Featuring Labeled Customer Emails and Support Responses

Network Diagram Tags

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3023333%2F9f9df25b75671db2d255b2d284c2c80c%2Fnetwork_diagram.svg?generation=1739380045025331&alt=media" alt="">

Discover the new, expanded version of this dataset with 20,000 ticket entries! Perfect for training models to classify and prioritize support tickets.

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

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 ...

Overview This dataset comprises detailed records of customer support tickets, providing valuable insights into various aspects of customer service operations. It is designed to aid in the analysis and modeling of customer support processes, offering a wealth of information for data scientists, machine learning practitioners, and business analysts.

Dataset Description The dataset includes the following features:

Ticket ID: Unique identifier for each support ticket. Customer Name: Name of the customer who submitted the ticket. Customer Email: Email address of the customer. Customer Age: Age of the customer. Customer Gender: Gender of the customer. Product Purchased: Product for which the customer has requested support. Date of Purchase: Date when the product was purchased. Ticket Type: Type of support ticket (e.g., Technical Issue, Billing Inquiry). Ticket Subject: Brief subject or title of the ticket. Ticket Description: Detailed description of the issue or inquiry. Ticket Status: Current status of the ticket (e.g., Open, Closed, Pending). Resolution: Description of how the ticket was resolved. Ticket Priority: Priority level of the ticket (e.g., High, Medium, Low). Ticket Channel: The Channel through which the ticket was submitted (e.g., Email, Phone, Web). First Response Time: Time taken for the first response to the ticket. Time to Resolution: Total time taken to resolve the ticket. Customer Satisfaction Rating: Customer satisfaction rating for the support received. Usage This dataset can be utilized for various analytical and modeling purposes, including but not limited to:

Customer Support Analysis: Understand trends and patterns in customer support requests, and analyze ticket volumes, response times, and resolution effectiveness. NLP for Ticket Categorization: Develop natural language processing models to automatically classify tickets based on their content. Customer Satisfaction Prediction: Build predictive models to estimate customer satisfaction based on ticket attributes. Ticket Resolution Time Prediction: Predict the time required to resolve tickets based on historical data. Customer Segmentation: Segment customers based on their support interactions and demographics. Recommender Systems: Develop systems to recommend products or solutions based on past support tickets. Potential Applications: Enhancing customer support workflows by identifying bottlenecks and areas for improvement. Automating the ticket triaging process to ensure timely responses. Improving customer satisfaction through predictive analytics. Personalizing customer support based on segmentation and past interactions. File information: The dataset is provided in CSV format and contains 8470 records and [number of columns] features.

Collection of 2229 support tickets manually classified into 7 categories, obtained from a IT support company in the Florianópolis (Brazil) region. Each ticket is represented by an unstructured text field, which is typed by the user that opened the call. The classification process was performed in 2020 by three IT support professionals. The corpus contains tickets in many languages, mainly English, German, Portuguese and Spanish.

All Personal Identifiable Information (PII) and sensitive information were removed (substituted by a tag indicating the original content, for instance: the sentence "this text was written by Leonardo" is converted to "this text was written by [NAME]"). The removal was performed in three steps: first, the automated machine learning-based tool AWS Comprehend PII Removal was used; then, a sequence of custom regular expressions was applied; last, the entire corpus was manually verified.

This dataset contains events from a ticketing management process of the help desk of an Italian software company. The process consists of 9 activities, and all cases start with the insertion of a new ticket into the ticketing management system. Each case ends when the issue is resolved and the ticket is closed. This log contains 3804 process instances (a.k.a "cases") and 13710 events

This dataset shows the number of Information Technology Help Desk Tickets and hours of work associated with them.

Console-AI/IT-helpdesk-synthetic-tickets dataset hosted on Hugging Face and contributed by the HF Datasets community

Information about Customer Information System (CIS) support tickets. The CIS System Functional Support team (CIS Admin “Help desk”) receive system issues (ex. billing issues, information requests, or system processing questions) affecting internal and external customers via email or phone call and number of tickets resolved same day as reported. Source data and published data updates monthly, however the dataset update job looks every week for the most recent monthly information. For this reason the Max Expected Data Age is 60 days.

Audio Retailer Customer Support Tickets

I created this dataset because I found other public customer support ticket datasets to have inconsistent labels.

  Dataset Details





  Dataset Description

This dataset contains synthetic customer support emails for a fictional multi-brand audio electronics retailer.The company sells real-world products from major brands (e.g. Bose, JBL, Apple, Sony) and offers fictional service plans like extended warranties or premium… See the full description on the dataset page: https://huggingface.co/datasets/h3en1x/audio-retailer-customer-support-tickets.

This dataset provides detailed records of telecom customer support tickets, including issue types, resolution timelines, agent actions, and customer satisfaction ratings. It enables process optimization, root cause analysis, and AI/ML chatbot training by offering granular insights into ticket lifecycles and outcomes.

