • 20%escalated = true
  • 0median escalation_level
  • 4priorities
  • 6issue types
  • 28products
  • 28product names
Escalated rate by statusescalated = true
0%50%100%0%open0 of 330%in_pro…0 of 54100%escala…32 of 3214%resolv…8 of 580%closed0 of 23
escalation_level200 rows, in bands of 0.5
0801601600200173escalated from 1013escalation_level →

Every row with escalation_level of 1 or more has escalated = true. None below it.

assigned_agent_id167 rows with a value · 33 left blank
  1. AGT002112
  2. AGT002210
  3. AGT00127
  4. AGT00096
  5. AGT00086
  6. AGT00236
  7. AGT00396
  8. AGT20016
  9. AGT20036
  10. AGT20066
21 columns by typefrom the column list below
  • string 13
  • integer 3
  • datetime 3
  • boolean 2

Columns

21 columns in four groups
blueprint · 21 columns
columntypedescriptionexample
Text 13 columns
ticket_idstringUnique identifier for the support ticketuniqueTK00000001
customer_idstringUnique identifier for the customer submitting the ticketCUS000101
customer_namestringFull name of the customer submitting the ticketoptionalPatricia Lee
customer_emailstringEmail address of the customeroptionalpatlee99@example.com
product_idstringIdentifier for the financial technology product related to the ticketPROD001
product_namestringName of the financial technology productoptionalFinFlow Mobile Banking
issue_typestringCategory of the issue reported (e.g., login, transaction, technical, account)6 valueslogin
issue_descriptionstringDetailed description of the issue reported by the customerUnable to log in using Fa…
prioritystringPriority level assigned to the ticketlow · medium · high · urgenthigh
statusstringCurrent status of the ticketopen · in_progress · escalated · resolved · closedresolved
assigned_agent_idstringIdentifier for the support agent assigned to the ticketoptionalAGT0012
assigned_agent_namestringName of the support agent assigned to the ticketoptionalOwen Fisher
resolution_summarystringSummary of how the issue was resolvedoptionalFace ID reset and login f…
Numbers 3 columns
escalation_levelintegerLevel to which the ticket was escalated (0 = not escalated, 1 = tier 1, 2 = tier 2, etc.)0 or more · optional0
resolution_time_minutesintegerTime taken to resolve the ticket, in minutes0 or more · optional45
customer_satisfaction_ratingintegerCustomer satisfaction rating for the support interaction (1-5 scale)1 to 5 · optional5
Dates and times 3 columns
submitted_datetimedatetimeDate and time when the support ticket was created2024-03-04T09:22:00
escalation_datetimedatetimeDate and time when the ticket was escalated (if applicable)optional2024-03-05T14:05:00
resolution_datetimedatetimeDate and time when the ticket was resolvedoptional2024-03-04T10:07:00
True or false 2 columns
escalatedbooleanIndicates if the ticket was escalated to a higher support tierfalse
follow_up_requiredbooleanIndicates if a follow-up is required for the ticketoptionalfalse

Use it for

  • escalated20%40 of 200 rowsmean escalation level…0.0open0.0in_p…1.6esca…0.19reso…

    A finance dashboard

    The escalated rate, escalation_level by status and a breakdown of assigned_agent_id. Excel, Power BI or Tableau.

  • Why do 40 of 200 rows have escalated = 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 submitted_datetime, customer_id and customer_name to fill a screen in front of a buyer.

Not quite right?

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This dataset200 rows21 columns
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blueprint · finance-technology-support-interactions

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 21 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 ticket assigned severity level
  • Resolution time tracked in hours
  • Escalations linked to ticket IDs
  • Support channels (email, chat) recorded
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
finance-technology-support-interactions

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