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.

*******, the television provider, was voted as the worst rated company for customer service in the United States in 2020, receiving the largest share of negative responses (** percent). Second in the list came Well Fargo and DIRECTV, with ** percent of respondents to the survey complaining about poor customer service. Customer service in the U.S. Good customer service is imperative for a company to do well and keep their customers. In 2020, 58 percent of customers in the United States have contacted customer service in the past month, while 40 percent of customers reported that they stopped doing business with a company as a result of poor customer service. This indicates that poor customer service is a significant deal breaker for a large part of consumers. The most used method to contact customer service is through voice channels, with ** percent of respondents mentioning it as their preferred method. Chatbots Another tool used in customer service is chatbots. Chatbots are artificial intelligence used to respond via online messaging and replacing the human factor. If customers had accessibility to effective chatbots, they would have a variety of benefits. However, 64 percent of respondents say they expect to enjoy 24-hour service the most. On the other hand, ** percent of respondents said that they would stop using a chatbot if they could deal with a real-life assistant. Additionally, ** percent of customers reported that their number one dislike of using chatbots was that it kept them from using a live person.

This statistic shows the share of customers worldwide by their opinion about the most frustrating aspect of a poor customer service experience in 2018, by age. During the survey, 26 percent of respondents, aged between 18 and 34 years, cited not being able to resolve their issue on their own using self-service as one of the most frustrating aspect of 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.

A March 2022 survey asked the public about their opinion on customer service at restaurants in the United States. The majority of respondents, ** percent, reported having a good opinion of customer service. Meanwhile, **** percent of respondents had a poor opinion.

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

This dataset supports the article entitled "Effects of Customer Reviews on Product Sales of Strong Brands: A Qualitative Comparative Analysis."

This dataset provides a comprehensive and realistic representation of customer sentiment across multiple online and offline shopping platforms. It contains 25,000 customer feedback records, each including demographic attributes, product categories, purchase channels, ratings, review text, and sentiment classification.

The dataset reflects how customers express their experiences on platforms such as Amazon, Flipkart, Meesho, Facebook Marketplace, Myntra, Ajio, Nykaa, Croma, Boat, Reliance Digital, BigBasket, JioMart, Swiggy Instamart, Zepto, and many others. It captures a wide spectrum of sentiments, from highly satisfied customers praising product quality and delivery speed to dissatisfied users reporting issues such as delayed deliveries, low-quality items, or unsatisfactory support.

Each review is paired with a star rating (1 to 5). Ratings of 4 and 5 are mapped to positive sentiment, 3 to neutral, and 1 and 2 to negative sentiment. Corresponding review text is generated to match the sentiment tone, making the dataset ideal for text and sentiment understanding.

In addition to sentiment and rating, the dataset includes essential service metrics such as response time (in hours), whether the issue was resolved, and whether a formal complaint was registered. This creates a richer ecosystem of customer experience and feedback patterns.

The dataset is suitable for a wide variety of uses, including customer insight studies, retail analytics, sentiment analysis, product review exploration, behavior understanding, or business decision making. Since the dataset is fully synthetic and free from personal identifiers, it is safe for all academic, analytical, and research purposes.

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

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

Comprehensive dataset containing 5 verified Appliances customer service businesses in Alicante, Spain 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.

Context

Customer reviews are the most crucial information for a company to understand the perspective of the user about the product or service. The dataset is web scraped from Trustpilot website for Nike online store customer reviews.

Content

Reviewer - reviewer of the product Title Content - header of the review title Star_rating - star rating between 1-5 Rating - poor, excellent, good Date - year.month.date format of published date for review Reviews_posted - reviews posted by the reviewer on the website Location - reviewer location

Acknowledgements

this data is part of Trustpilot reviews website which is web scraped

Inspiration

This is the inspiration for me to learn about web scraping to understand the perspective of a real user of the product

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.

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.

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

Amazon Product Reviews

18 Years of Customer Ratings and Experiences

By Huggingface Hub [source]

About this dataset

The Amazon Reviews Polarity Dataset discloses eighteen years of customers' ratings and reviews from Amazon.com, offering an unparalleled trove of insight and knowledge. Drawing from the immense pool of over 35 million customer reviews, this dataset presents a broad spectrum of customer opinions on products they have bought or used. This invaluable data is a gold mine for improving products and services as it contains comprehensive information regarding customers' experiences with a product including ratings, titles, and plaintext content. At the same time, this dataset contains both customer-specific data along with product information which encourages deep analytics that could lead to great advances in providing tailored solutions for customers. Has your product been favored by the majority? Are there any aspects that need extra care? Use Amazon Reviews Polarity to gain deeper insights into what your customers want - explore now!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Analyze customer ratings to identify trends: Take a look at how many customers have rated the same product or service with the same score (e.g., 4 stars). You can use this information to identify what customers like or don’t like about it by examining common sentiment throughout the reviews. Identifying these patterns can help you make decisions on which features of your products or services to emphasize in order to boost sales and satisfaction rates.

2 Review content analysis: Analyzing review content is one of the best ways to gauge customer sentiment toward specific features or aspects of a product/service. Using natural language processing tools such as Word2Vec, Latent Dirichlet Allocation (LDA), or even simple keyword search algorithms can quickly reveal general topics that are discussed in relation to your product/service across multiple reviews - allowing you quickly pinpoint areas that may need improvement for particular items within your lines of business.