Event log concerning the ticketing management process of the Help desk of an Italian software company

The Help Desk and Ticketing Software market is experiencing robust growth, driven by the increasing need for efficient customer service and streamlined internal communication across diverse industries. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the growing importance of providing seamless omnichannel support (email, chat, social media), and the increasing demand for automation and AI-powered features to improve response times and resolution rates. Businesses of all sizes, from small enterprises to large corporations, are increasingly reliant on these systems to manage customer inquiries, track issues, and improve overall operational efficiency. The competitive landscape is dynamic, with established players like Zendesk and Freshdesk alongside emerging innovative solutions vying for market share. The integration of these systems with CRM platforms and other business tools further enhances their value proposition, contributing to the overall growth trajectory. The forecast period (2025-2033) anticipates continued expansion, primarily driven by the increasing adoption in developing economies and the continuous advancements in artificial intelligence and machine learning capabilities within the software. Integration with emerging technologies like IoT and blockchain will further broaden the applications and market potential. While factors such as initial implementation costs and the need for specialized technical expertise can pose challenges, the overall long-term outlook remains positive, indicating a substantial increase in market value over the projected period. The segmentation of the market, encompassing features like on-premise vs. cloud deployment, pricing models, and target customer size (SMB vs. Enterprise), will continue to influence specific growth rates within different segments.

This dataset contains detailed records of IT service tickets, combining structured metadata (such as priority, category, and assignment) with rich ticket descriptions suitable for natural language processing. It enables automated ticket triage, prioritization, and advanced analytics for IT support operations, making it ideal for machine learning and process optimization.

Synthetic IT Support Tickets

This dataset contains {N} synthetic IT support tickets generated for testing and training ITSM systems like ServiceNow. Each ticket includes fields such as short_description, description, urgency, impact, category, assignment_group, and resolution.

  Example

{ "short_description": "Merchant unable to sign agreement", "description": "A merchant reported being unable to e-sign their agreement...", "urgency": 2, "impact": 1, "category":… See the full description on the dataset page: https://huggingface.co/datasets/6StringNinja/synthetic-servicenow-incidents.

The open-source ticketing system software market is experiencing robust growth, driven by increasing demand for flexible, cost-effective, and customizable solutions across various industries. The market's appeal stems from its ability to cater to specific business needs without the vendor lock-in associated with proprietary systems. Factors like rising adoption of cloud-based deployments, the need for improved customer service management in large enterprises and SMEs, and the growing preference for self-service options are fueling market expansion. While initial setup and customization might require technical expertise, the long-term cost savings and enhanced control offered by open-source options significantly outweigh these considerations. The market is highly fragmented, with numerous players offering diverse functionalities and support levels. This competitive landscape fosters innovation and provides organizations with a wide array of choices to select solutions perfectly aligned with their technical capabilities and budgetary constraints. The projected Compound Annual Growth Rate (CAGR) suggests a continuous upward trajectory, indicating sustained demand and potential for further market penetration in both established and emerging economies. Geographic expansion, particularly in regions with growing digital infrastructure and increasing IT spending, will likely contribute significantly to the market's overall growth in the coming years. The prominent players in the open-source ticketing system software market, such as Tidio, osTicket, Zammad, and others, are continually enhancing their offerings, adding new features, and improving user experience. The market is witnessing a trend towards integration with other business tools, improving workflow automation and data analytics capabilities. Furthermore, the increasing importance of data security and compliance is driving the adoption of robust security features in these systems. The market's future growth will be influenced by factors such as the evolution of cloud technologies, advancements in artificial intelligence and machine learning for improved customer support, and the growing adoption of open-source philosophies within organizations. While potential restraints exist, such as the need for in-house expertise for maintenance and customization, the overall market outlook remains positive, driven by the inherent advantages of flexibility, cost-effectiveness, and community support associated with open-source solutions.

IT Ticketing System Software Market Outlook

The global IT Ticketing System Software market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach around USD 6.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% during the forecast period. The market growth is primarily driven by the increasing demand for efficient IT service management solutions and the rising adoption of cloud-based services across various industries.

The growth of the IT Ticketing System Software market can be attributed to several key factors. Firstly, the increasing complexity of IT environments in organizations necessitates robust ticketing systems to manage and resolve technical issues efficiently. This complexity is often a result of the digital transformation initiatives that many companies are undertaking, which involve integrating numerous software applications and hardware devices. Consequently, there is a growing need for advanced IT ticketing systems to streamline IT support processes and enhance operational efficiency.

Another significant growth factor is the rising adoption of automation and artificial intelligence (AI) in IT service management. Modern IT ticketing systems are increasingly incorporating AI and machine learning capabilities to automate routine tasks, such as ticket categorization, prioritization, and resolution. These advancements not only improve the efficiency of IT support teams but also enhance the overall user experience by reducing response times and ensuring quicker issue resolution.

Moreover, the growing emphasis on customer-centric IT services is driving the demand for advanced ticketing systems. Organizations are increasingly recognizing the importance of delivering high-quality IT support services to maintain customer satisfaction and loyalty. IT ticketing systems play a crucial role in achieving this by providing a structured approach to managing and resolving customer issues. Additionally, the integration of IT ticketing systems with other customer relationship management (CRM) and enterprise resource planning (ERP) systems further enhances their functionality and value.

