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.

According to a survey conducted in March 2024 among online shoppers, ** percent of consumers in the United States had stopped shopping with a brand they received poor customer service from, while ** percent of them had written a bad review online. Meanwhile, about ** percent had shared their experience on social media.

This statistic shows the percentage of consumers who would switch service provider after one incidence of bad customer service in the United Kingdom in 2013, by organisation type. Of respondents, ** percent claimed they would change their credit card provider after a bad customer service experience and ** percent would change their insurance company in the same case.

Comprehensive dataset containing 937 verified Appliances customer service businesses in United States with complete contact information, ratings, reviews, and location data.

"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."

Comprehensive dataset containing 45 verified Appliances customer service businesses in Venezuela with complete contact information, ratings, reviews, and location data.

Each week we send thousands of consumers' complaints about financial products and services to companies for response. Those complaints are published here after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaint narratives are consumers' descriptions of their experiences in their own words. By adding their voice, consumers help improve the financial marketplace. The database generally updates daily.

1) Data Introduction • The Samsung Customer Reviews Dataset contains 1,000 customer reviews of various Samsung products, including smartphones, tablets, TVs, and smartwatches. User feedback, ratings, and timestamps are included, which are useful for emotional analysis, customer satisfaction surveys, and product quality assessment.

2) Data Utilization (1) Samsung Customer Reviews Dataset has characteristics that: • This dataset contains structured text and numerical information for each review, including product name, username, rating, review title, review body, and creation date, for detailed analysis by review. (2) Samsung Customer Reviews Dataset can be used to: • Customer Opinion Analysis and Emotional Classification: Review texts and ratings can be used to identify customer positive and negative emotions, major complaints and compliments about Samsung products, and to improve products and develop marketing strategies. • Comparison of satisfaction and trend analysis by product: By analyzing review data by product group and period, market trends such as popular products, changes in customer preferences, and repeatedly mentioned issues can be derived and used for competitor analysis or new product planning.

Comprehensive dataset containing 13 verified Appliances customer service businesses in New Jersey, United States with complete contact information, ratings, reviews, and location data.

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.

Data Entry Service Market Outlook

The global data entry service market size is poised to experience significant growth, with the market expected to rise from USD 2.5 billion in 2023 to USD 4.8 billion by 2032, achieving a Compound Annual Growth Rate (CAGR) of 7.5% over the forecast period. This growth can be attributed to several factors including the increasing adoption of digital technologies, the rising demand for data accuracy and integrity, and the need for businesses to manage vast amounts of data efficiently.

One of the key growth factors driving the data entry service market is the rapid digital transformation across various industries. As businesses continue to digitize their operations, the volume of data generated has increased exponentially. This data needs to be accurately entered, processed, and managed to derive meaningful insights. The demand for data entry services has surged as companies seek to outsource these non-core activities, enabling them to focus on their primary business operations. Additionally, the widespread adoption of cloud-based solutions and big data analytics has further fueled the demand for efficient data management services.

Another significant driver of market growth is the increasing need for data accuracy and integrity. Inaccurate or incomplete data can lead to poor decision-making, financial losses, and a decrease in operational efficiency. Organizations are increasingly recognizing the importance of maintaining high-quality data and are investing in data entry services to ensure that their databases are accurate, up-to-date, and reliable. This is particularly crucial for industries such as healthcare, BFSI, and retail, where precise data is essential for regulatory compliance, customer relationship management, and operational efficiency.

The cost-effectiveness of outsourcing data entry services is also contributing to market growth. By outsourcing these tasks to specialized service providers, organizations can save on labor costs, reduce operational expenses, and improve productivity. Service providers often have access to advanced tools and technologies, as well as skilled professionals who can perform data entry tasks more efficiently and accurately. This not only leads to cost savings but also allows businesses to reallocate resources to more strategic activities, driving overall growth.