3 Track associated scores over time: By tracking customer ratings overtime, you may be able to better understand when there has been an issue with something specific related to your product/service - such as negative response toward a feature that was introduced but didn’t seem popular among customers and was removed shortly after introduction.. This can save time and money by identifying issues before they become widespread concerns with larger sets of consumers who invest their money in using your company's item(s).

4 Visualize sentiment data over time graphs : Utilizing visualizations such as bar graphs can help identify trends across different categories quicker than raw numbers alone; combining both numeric values along with color differences associated between different scores allows you spot anomalies easier - allowing faster resolution times when trying figure out why certain spikes occurred where other stayed stable (or vice-versa) when comparing similar data points through time-series based visualization models

Research Ideas

• Developing a customer sentiment analysis system that can be used to quickly analyze the sentiment of reviews and identify any potential areas of improvement.
• Building a product recommendation service that takes into account the ratings and reviews of customers when recommending similar products they may be interested in purchasing.
• Training a machine learning model to accurately predict customers’ ratings on new products they have not yet tried and leverage this for further product development optimization initiatives

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:--------------|:-------------------------------------------------------------------| | label | The sentiment of the review, either positive or negative. (String) | | title | The title of the review. (String) ...

GameStop Customer Reviews Dataset

Analytical Review Data of GameStop Products

By Crawl Feeds [source]

About this dataset

GameStop Product Reviews Dataset

Comprehensive and Detailed Customer Reviews and Ratings of Products from GameStop

Data Overview:

This dataset comprises a rich variety of information centered on customer reviews and ratings for products purchased from GameStop. For each review, the data includes detailed aspects such as the product name, brand, SKU (Stock Keeping Unit), helpful and non-helpful votes count, reviewer's name along with their review title & description. Further insights can be found through additional features that outline whether or not the reviewer recommends the product, whether they are a verified purchaser and encompass individual & average ratings for each product.

Other significant facets encapsulated within this valuable resource involve multimedia elements like images posted in reviews. To verify temporal relevance, timestamps revealing when the review was written (reviewed_at) as well as when the data was collected (scraped_at) are provided.

Additionally, URLs related to both specific items up for purchase (url) at GameStop's site and other users' reviews pages (reviews_link) have been accumulated within. The total number of customer feedback posts per item is also available under reviews_count series.

Structure:

The dataset structure presents serialized versions of the afore-mentioned fields. This includes strings such as 'name', 'brand', 'review_title', etc; date times including 'reviewed_at' and 'scraped_at'; floating point numbers such as 'rating' & 'average_rating'; integers representing counts ('helpful_count','not_helpful_count' ); boolean flags determining reviewers recommendations or verified purchase status ('recommended_review','verifed_purchaser') along with some potential null entries spotted across several columns making it dynamic yet intuitive even to an unfamiliar eye.

Use Case:

This dataset can serve multiple functions depending largely on user requirements.There are intriguing prospects around tracking consumer sentiments across time periods which could lend fascinating insights into sales patterns.Another possibility might revolve around determining best selling items or brands on GameStop according to customer impressions and sales counts. Additionally, there is potential to link buying trends with whether the product was purchased legitimately or not.

This dataset could also be used by product managers to enhance existing ones or create improved versions of them taking into account customer suggestions from their review content.Finaly, marketing teams could use this dataset to strategize campaigns by identifying products with positive reviews & scaling promotions for those.

Of course,the versatility of this resource opens up vast domains, ranging from sentiment analysis and recommendation systems using machine learning methodologies,to data visualization projects that help demonstrate consumer trends in a more approachable

How to use the dataset

• Sentiment Analysis: Use the 'review_description' field to understand customer sentiment towards specific products. NLP techniques can be deployed to derive sentiments from reviews text, which could help in understanding overall consumer opinion.

• Brand Analysis: Use the 'brand' field for comparative analysis of various brands sold on GameStop's platform.

• Product Recommendation System: Develop a product recommendation system based on the user's past purchase record represented by 'brand', 'sku', and past reviews.

• Customer Segmentation: Analyse fields like 'rating', 'recommended_review', and 'verifed_purchaser' for advanced segmentation of customers.

• Product Performance Analysis: By examining fields like average rating (average_rating), number of reviews (reviews_count), recommend status (recommended_review), one can gauge how well a product is performing or received by customers.

• Review Popularity Analysis: The dataset features two interesting variables - helpful_count and not_helpful_count; these reflect how other users perceived a review’s usefulness in helping them make purchasing decisions.

7 .**Time Series Forecasting**: Although we're instructed not to include any dates here, don't forget that this dataset has temporal elements ('reviewed_at') you could use for forecasting trends over time!

8 .**Reviewer Trustworthiness Assessment**: The verified purchaser field can be used as an indicator for trustworthiness of the review or reviewer bias.

P...

Access our Trustpilot Reviews Data in CSV Format, offering a comprehensive collection of customer reviews from Trustpilot.

This dataset includes detailed reviews, ratings, and feedback across various industries and businesses. Available in a convenient CSV format, it is ideal for market research, sentiment analysis, and competitive benchmarking.

Leverage this data to gain insights into customer satisfaction, identify trends, and enhance your business strategies. Whether you're analyzing consumer sentiment or conducting competitive analysis, this dataset provides valuable information to support your needs.