Helpdesk Automation is becoming an integral part of modern IT service management strategies. By leveraging automation, organizations can significantly reduce the manual workload on IT support teams, allowing them to focus on more complex and strategic tasks. Automated helpdesk solutions can handle routine inquiries, ticket categorization, and even initial troubleshooting steps, thereby improving response times and customer satisfaction. As businesses continue to grow and expand their IT infrastructure, the need for scalable and efficient helpdesk automation tools becomes increasingly critical. This trend is particularly evident in industries that experience high volumes of support requests, where automation can lead to substantial cost savings and efficiency gains.

From a regional perspective, North America is expected to dominate the IT Ticketing System Software market during the forecast period. The region's strong presence of leading market players, coupled with the high adoption rate of advanced IT solutions, contributes to its significant market share. Additionally, the increasing focus on enhancing IT infrastructure and the rising demand for cloud-based services further boost the market growth in North America. On the other hand, the Asia Pacific region is anticipated to witness the highest growth rate, driven by the rapid digitalization of businesses, growing IT investments, and the increasing adoption of cloud technologies in countries like China and India.

Component Analysis

The IT Ticketing System Software market is segmented into two primary components: software and services. The software segment includes the various IT ticketing solutions available, ranging from basic ticket management systems to advanced platforms with integrated AI and automation capabilities. The services segment encompasses the support, maintenance, and consulting services provided to ensure the effective deployment and operation of IT ticketing systems.

The software segment is expected to hold the largest share of the market during the forecast period. This dominance can be attributed to the continuous advancements in software technologies and the increasing availability of feature-rich IT ticketing solutions. Modern IT ticketing software offers a wide range of functionalities, including au

Hashiru11/support-tickets-telecommunication dataset hosted on Hugging Face and contributed by the HF Datasets community

The global online help desk market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions and the expanding need for efficient customer service across diverse sectors. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors. The rise of e-commerce necessitates sophisticated customer support systems, while banks and financial institutions leverage online help desks to enhance security and regulatory compliance. Furthermore, the shift towards remote work models has increased the demand for accessible and reliable customer support solutions. Key trends include the integration of AI-powered chatbots for improved response times and personalized support, the increasing adoption of omnichannel support strategies, and the growing focus on self-service options to reduce support tickets. However, challenges remain, including the need for robust cybersecurity measures to protect sensitive customer data and the ongoing need for skilled personnel to manage and maintain these complex systems. The market is segmented by application (e-commerce, banking, others) and deployment type (on-premises, cloud-based), with the cloud-based segment dominating due to its scalability, cost-effectiveness, and accessibility. Major players like Zendesk, Groove, HappyFox, Freshdesk, Atlassian, and Zoho are actively shaping market dynamics through innovation and strategic acquisitions. The geographical distribution of the market demonstrates strong growth across all regions, but North America and Europe currently hold the largest market shares due to higher technology adoption rates and established business infrastructure. However, the Asia-Pacific region is poised for significant expansion in the coming years, driven by rapid economic growth and increasing internet penetration. The competitive landscape is dynamic, characterized by both established players and emerging startups, leading to continuous innovation and the development of more sophisticated and user-friendly online help desk solutions. The market's future growth will depend heavily on technological advancements, regulatory changes, and the evolving customer expectations for seamless and efficient support. Continued investment in AI and machine learning will be crucial for enhancing the capabilities of online help desk systems and meeting the growing demands for personalized and proactive support.

The help desk ticketing software market is experiencing robust growth, driven by the increasing need for efficient customer service and streamlined internal support processes across diverse industries. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This growth is fueled by several key trends, including the rising adoption of cloud-based solutions, the increasing demand for integrated omnichannel support (email, chat, social media), and the growing focus on improving customer satisfaction metrics through quicker resolution times and personalized experiences. Businesses are increasingly recognizing the value of sophisticated ticketing systems to manage support requests effectively, improve agent productivity, and gain valuable insights into customer issues. The market is segmented by deployment (cloud, on-premise), business size (small, medium, large enterprises), and industry vertical (e.g., IT, healthcare, finance). While the competitive landscape is crowded, with established players like Zendesk and Freshdesk alongside emerging innovative solutions, the market offers substantial opportunities for both incumbents and new entrants. The increasing complexity of IT infrastructure and the need for proactive support are further driving market expansion. Factors such as high initial investment costs for comprehensive systems, the need for specialized technical expertise for implementation and maintenance, and potential integration challenges with existing business systems can act as restraints to market growth. However, the benefits of improved customer satisfaction, increased operational efficiency, and reduced support costs significantly outweigh these challenges. The market is expected to see continued innovation in areas such as AI-powered chatbots, automated ticket routing, and predictive analytics, further enhancing the capabilities of help desk ticketing software and broadening its appeal across various organizations. The ongoing digital transformation across industries will continue to be a significant driver of market growth in the coming years.

"Percent of Total Tickets Resolved by the Service Desk"