From a regional perspective, the Asia Pacific region is expected to witness the highest growth in the data entry service market during the forecast period. This can be attributed to the region's strong IT infrastructure, the presence of numerous outsourcing service providers, and the growing adoption of digital technologies across various industries. North America and Europe are also significant markets, driven by the high demand for data management services in sectors such as healthcare, BFSI, and retail. The Middle East & Africa and Latin America are anticipated to experience steady growth, supported by increasing investments in digital infrastructure and the rising awareness of the benefits of data entry services.

Service Type Analysis

The data entry service market can be segmented into various service types, including online data entry, offline data entry, data processing, data conversion, data cleansing, and others. Each of these service types plays a crucial role in ensuring the accuracy, integrity, and usability of data. Online data entry services involve entering data directly into an online system or database, which is essential for real-time data management and accessibility. This service type is particularly popular in industries such as e-commerce, where timely and accurate data entry is critical for inventory management and customer service.

Offline data entry services, on the other hand, involve entering data into offline systems or databases, which are later synchronized with online systems. This service type is often used in industries where internet connectivity may be unreliable or where data security is a primary concern. Offline data entry is also essential for processing historical data or data that is collected through physical forms and documents. The demand for offline data entry services is driven by the need for accurate and timely data entry in sectors such as manufacturing, government, and healthcare.

Data processing services involve the manipulation, transformation, and analysis of raw data to produce meaningful information. This includes tasks such as data validation, data sorting, data aggregation, and data analysis. Data processing is a critical componen

This comprehensive dataset offers a rich collection of over 5 million customer reviews for hotels and accommodations listed on Booking.com, specifically sourced from the United States. It provides invaluable insights into guest experiences, preferences, and sentiment across various properties and locations within the USA. This dataset is ideal for market research, sentiment analysis, hospitality trend identification, and building advanced recommendation systems.

Key Features:

• Geographic Focus: Exclusively reviews from properties located in the USA.
• Comprehensive Coverage: Includes a wide range of hotel types and sizes across different states and cities in the US, covering reviews from January 2020 to June 2025.
• Rich Detail: Each record provides detailed review information, allowing for in-depth analysis.
• Structured Format: Clean, organized, and ready for immediate use in various analytical tools and platforms.

Dive into a sample of 1,000+ records to experience the dataset's quality. For full access to this comprehensive data, submit your request at Booking reviews data.

Use Cases:

• Market Research: Gain insights into customer preferences and satisfaction in the US hospitality sector.
• Sentiment Analysis: Analyze the emotional tone of reviews to gauge customer sentiment towards hotels and services.
• Competitor Analysis: Benchmark hotel performance and identify areas for improvement against competitors.
• Trend Identification: Discover emerging trends in hotel amenities, service expectations, and guest behavior in the US.
• Recommendation Systems: Develop and train models to recommend hotels based on user preferences and review data.
• Natural Language Processing (NLP): Create and refine NLP models for text summarization, topic modeling, and opinion mining.
• Academic Research: Support studies on tourism, consumer behavior, and data science applications in hospitality.

Utilize our Amazon reviews dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset can aid in understanding customer behavior, product performance, and market trends, empowering organizations to refine their product and marketing strategies. Access the entire dataset or tailor a subset to fit your requirements. Popular use cases include: Product Performance Analysis: Analyze Amazon reviews to assess product performance, uncovering customer satisfaction levels, common issues, and highly praised features to inform product improvements and marketing messages. Customer Behavior Insights: Gain insights into customer behavior, purchasing patterns, and preferences, enabling more personalized marketing and product recommendations. Demand Forecasting: Leverage Amazon reviews to predict future product demand by analyzing historical review data and identifying trends, helping to optimize inventory management and sales strategies. Accessing and analyzing the Amazon reviews dataset supports market strategy optimization by leveraging insights to analyze key market trends and customer preferences, enhancing overall business decision-making.

Comprehensive dataset containing 23 verified Appliances customer service businesses in Algeria with complete contact information, ratings, reviews, and location data.

