Overview

Synthetic tabular dataset of 50,000 support tickets from 25 companies used to study priority classification (low, medium, high). Companies differ by size and industry; large companies operate across multiple regions. Features mix numeric and categorical signals commonly available at ticket intake. Data is fully artificial—no real users, systems, or proprietary logs.

Intended use: benchmarking supervised learning for tabular classification (e.g., Gradient Boosting, XGBoost, LightGBM, AdaBoost, SVM, Naive Bayes), feature engineering, handling mixed types, class imbalance, and mild label noise.

File & schema

Identifiers & time

-ticket_id (int64): unique ticket identifier (randomized order)

-day_of_week (Mon–Sun), day_of_week_num (1–7; Mon=1)

Company profile (replicated per row)

-company_id (int), company_size (Small/Medium/Large + _cat),

-industry (7 categories + _cat),

-customer_tier (Basic/Plus/Enterprise + _cat),

-org_users (int): active user seats (Large up to ~10,000)

Context

-region (AMER/EMEA/APAC + _cat)

-past_30d_tickets (int), past_90d_incidents (int)

Product & channel

-product_area (auth, billing, mobile, data_pipeline, analytics, notifications + _cat)

-booking_channel (web, email, chat, phone + _cat)

-reported_by_role (support, devops, product_manager, finance, c_level + _cat)

Impact & flags

-customers_affected (int, heavy-tailed)

-error_rate_pct (float, 0–100; sometimes 0.0 as “unmeasured”)

-downtime_min (int, 0 when only degraded)

-payment_impact_flag, security_incident_flag, data_loss_flag, has_runbook (0/1)

Text proxy

-customer_sentiment (negative/neutral/positive + _cat with 0 = missing)

-description_length (int, 20–2000)

Target

-priority (low/medium/high + priority_cat = 1/2/3)

Notes & limitations

• Fully synthetic; suitable for education, benchmarking, and tutorials.
• No temporal ordering or post-resolution fields are included, avoiding label leakage.
• The noise level is tuned for ~97–98% ceiling performance with well-optimized models, not perfect separability.

jadaprojects1/customer-support-ticket-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

This dataset provides comprehensive logs of customer support tickets, including detailed issue descriptions, resolution times, outcomes, agent involvement, and customer satisfaction ratings. It enables analysis of support process efficiency, identification of bottlenecks, and development of NLP models for automated ticket classification and resolution prediction.

Support Ticket Routing by Intent Market Outlook

According to our latest research, the global Support Ticket Routing by Intent market size reached USD 1.45 billion in 2024. The market is expected to grow at a robust CAGR of 17.8% from 2025 to 2033, reaching a projected value of USD 6.05 billion by the end of the forecast period. This significant growth is primarily driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies to automate and optimize customer support processes across various industries. The growing demand for efficient and accurate ticket routing solutions is further fueled by the need to enhance customer satisfaction and reduce operational costs.

One of the primary growth factors propelling the Support Ticket Routing by Intent market is the rapid digital transformation initiatives undertaken by enterprises globally. As organizations strive to deliver seamless customer experiences, the necessity for intelligent support ticket management systems has become more pronounced. The implementation of intent-based routing solutions enables businesses to analyze and understand customer queries more effectively, thus ensuring that tickets are assigned to the most appropriate agents or departments. This not only accelerates response times but also improves first-contact resolution rates, which are critical performance indicators for customer support operations. Additionally, the proliferation of omnichannel communication channels, such as email, chat, social media, and voice, has necessitated the adoption of advanced routing technologies that can handle high ticket volumes with precision and agility.

Another significant driver for the Support Ticket Routing by Intent market is the increasing complexity and variety of support requests being generated in today’s digital ecosystem. With the surge in remote work, e-commerce, and digital services, organizations are encountering a broader spectrum of customer issues that require specialized handling. Intent-based routing leverages natural language processing (NLP) and AI to categorize and prioritize tickets based on context, urgency, and sentiment. This intelligent automation reduces the manual intervention required for ticket triage, minimizes human errors, and ensures that high-priority issues are addressed promptly. As a result, enterprises are able to optimize resource allocation, lower support costs, and enhance overall productivity, further driving the adoption of these solutions.

