pngwn/system-one-decisions

Featuring Labeled Customer Emails and Support Responses

🔧 Synthetic IT Ticket Generator — Custom Dataset

Create a dataset tailored to your own queues & priorities (no PII).

👉 Generate custom data

• Define your queues, priorities, language

Need an on-prem AI to auto-classify tickets?
→ Open Ticket AI

There are 2 Versions of the dataset, the new version has more tickets, but only languages english and german. So please look at both files, to find what best fits your needs. Checkout my Open Source Customer Support AI: Open Ticket AI

Definetly check out my other Dataset:
Tickets from Github Issues

>It includes priorities, queues, types, tags, and business types. This preview offers a detailed structure with classifications by department, type, priority, language, subject, full email text, and agent answers.

Features / Attributes

Field	Description	Values
🔀 Queue	Specifies the department to which the email ticket is routed	e.g. Technical Support, Customer Service, Billing and Payments, ...
🚦 Priority	Indicates the urgency and importance of the issue	🟢Low 🟠Medium 🔴Critical
🗣️ Language	Indicates the language in which the email is written	EN, DE
Subject	Subject of the customer's email
Body	Body of the customer's email
Answer	The response provided by the helpdesk agent
Type	The type of ticket as picked by the agent	e.g. Incident, Request, Problem, Change ...
🏢 Business Type	The business type of the support helpdesk	e.g. Tech Online Store, IT Services, Software Development Company
Tags	Tags/categories assigned to the ticket, split into ten columns in the dataset	e.g. "Software Bug", "Warranty Claim"

Queue

Specifies the department to which the email ticket is categorized. This helps in routing the ticket to the appropriate support team for resolution.

• 💻 Technical Support: Technical issues and support requests.
• 🈂️ Customer Service: Customer inquiries and service requests.
• 💰 Billing and Payments: Billing issues and payment processing.
• 🖥️ Product Support: Support for product-related issues.
• 🌐 IT Support: Internal IT support and infrastructure issues.
• 🔄 Returns and Exchanges: Product returns and exchanges.
• 📞 Sales and Pre-Sales: Sales inquiries and pre-sales questions.
• 🧑‍💻 Human Resources: Employee inquiries and HR-related issues.
• ❌ Service Outages and Maintenance: Service interruptions and maintenance.
• 📮 General Inquiry: General inquiries and information requests.

Priority

Indicates the urgency and importance of the issue. Helps in managing the workflow by prioritizing tickets that need immediate attention.

• 🟢 1 (Low): Non-urgent issues that do not require immediate attention. Examples: general inquiries, minor inconveniences, routine updates, and feature requests.
• 🟠 2 (Medium): Moderately urgent issues that need timely resolution but are not critical. Examples: performance issues, intermittent errors, and detailed user questions.
• 🔴 3 (Critical): Urgent issues that require immediate attention and quick resolution. Examples: system outages, security breaches, data loss, and major malfunctions.

Language

Indicates the language in which the email is written. Useful for language-specific NLP models and multilingual support analysis.

• en (English)
• de (German)

Answer

The response provided by the helpdesk agent, containing the resolution or further instructions. Useful for analyzing the quality and effectiveness of the support provided.

Types

Different types of tickets categorized to understand the nature of the requests or issues.

• ❗ Incident: Unexpected issue requiring immediate attention.
• 📝 Request: Routine inquiry or service request.
• ⚠️ Problem: Underlying issue causing multiple incidents.
• 🔄 Change: Planned change or update.

Tags

Tags/categories assigned to the ticket to further classify and identify common issues or topics.

• Examples: "Product Support," "Technical Support," "Sales Inquiry."

Use Cases

Task	Description
Text Classification	Train machine learning models to accurately classify email content into appropriate departments, improving ticket routing and handling.
Priority Prediction	Develop algorithms to predict the urgency of emails, ensuring that critical issues are addressed promptly.
Customer Support Analysis	Analyze the dataset to gain insights into common customer issues, optimize support processes, and enhance overall service quality.

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More Information

Other Datasets can also be found on Kaggle, including a Multi Language Ticket Dataset, which also has French, Spanish and Portuguese Tickets.

Created By

Softoft