This statistic shows the share of customers in the U.S. and worldwide by if they have ever stopped doing business with a brand due to a poor customer service experience in 2018. During the survey, 62 percent of respondents from the United States stated that they have stopped doing business with a brand due to a poor customer service experience.

Records from operating a customer call center or service center providing services to the public. Services may address a wide variety of topics such as understanding agency mission-specific functions or how to resolve technical difficulties with external-facing systems or programs. Includes:rn- incoming requests and responsesrn- trouble tickets and tracking logs rn- recordings of call center phone conversations with customers used for quality control and customer service trainingrn- system data, including customer ticket numbers and visit tracking rn- evaluations and feedback about customer servicesrn- information about customer services, such as “Frequently Asked Questions” (FAQs) and user guidesrn- reports generated from customer management datarn- complaints and commendation records; customer feedback and satisfaction surveys, including survey instruments, data, background materials, and reports.

In 2022, the communication channel that was considered to be the easiest to use in customer service in the United States was a ******************. ** percent of respondents chose this as their answer, whereas only ** percent stated a live video chat was the easiest communication channel to use.

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.

During a 2022 survey carried out in the United States, ** percent of responding millennial consumers stated that it was important or very important to them that they could contact a real person when communicating with a business. Only *** percent said it was not important or not at all important.

"This dataset contains transcribed customer support calls from companies in over 160 industries, offering a high-quality foundation for developing customer-aware AI systems and improving service operations. It captures how real people express concerns, frustrations, and requests — and how support teams respond.

Included in each record:

• Full call transcription with labeled speakers (system, agent, customer)
• Concise human-written summary of the conversation
• Sentiment tag for the overall interaction: positive, neutral, or negative
• Company name, duration, and geographic location of the caller
• Call context includes industries such as eCommerce, banking, telecom, and streaming services

Common use cases:

• Train NLP models to understand support calls and detect churn risk
• Power complaint detection engines for customer success and support teams
• Create high-quality LLM training sets with real support narratives
• Build summarization and topic tagging pipelines for CX dashboards
• Analyze tone shifts and resolution language in customer-agent interaction

This dataset is structured, high-signal, and ready for use in AI pipelines, CX design, and quality assurance systems. It brings full transparency to what actually happens during customer service moments — from routine fixes to emotional escalations."

This operations dashboard shows historic and current data related to this performance measure.

As of July 2024, about ** percent of British customers stated that they preferred getting excellent service in the automotive sector, even if it means paying more for it. Two years earlier, this share stood at **** percent.

This dataset provides Customer Service Satisfaction results from the Annual Community Survey. The survey questions assess satisfaction with overall customer service for individuals who had contacted the city in the past year. For years where there are multiple questions related to overall customer service and treatment, the average of those responses is provided in this dataset. Responses for each question are shown in the detailed dataset.For years 2010-2014, respondents were first asked, "Have you contacted the city in the past year?". If they answered that they had contacted the city, then they were asked additional questions about their experience. The "number of respondents" field represents the number of people who answered yes to the contact question.Responses of "don't know" are not included in this dataset, but can be found in the dataset for the entire Community Survey. A survey was not completed for 2015.The performance measure dashboard is available at 2.02 Customer Service Satisfaction.Additional InformationSource: Community Attitude SurveyContact: Wydale HolmesContact E-Mail: Wydale_Holmes@tempe.govData Source Type: Excel and PDFPreparation Method: Extracted from Annual Community Survey resultsPublish Frequency: AnnualPublish Method: ManualData Dictionary

MDH Customer Service Report FY21- Improving the Customer Experience from Multiple Perspectives

Envestnet®| Yodlee®'s Consumer Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

This dataset provides Customer Service Satisfaction results from the Annual Community Survey. The survey questions assess satisfaction with overall customer service for inpiduals who had contacted the city in the past year.

For years where there are multiple questions related to overall customer service and treatment, the average of those responses are provided in this dataset. Responses for each question are shown in the detailed dataset.

For years 2010-2014, respondents were first asked "Have you contacted the city in the past year?". If they answered that they had contacted the city, then they were asked additional questions about their experience. The "number of respondents" field represents the number of people who answered yes to the contact question.

Responses of "don't know" are not included in this dataset, but can be found in the dataset for the entire Community Survey. A survey was not completed for 2015.

The performance measure dashboard is available at 2.02 Customer Service Satisfaction.

Additional Information

Source: Community Attitude Survey

Contact: Wydale Holmes

Contact E-Mail: Wydale_Holmes@tempe.gov

Data Source Type: Excel and PDF

Preparation Method: Extracted from Annual Community Survey results

Publish Frequency: Annual

Publish Method: Manual

Data Dictionary

This dataset contains select monthly performance statistics that DOI regularly reports to the Mayor's Office of Operations for 2010 - 2015. This dataset includes several indicators that are cummulated for the Mayor's Management Reports, such as the including numbers of complaints received by the Agency and the numbers of arrests made. This dataset also includes monthly statistics on the Agency's outreach efforts (anticorrupion and whistleblower lectures) as well customer service indicators (such as the number of emails received by the Agency).