Additionally, the increasing focus on data-driven decision-making and analytics is shaping the evolution of the Support Ticket Routing by Intent market. Modern ticket routing platforms are equipped with advanced analytics and reporting capabilities, providing organizations with real-time insights into ticket trends, agent performance, and customer satisfaction metrics. These actionable insights empower support managers to identify bottlenecks, optimize workflows, and implement targeted training programs for agents. Furthermore, regulatory compliance and data security requirements, especially in sectors like BFSI and healthcare, are encouraging enterprises to invest in robust intent-based routing solutions that can ensure secure and compliant handling of sensitive customer information.

From a regional perspective, North America currently dominates the Support Ticket Routing by Intent market, accounting for the largest share in 2024, primarily due to the early adoption of AI-driven technologies and the presence of leading technology vendors. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by rapid digitalization, expanding IT infrastructure, and increasing investments in customer service automation across emerging economies such as China and India. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, supported by the rising demand for efficient support solutions in BFSI, healthcare, and retail sectors.

Component Analysis

The Support Ticket Routing by Intent market is segmented by component into software and services, each playing a pivotal role in the overall ecosystem. The software segment comprises platforms and solutions that leverage AI, ML, and NLP technologies to automate the routing of support tickets based on intent analysis. These platforms are designed to integrate seamlessly with existing customer relationship management (CRM) and hel

Support Ticket Routing by Intent Market Outlook

According to our latest research, the global Support Ticket Routing by Intent market size achieved a valuation of USD 1.67 billion in 2024. The market is expected to grow at a robust CAGR of 17.3% from 2025 to 2033, reaching an estimated USD 6.02 billion by 2033. This impressive growth trajectory is primarily driven by the escalating adoption of artificial intelligence (AI) and machine learning (ML) technologies for automating and optimizing customer service operations, as organizations seek to enhance customer experience, operational efficiency, and cost-effectiveness.

The growth of the Support Ticket Routing by Intent market is strongly influenced by the increasing digital transformation initiatives across industries, which have led to a surge in customer queries and support requests. As businesses expand their digital touchpoints, the volume and complexity of support tickets have grown exponentially, necessitating advanced solutions that can accurately interpret and categorize customer intents. AI-powered intent-based routing systems are increasingly being adopted to automate the triage process, significantly reducing response times and improving first-contact resolution rates. This trend is particularly pronounced in sectors such as BFSI, retail, and telecommunications, where customer expectations for prompt and personalized support are exceptionally high. The integration of natural language processing (NLP) and sentiment analysis further enhances the accuracy of intent detection, enabling organizations to deliver more contextually relevant solutions and drive higher customer satisfaction.

Another key driver for the Support Ticket Routing by Intent market is the rising demand for scalable and cost-effective support solutions among enterprises of all sizes. Small and medium enterprises (SMEs), in particular, are leveraging cloud-based intent routing platforms to streamline their support operations without the need for significant upfront investments in infrastructure. The availability of flexible deployment models, including both on-premises and cloud solutions, allows organizations to tailor their support ticket routing systems to their specific operational requirements and compliance needs. Furthermore, the growing emphasis on omnichannel support and the need to unify customer interactions across multiple channels—such as email, chat, social media, and voice—have accelerated the adoption of intent-based routing technologies that can seamlessly integrate with existing customer relationship management (CRM) and helpdesk platforms.

Additionally, the market is benefiting from the increasing focus on data-driven decision-making and the use of advanced analytics to optimize support workflows. Organizations are leveraging intent-based routing solutions not only to automate ticket assignment but also to gain actionable insights into customer behavior, common pain points, and support team performance. This data-driven approach enables continuous improvement in service delivery, proactive issue resolution, and more effective resource allocation. The integration of AI-driven analytics with support ticket routing systems is also facilitating predictive support, where potential issues are identified and addressed before they escalate, further enhancing the overall customer experience and reducing operational costs.

From a regional perspective, North America continues to dominate the Support Ticket Routing by Intent market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high adoption rate of advanced AI and automation technologies, coupled with the presence of major technology vendors and a mature digital infrastructure, has positioned North America as a key innovation hub. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by rapid digitalization, increasing investments in customer service technologies, and the expanding presence of global enterprises. Europe remains a significant market, characterized by strong regulatory frameworks and a growing emphasis on data privacy and compliance. Other regions, including Latin America and the Middle East & Africa, are also experiencing steady growth, supported by the rising adoption of cloud-based support solutions and the increasing focus on enhancing customer engagement.

