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.

About Dataset

Check out my new Dataset:

Multilingual Support Tickets

Dataset Description:

The Email Ticket Text Multi-Language Classification dataset offers an extensive and meticulously organized collection of email tickets written in multiple languages, including German, English, Spanish, and French. This dataset is specifically designed to enhance natural language processing (NLP), machine learning, and customer support optimization efforts. It provides a rich resource for developing and testing models that classify and prioritize customer support tickets. The dataset's structure and comprehensiveness make it ideal for both academic research and practical applications in improving customer service efficiency.

Dataset Features:

1. Queue:

   • Description: Specifies the department to which the email ticket is categorized. This helps in routing the ticket to the appropriate support team for resolution.
   • Values:
     • Software: Issues related to software applications, systems, bugs, and user errors.
     • Hardware: Problems related to physical devices, hardware malfunctions, installation issues, and maintenance requests.
     • Accounting: Inquiries and issues concerning financial transactions, billing, account discrepancies, and other accounting-related matters.

2. Priority:

   • Description: Indicates the urgency and importance of the issue. Helps in managing the workflow by prioritizing tickets that need immediate attention.
   • Values:
     • 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 user questions requiring detailed responses.
     • 3 (Critical): Urgent issues that require immediate attention and quick resolution. Examples: System outages, security breaches, data loss, and major malfunctions.

3. Software Used:

   • Description: Specifies the software application involved in the issue. Useful for categorizing and analyzing software-related problems.
   • Values: Examples include specific software names like "Arbitrum," "Adobe Premiere Pro 2021," "Excel," etc.

4. Hardware Used:

   • Description: Specifies the hardware device involved in the issue. Helps in identifying and troubleshooting hardware-related problems.
   • Values: Examples include specific hardware names like "Wireless Mouse," "IP PBX," "SFX-Netzteil," etc.

5. Accounting Category:

   • Description: Specifies the sub-category within accounting for more granular classification of financial inquiries.
   • Values: Examples include categories like "Customer Inquiries::Technical Support," "Employee Inquiries::Technical," "Customer Inquiries::Cancellations," etc.

6. Language:

   • Description: Indicates the language in which the email is written. Useful for language-specific NLP models and multilingual support analysis.
   • Values: Examples include "en" (English), "de" (German), "es" (Spanish), "fr" (French).

7. Subject:

   • Description: Provides a brief overview of the email content, aiding in quick scanning and initial categorization.
   • Values: Examples include subject lines like "Wireless Mouse suddenly stops working," "Problème de connexions IP PBX," "Problem mit meinem SFX-Netzteil," etc.

8. Text:

   • Description: Contains the full email text for in-depth analysis. Crucial for training NLP models to understand and classify complex and varied text inputs.
   • Values: Full content of the emails, describing the issue in detail.

Usage Instructions:

To access the full dataset, please contact me at dataset@softoft.de. This page only provides a preview with 200 randomly selected rows from the complete dataset, which comprises over 8,000 rows. More information about me and my company can be found at Softoft. For detailed information about this dataset, visit the dataset website.

Key Features:

• Email Subject: Provides a brief overview of the email content, aiding in quick scanning and initial categorization.
• Email Texts: Contains the full email text for in-depth analysis, crucial for training NLP models.
• Department Classification: Categorizes emails into Software, Hardware, and Accounting departments, enabling specialized handling.
• Priority Levels: Ranks emails based on urgency from low to critical, ensuring that critical issues are addressed promptly.

Use Cases:

1. Text Classification:
   • Tra...

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

The Customer Support Ticket Dataset is a dataset that includes customer support tickets for various tech products. It consists of customer inquiries related to hardware issues, software bugs, network problems, account access, data loss, and other support topics. The dataset provides information about the customer, the product purchased, the ticket type, the ticket channel, the ticket status, and other relevant details.

The dataset can be used for various analysis and modelling tasks in the customer service domain.

Features Description:

• Ticket ID: A unique identifier for each ticket.
• Customer Name: The name of the customer who raised the ticket.
• Customer Email: The email address of the customer (Domain name - @example.com is intentional for user data privacy concern).
• Customer Age: The age of the customer.
• Customer Gender: The gender of the customer.
• Product Purchased: The tech product purchased by the customer.
• Date of Purchase: The date when the product was purchased.
• Ticket Type: The type of ticket (e.g., technical issue, billing inquiry, product inquiry).
• Ticket Subject: The subject/topic of the ticket.
• Ticket Description: The description of the customer's issue or inquiry.
• Ticket Status: The status of the ticket (e.g., open, closed, pending customer response).
• Resolution: The resolution or solution provided for closed tickets.
• Ticket Priority: The priority level assigned to the ticket (e.g., low, medium, high, critical).
• Ticket Channel: The channel through which the ticket was raised (e.g., email, phone, chat, social media).
• First Response Time: The time taken to provide the first response to the customer.
• Time to Resolution: The time taken to resolve the ticket.
• Customer Satisfaction Rating: The customer's satisfaction rating for closed tickets (on a scale of 1 to 5).

Use Cases of such dataset:

• Customer Support Analysis: The dataset can be used to analyze customer support ticket trends, identify common issues, and improve support processes.
• Natural Language Processing (NLP): The ticket descriptions can be used for training NLP models to automate ticket categorization or sentiment analysis.
• Customer Satisfaction Prediction: The dataset can be used to train models to predict customer satisfaction based on ticket information.
• Ticket Resolution Time Prediction: The dataset can be used to build models for predicting the time it takes to resolve a ticket based on various factors.
• Customer Segmentation: The dataset can be used to segment customers based on their ticket types, issues, or satisfaction levels.
• Recommender Systems: The dataset can be used to build recommendation systems for suggesting relevant solutions or products based on customer inquiries.

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.

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.

A complete list of live websites using the Support Tickets technology, compiled through global website indexing conducted by WebTechSurvey.

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.

A complete list of live websites using the Support Ticket System technology, compiled through global website indexing conducted by WebTechSurvey.

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.

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.

A complete list of live websites using the Simple Support Ticket System technology, compiled through global website indexing conducted by WebTechSurvey.

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

A complete list of live websites using the Support Tickets V2 technology, compiled through global website indexing conducted by WebTechSurvey.

Dataset

This dataset was created by vijay thurimella

Contents

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.

A complete list of live websites using the Live Support Tickets technology, compiled through global website indexing conducted by WebTechSurvey.