The Tempe Fire Medical Rescue Department (TFMR) is an “all-hazards” department that responds to all types of calls for service. The City of Tempe collects data from an annual Community Survey and the monthly TFMR Customer Service Survey to gauge resident perceptions about the quality and satisfaction of city services, programs and direction. The survey results help to determine priorities for the community as part of the City's ongoing strategic management process. This page provides data for the Fire Services Satisfaction performance measure. The performance measure dashboard is available at 1.04 Fire Services Satisfaction Includes detailed responses to Tempe Fire Medical Rescue Customer Satisfaction Survey. Surveys involving medical patients are sent out weekly to Tempe Medical Fire Rescue patients who provided email address at time of treatment. Results are calculated monthly for the prior months responses for review by Tempe Fire Medical Rescue administrators. Respondents are asked to answer several questions about their experience. Detailed information about the questions are included in the data dictionary for this dataset. Additional Information Source: Tempe Fire Medical Rescue Customer Satisfaction SurveyContact: Wydale Holmes / Hans Silberschlag (Fire Customer Survey)Contact E-Mail: wydale_holmes@tempe.govData Source Type: ExcelPreparation Method: Data downloaded from website (Survey Monkey)Publish Frequency: Monthly (Fire Customer Survey)Publish Method: ManualData Dictionary

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

In the Netherlands, the preference of human interaction or automated customer service varied by generation in 2023. When asked about whether or not they prefer automated services for solving simpler issues, Generation Z was most likely to prefer the automated system, with ** percent respondent share. Both Millennials and Generation Z stated that they expect a human to immediately respond to them when contacting a company directly, both with around ** percent share of respondents.

Comprehensive dataset containing 15 verified Appliances customer service businesses in Minnesota, United States with complete contact information, ratings, reviews, and location data.

Customer Satisfaction Kiosk Market Outlook

The global customer satisfaction kiosk market size in 2023 is estimated to be around USD 1.5 billion, demonstrating a robust growth trajectory with a compound annual growth rate (CAGR) of 9.2% projected through 2032. By 2032, the market is expected to reach approximately USD 3.4 billion. This growth is driven by increasing demand for customer feedback solutions, enhanced user engagement technologies, and the rising emphasis on customer experience across various industries.

One of the key growth factors for the customer satisfaction kiosk market is the expanding focus on customer experience management (CEM) across enterprises. Businesses are increasingly realizing the significance of customer feedback in driving improvements and innovations. Kiosks offer a convenient and immediate way for customers to provide feedback, thus helping businesses to rapidly address issues and improve service quality. The real-time data collection capabilities of these kiosks are crucial for making timely and informed decisions, thereby enhancing overall customer satisfaction.

The integration of advanced technologies such as Artificial Intelligence (AI) and data analytics is another major growth driver for this market. AI-powered kiosks can analyze customer feedback in real-time, offering actionable insights that help businesses to personalize and improve their services. Furthermore, the use of data analytics enables companies to identify trends and patterns in customer behavior, allowing for more targeted improvement initiatives. The incorporation of these advanced technologies is expected to further augment the market growth over the forecast period.

Additionally, the advent of the Internet of Things (IoT) has revolutionized the capabilities of customer satisfaction kiosks. IoT-enabled kiosks can seamlessly integrate with other digital systems within an organization, providing a unified view of customer feedback across multiple touchpoints. This interconnected ecosystem enhances the accuracy and comprehensiveness of the feedback collected, thereby facilitating more effective customer service interventions. The increasing adoption of IoT in kiosk technology is anticipated to drive significant market growth in the coming years.

From a regional perspective, North America holds a substantial share of the global customer satisfaction kiosk market, primarily due to the early adoption of advanced technologies and a high focus on enhancing customer experience across industries. Europe follows closely, benefiting from a well-established retail and hospitality sector. The Asia Pacific region is poised for rapid growth, driven by burgeoning retail markets, increasing digitalization, and a growing emphasis on customer service quality. Latin America and the Middle East & Africa are also expected to witness significant market expansion, albeit at a slower pace, fueled by emerging market dynamics and improving technological infrastructure.

Component Analysis

The customer satisfaction kiosk market is segmented into hardware, software, and services. The hardware segment encompasses the physical components of kiosks, including screens, printers, touch interfaces, and other peripheral devices. The software segment includes the various programs and applications that enable the functionality of these kiosks, such as data collection, feedback analysis, and reporting tools. The services segment covers installation, maintenance, and support services provided by vendors to ensure the smooth operation of kiosks.

Hardware is a critical component of the customer satisfaction kiosk market, as it forms the backbone of the kiosk system. The durability and reliability of hardware components are paramount, as kiosks are often placed in high-traffic areas and must withstand constant use. Innovations in hardware design, such as the development of more robust touchscreens and compact, energy-efficient components, have significantly improved the performance and lifespan of kiosks. As a result, the demand for advanced hardware solutions is expected to grow steadily during the forecast period.

Software plays an equally important role in the functionality of customer satisfaction kiosks. It enables the collection, processing, and analysis of customer feedback, making it a vital component for businesses seeking to leverage customer insights. Advanced software solutions often incorporate features such as real-time data analytics, AI-driven sentiment analysis, and integration with Customer Relationship Management (CRM) systems. These capab

A number of polluting activities and operations require permits or licences. To save applicants' time, the EPD has established Customer Service Counters at all of its Regional Offices. The department also receive electronic submissions. For details, please refer to EPD Webpage.

According to the results of a recent survey, about ******* of the customers in the United Kingdom (UK) stated that they would pay more to get excellent service because they trust the company they use. A further ** percent of the British customers said that they prefer getting excellent service even if it cost more, they feel happier knowing they have the support and advice.