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.

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.

Customer Support Ticket Records

  Dataset Description

Structured complaint and resolution logs from customer support channels

  Dataset Information

Category: Customer and User Behavior Format: CSV, Parquet Rows: 300,000 Columns: 12 Date Generated: 2025-10-05 Location: data/customer_support_ticket_records/

  Schema

Column Type Sample Values

ticket_id String TKT00000001

created_at Datetime 2025-09-13 16:40:00

customer_id String CUST7722393… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/nigerian-telecom-customer-support-ticket-records.

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

Support Ticket Resolution Data

  Dataset Description

Comprehensive support ticket resolution data for Nigerian retail and e-commerce analysis

  Dataset Information

Category: Customer Support Industry: Retail & E-Commerce Country: Nigeria Format: CSV, Parquet Rows: 100,000 Columns: 10 Date Generated: 2025-10-06 Location: data/support_ticket_resolution_data/ License: GPL

  Schema

Column Type Sample Values

ticket_id String TKT0000000… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/nigerian_retail_and_ecommerce_support_ticket_resolution_data.

This dataset provides comprehensive logs of tech support tickets, including customer and agent details, ticket lifecycle events, escalation history, and resolution outcomes. It enables technology firms to analyze support processes, optimize resource allocation, and improve customer satisfaction through actionable insights.

The Help Desk & Ticketing Software market is experiencing robust growth, driven by the increasing need for efficient customer service and streamlined internal communication across diverse organizations. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the integration of AI-powered features for automation and improved response times, and a growing emphasis on enhancing customer experience (CX) across all industries. Businesses of all sizes are increasingly recognizing the value of centralized ticketing systems to manage support requests, track resolutions, and improve overall operational efficiency. This has led to a significant rise in demand for sophisticated software solutions that provide features such as self-service portals, knowledge bases, and robust reporting capabilities. The market's competitive landscape is dynamic, with a mix of established players and emerging innovative companies vying for market share. This competition fosters innovation and drives the development of advanced functionalities, benefiting end-users. While the precise market size figures are unavailable, a reasonable estimate based on market trends suggests a 2025 market valuation of approximately $15 billion, growing at a Compound Annual Growth Rate (CAGR) of 12% over the forecast period (2025-2033). This growth is anticipated to be driven by increasing cloud adoption and the integration of advanced technologies like AI and machine learning within help desk solutions. Factors such as stringent data privacy regulations and the need for seamless integration with existing enterprise systems may present challenges to growth, but the overall positive market outlook is expected to continue. The market segmentation is broad, catering to various business sizes and needs, with cloud-based solutions projected to dominate the market share in the coming years. The competitive landscape remains highly fragmented, encouraging both organic growth and strategic mergers and acquisitions within the sector. This indicates a healthy and evolving market with ample opportunities for both existing and new players.

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.

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.

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.

The Help Desk Ticketing System Server market is experiencing robust growth, driven by the increasing need for efficient IT support and streamlined customer service across diverse industries. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors, including the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the increasing demand for improved customer experience through quicker resolution times, and the growing integration of AI and machine learning to automate ticket routing and resolution. Furthermore, the rising adoption of hybrid work models necessitate robust and accessible help desk systems capable of managing diverse user locations and devices. The market's segmentation reflects the varied needs of different organizations; companies offer solutions tailored to specific industries and business sizes, leading to a competitive landscape characterized by both established players and innovative startups. The market's continued growth trajectory is expected to be driven by ongoing technological advancements, increased investment in IT infrastructure, and the persistent need for efficient problem management across global enterprises. Significant regional variations are anticipated, with North America and Europe expected to maintain substantial market share due to high technology adoption rates and the presence of established players. However, rapid technological advancements and expanding digital infrastructure in developing regions are expected to unlock significant growth opportunities in the coming years, fostering a more geographically diversified market in the long term. The increasing sophistication of these systems, incorporating features such as self-service portals, knowledge bases, and advanced analytics, further solidifies their importance in enhancing operational efficiency and boosting customer satisfaction. The competitive landscape involves both large established vendors and smaller niche players, fostering innovation and providing a diverse range of solutions to address the evolving needs of organizations.

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.

Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.