Datasets:

s2pidape
/

support-ticket-dataset

license: cc-by-4.0
language:
  - en
size_categories:
  - 1M<n<10M
task_categories:
  - tabular-classification
  - text-classification
task_ids:
  - multi-class-classification
  - intent-classification
tags:
  - synthetic
  - customer-support
  - counterfactual
  - causal-inference
  - cost-sensitive-learning
  - operations-research
  - tabular
pretty_name: Support Tickets with an AI-Automation Counterfactual
configs:
  - config_name: with_ai
    data_files: with_ai/*.parquet
  - config_name: humans_only
    data_files: humans_only/*.parquet

Support Tickets with an AI-Automation Counterfactual

1.5M synthetic support tickets in two matched worlds — one where an AI assistant handles part of the queue, one staffed entirely by humans. Same tickets, same customers, same attributes. Only the routing differs.

Most support datasets give you one world and leave you guessing about the other. This one gives you both, so questions like "what would this have cost without automation?" are measured rather than estimated.

from datasets import load_dataset

ai    = load_dataset("s2pidape/support-ticket-dataset", "with_ai")
human = load_dataset("s2pidape/support-ticket-dataset", "humans_only")

⚠️ This data is synthetic

I generated it with a simulator I wrote. Nothing here is evidence about real support desks. It is a sandbox for methods, and the honest limitations are listed near the bottom — please read them before deciding whether it fits your problem.

The two configs

Config	Rows	Cols	What it is
`with_ai`	1,500,000	39	Routed with an AI assistant. Carries the label `auto_resolved`. Staffed by 32 human agents + AI.
`humans_only`	1,500,000	38	The same tickets, same order, every attribute identical — routed to people only. Staffed by 44 agents.

The two differ in exactly three columns: assigned_team, assigned_to, resolved_by. Everything else — timings, SLA outcomes, CSAT, descriptions — is byte-identical, and ticket_id aligns row-for-row. with_ai additionally carries auto_resolved and escalated_to_human; humans_only carries escalated (identical in content to escalated_to_human).

44 → 32 agents is the counterfactual the pair encodes.

What you can do with it

Cost-sensitive learning. The main draw. Most classification datasets assume symmetric error costs. Here they are wildly asymmetric and measurable: a ticket the AI attempts and drops takes 30.5h to resolve versus 12.3h if a human had taken it from the start — so a false positive costs more than a true positive saves. Datasets that let you practice this are scarce.
Counterfactual / causal inference. Compute the effect of automation directly from the matched pair instead of estimating it.
A teaching set for "ML is not always the answer." A one-line rule (automate this issue type if it historically self-resolved ≥50%) scores 80.1% accuracy. A tuned LightGBM over text plus 20 features scores 80.4%. The label is stochastic, so ~80% is a genuine ceiling. Rare to be able to demonstrate that with real numbers.
Ordinary supervised tasks. Binary classification (auto_resolved), 100-class intent (issue_type_id), 5-class routing (assigned_team), regression on resolution_time_hours, SLA-breach prediction, queueing/survival analysis.

Schema

ticket_id · created_at · issue_description · category · issue_type_id · product · channel · region · language · customer_name · customer_email · customer_age · customer_gender · subscription_type · customer_tenure_months · previous_tickets · customer_segment · priority · issue_complexity_score · assigned_team · assigned_to · status · auto_resolved* · escalated_to_human* / escalated** · escalation_reason · reopen_count · first_response_time_hours · resolution_time_hours · paused_hours · resolution_wallclock_hours · first_resolved_at · resolved_at · closed_at · resolved_by · resolution_notes · sla_target_hours · sla_breached · sla_breach_margin · customer_satisfaction_score

_{* with_ai only · ** humans_only only}

Names and emails are generated, not real people. No PII.

Leakage warning

If you model auto_resolved, these columns are the answer and must be dropped: escalated_to_human, escalation_reason, assigned_team, assigned_to, resolved_by, resolution_notes, status, and every post-resolution timing/SLA/CSAT field. Train only on what exists at intake.

Also drop the 152,745 rows where auto_resolved is null — those tickets are still open. Their outcome is unknown, not negative; folding them into the negative class biases the model. That leaves 1,347,255 labelled rows, 47.7% positive.

Signal in the data

Field	Behaviour
`issue_complexity_score`	Strongest signal. Solve rate falls monotonically 91.6% (level 1) → 5.5% (level 10).
`issue_type_id`	100 types spanning 0.9% → 93.9% solve rate. No type is pure — none is 0% or 100%.
`category`	24.2% (Security) → 70.7% (Onboarding).
`priority`	23.9% (Urgent) → 69.1% (Low).
`product`, `channel`, `customer_segment`, `subscription_type`	Pure noise — all within 0.3pp of the 47.7% mean. Included deliberately as distractors.

Known limitations

Stated plainly so you can judge fit before downloading 176 MB:

issue_description is template-generated and one-to-one with issue_type_id. Intent classification scores a meaningless 100%; only ~2,500 distinct TF-IDF features exist across 1.4M documents. Do not use this for NLP benchmarking — real intent models run 70–85%.
resolution_notes has only 10 distinct values across 1.4M rows, and they are internal summaries rather than customer replies. There is no reply text, so nothing generative can be trained here.
Perfectly stationary. Solve rate sits between 0.475 and 0.480 across all twelve quarters. Train on 2022–23, test on 2024, and AUC moves by 0.0003. Useless for drift/concept-shift work.
Four columns carry no signal at all (see table above). Intentional, but know it going in.
The counterfactual file's timing columns are copied, not re-simulated. humans_only is valid for the headcount counterfactual (44 vs 32 agents) but its resolution_time_hours are the AI-world values. Do not read it as "how long humans would have taken."

Stats

Span: 2022-01-01 → 2024-12-30 (3.0 years), volume growing 28,122 → 54,975 per month
Automation rate 47.7% · escalation rate 30.5% · SLA breach 10.4% · reopen 11.4%
Mean CSAT 3.63/5 · median first response 0.45h · median resolution 1.82h
11 categories · 100 issue types · 6 products · 5 channels · 6 languages · 5 teams

Reference implementation

A full analysis pipeline built on this dataset — three-stage triage, guardrail, cost model, 26-check verification suite — is at github.com/s2pidape/ai-solvable, with a written business case.

Citation

@misc{support_tickets_ai_counterfactual,
  title  = {Support Tickets with an AI-Automation Counterfactual},
  author = {Talgat Kozahmetov},
  year   = {2026},
  url    = {https://huggingface.co/datasets/s2pidape/support-ticket-dataset}
}

License

CC BY 4.0 — free to use, share, and adapt, including commercially, with attribution.