Reputation Management for Auto Shops Market Outlook

According to our latest research, the global Reputation Management for Auto Shops market size reached USD 1.12 billion in 2024, with a robust compound annual growth rate (CAGR) of 11.1% projected through the forecast period. By 2033, the market is forecasted to reach USD 2.93 billion, driven by the increasing digitization of the automotive service sector and the growing importance of online presence and customer feedback in shaping consumer decisions. The accelerating adoption of digital platforms and the surge in online reviews are pivotal growth factors that are fundamentally transforming how auto shops manage and enhance their reputations in a highly competitive market.

One of the primary growth drivers for the Reputation Management for Auto Shops market is the rapidly evolving digital landscape, where consumers increasingly rely on online reviews and ratings before choosing automotive services. The proliferation of review platforms such as Google, Yelp, and Facebook has made it imperative for auto shops to actively monitor and manage their online reputation. Customers are more likely to select service providers with positive reviews and high ratings, making reputation management solutions essential for business growth and customer retention. This digital shift has compelled independent auto shops, franchise chains, and dealership service centers alike to invest in sophisticated reputation management tools to safeguard and enhance their brand image, directly impacting their bottom line.

Another significant factor propelling market growth is the integration of advanced analytics and artificial intelligence (AI) into reputation management platforms. These technologies enable auto shops to gain actionable insights from vast amounts of customer feedback, social media conversations, and review data. By leveraging AI-driven sentiment analysis and automated response systems, businesses can proactively address negative feedback, identify emerging trends, and tailor their services to meet evolving customer expectations. The ability to respond swiftly and strategically to both positive and negative reviews not only improves customer satisfaction but also fosters long-term loyalty, giving auto shops a competitive edge in a crowded marketplace.

Furthermore, the increasing regulatory scrutiny and the need for compliance with data privacy laws are influencing the adoption of reputation management solutions. Auto shops are under pressure to ensure transparency and ethical handling of customer data, especially when engaging with online reviews and feedback. Comprehensive reputation management platforms offer robust security features and compliance tools, enabling businesses to adhere to industry regulations while effectively managing their digital presence. This regulatory environment, combined with the growing recognition of reputation as a critical business asset, is fueling sustained investment in reputation management solutions across the automotive service industry.

From a regional perspective, North America currently dominates the Reputation Management for Auto Shops market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high penetration of digital platforms, mature automotive aftermarket, and strong consumer awareness in North America have fostered widespread adoption of reputation management tools. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid urbanization, expanding automotive sectors, and increasing internet penetration. Latin America and the Middle East & Africa are also experiencing steady growth as auto shops in these regions recognize the value of a strong online reputation in attracting and retaining customers.

Component Analysis

The Reputation Management for Auto Shops market is segmented by component into software and services, each playing a crucial role in the overall ecosystem. Software solutions form the backbone of reputation m

Abstract: With the e-commerce growth, more people are buying products over the internet. To increase customer satisfaction, merchants provide spaces for product and service reviews. Products with positive reviews attract customers, while products with negative reviews lose customers. Following this idea, some individuals and corporations write fake reviews to promote their products and services or defame their competitors. The difficulty for finding these reviews was in the large amount of information available. One solution is to use data mining techniques and tools, such as the classification function. Exploring this situation, the present work evaluates classification techniques to identify fake reviews about products and services on the Internet. The research also presents a literature systematic review on fake reviews. The research used 8 classification algorithms. The algorithms were trained and tested with a hotels database. The CONCENSO algorithm presented the best result, with 88% in the precision indicator. After the first test, the algorithms classified reviews on another hotels database. To compare the results of this new classification, the Review Skeptic algorithm was used. The SVM and GLMNET algorithms presented the highest convergence with the Review Skeptic algorithm, classifying 83% of reviews with the same result. The research contributes by demonstrating the algorithms ability to understand consumers’ real reviews to products and services on the Internet. Another contribution is to be the pioneer in the investigation of fake reviews in Brazil and in production engineering.

Colombia Consumer Price Index (CPI): Poor: Meals In Establishments of Service to the Table & Self-service data was reported at 101.190 Dec2018=100 in Jan 2019. Colombia Consumer Price Index (CPI): Poor: Meals In Establishments of Service to the Table & Self-service data is updated monthly, averaging 101.190 Dec2018=100 from Jan 2019 (Median) to Jan 2019, with 1 observations. Colombia Consumer Price Index (CPI): Poor: Meals In Establishments of Service to the Table & Self-service data remains active status in CEIC and is reported by National Statistics Administrative Department. The data is categorized under Global Database’s Colombia – Table CO.I015: Consumer Price Index: COICOP: Dec2018=100: by Sub Class of Good and Services.

