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Helpdesk Ticket Classification Dataset
Data Science and Analytics
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About
This dataset features labeled customer emails and their corresponding support responses, designed to assist in training machine learning models. It provides a detailed structure for classifying and prioritising support tickets, including classifications by department, type, priority, language, subject, full email text, and agent answers. It is ideal for developing algorithms to predict email urgency, automate ticket routing, and gain insights into common customer issues to enhance service quality.
Columns
- subject: The customer's email subject.
- body: The full text of the customer's email.
- answer: The response provided by the helpdesk agent, containing the resolution or further instructions.
- type: The category of the ticket as determined by the agent (e.g., Incident, Request, Problem, Change).
- queue: Specifies the department to which the email ticket is routed (e.g., Technical Support, Customer Service, Billing and Payments, Product Support, IT Support, Returns and Exchanges, Sales and Pre-Sales, Human Resources, Service Outages and Maintenance, General Inquiry).
- priority: Indicates the urgency and importance of the issue, with levels such as Low (non-urgent), Medium (moderately urgent), and Critical (urgent issues requiring immediate attention).
- language: The language in which the email is written (e.g., English, German, Spanish, French, Portuguese).
- version: An identifier for the dataset version, though its specific relevance is noted as low.
- tag_1 to tag_8: Tags or categories assigned to the ticket to further classify common issues or topics (e.g., "Software Bug", "Warranty Claim", "Security", "Performance", "IT", "Tech Support").
Distribution
The dataset is provided in CSV format and includes approximately 28,600 valid records. The file size is 26 MB. An expanded version of this dataset is available with 20,000 ticket entries.
Usage
This dataset is suitable for various applications, including:
- Text Classification: Training machine learning models to accurately classify email content into appropriate departments, improving ticket routing and handling.
- Priority Prediction: Developing algorithms to predict the urgency of emails, ensuring critical issues are addressed promptly.
- Customer Support Analysis: Analysing the dataset to gain insights into common customer issues, optimise support processes, and enhance overall service quality.
Coverage
The dataset primarily covers customer support interactions, with emails predominantly in English (en) and German (de). While other languages like Spanish (ES), French (FR), and Portuguese (PT) are listed as possible values for the language field, the provided sample data suggests the majority of tickets are in English and German. There is no specific geographic, time range, or demographic scope detailed within the dataset description.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
This dataset is ideal for:
- Data scientists and machine learning engineers looking to train models for automated ticket classification, priority prediction, and natural language processing in customer support contexts.
- Business analysts seeking to understand customer pain points, identify trends in support requests, and improve customer service operations.
- Customer support managers interested in optimising workflow, resource allocation, and agent response effectiveness.
Dataset Name Suggestions
- Customer Email Support Tickets
- Helpdesk Ticket Classification Dataset
- Multi-Language IT Support Emails
- Prioritised Customer Service Enquiries
Attributes
Original Data Source: Helpdesk Ticket Classification Dataset
QUALITY