These datasets include ratings as well as social (or trust) relationships between users. Data are from LibraryThing (a book review website) and epinions (general consumer reviews).

Metadata includes

• reviews

• price paid (epinions)

• helpfulness votes (librarything)

• flags (librarything)

Introduction

This French Call Center Speech Dataset for the BFSI (Banking, Financial Services, and Insurance) sector is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for French-speaking customers. Featuring over 30 hours of real-world, unscripted audio, it offers authentic customer-agent interactions across a range of BFSI services to train robust and domain-aware ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, financial technology teams, and NLP researchers to build high-accuracy, production-ready models across BFSI customer service scenarios.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native French speakers. Captured in realistic financial support settings, these conversations span diverse BFSI topics from loan enquiries and card disputes to insurance claims and investment options, providing deep contextual coverage for model training and evaluation.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral, ensuring real-world BFSI voice coverage.

This variety ensures models trained on the dataset are equipped to handle complex financial dialogues with contextual accuracy.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, making financial domain model training faster and more accurate.

Metadata

Rich metadata is available for each participant and conversation:

The dataset contains reviews from customers about some of Nike's selling shoes. It can be used for customer review sentiment analysis, which is the task of automatically identifying the sentiment (positive, negative, or neutral) of a customer review. The dataset also includes additional information about the reviews, such as the rating, review date, location, username, and title.

Here is a detailed description of each column:

• Rating: The rating that the customer gave to the shoe.
• Review Date: The date on which the review was written.
• Location: The location of the customer who wrote the review.
• Username: The username of the customer who wrote the review.
• Review: The full text of the customer review.
• Fit Feedback: The customer's feedback on the fit of the shoe.
• Comfort Feedback: The customer's feedback on the comfort of the shoe.
• Recommend Feedback: Whether or not the customer recommends the shoe to others.
• Title: The title of the customer review.
• IsPromoReview: Whether or not the customer was incentivized to write the review (e.g., with a discount or free product).
• Subtitle: The subtitle of the customer review.
• ColorDescription: The colour of the shoe that the customer reviewed.
• FullPrice: The full price of the shoe that the customer reviewed.
• Discounted: Whether or not the shoe was on sale when the customer purchased it.
• EmployeePrice: The price that Nike employees pay for the shoe.
• CurrentPrice: The current price of the shoe.
• IsLaunch: Whether or not the shoe is a new release.
• Pid: The unique identifier of the shoe.
• Label: The label of the shoe, such as "Men's" or "Women's."

This dataset is a valuable resource for businesses wanting to understand customers' feedback on their products. The data can be used to identify areas where the effects can be improved, as well as to develop marketing and sales strategies that are tailored to the needs of the customers.

Here are some specific examples of how the data can be used:

• Identify the most important factors to customers: By analyzing the customer reviews, businesses can identify the factors that are most important to customers when choosing a shoe. For example, if many customers are writing reviews about the comfort of the shoe, then this suggests that comfort is an essential factor for customers. This information can be used to design and market shoes that are more comfortable.
• Understand customer sentiment: By analyzing the sentiment of the customer reviews, businesses can understand how customers feel about their products. This information can be used to identify areas where the effects can be improved, as well as to develop marketing and sales strategies that are tailored to the needs of the customers.
• Identify trends: By analyzing customer reviews over time, businesses can identify trends in customer preferences. For example, if more and more customers are writing reviews about sustainable shoes, then this suggests that sustainability is becoming an increasingly important factor for customers. This information can be used to develop new products that meet the needs of the customers.

Overall, the Nike shoe data is a valuable resource for businesses that want to understand their customers' feedback on their products. The data can be used to improve products, develop marketing and sales strategies, and identify trends in customer preferences.