According to a survey conducted in September 2021 in France, Germany, United Kingdom, and United States, almost half of responding consumers said they were most likely to switch to a competitor when their expectations fail to be met by companies and brands. Another ** percent of respondents stated that they would tell others about their bad experience.

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.

This statistic shows the share of customers in the U.S. and worldwide by their opinion about the most frustrating aspect of a poor customer service experience in 2018. During the survey, 18 percent of respondents from the United States cited not being able to resolve their issue on their own using self-service as one of the most frustrating aspects of a poor customer service experience.

This statistic shows the share of customers in the United States who stopped doing business with a company due to poor customer service from 2016 to 2020. During the 2020 survey, 40 percent of customers stated they stopped doing business with a company due to poor customer service.

Introduction

Customer Service Statistics: Customer service is a crucial component of business operations, significantly affecting customer retention and revenue generation. Research shows that 88% of customers are more likely to make repeat purchases when they receive excellent customer service. On the other hand, U.S. companies lose approximately USD 75 billion each year due to poor customer service.

Consumer expectations have evolved; 80% of consumers believe that the experience a company provides is just as important as its products and services. Additionally, 45% of consumers expect their issues to be resolved during their first interaction.

The use of artificial intelligence (AI) in customer service is increasing, with 56% of companies currently employing AI-powered chatbots to improve their operations. Projections indicate that by 2025, 85% of customer interactions will be managed without human intervention, thanks to advancements in AI. However, the human touch remains essential, as 80% of consumers expect to interact with a live agent when they contact a company.

These statistics illustrate the vital role of exceptional customer service in building loyalty and driving business success.

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.

Customer experience – the customer’s perception of their provider through the sum of all interactions – has come into sharp focus these last 12 months. As firms like Apple and Amazon have demonstrated, real value resides not just in the products and services a company provides but in how it provides them, especially when the economics of that product decline – as in a low interest rate environment – and when the costs of “bad” experiences increase, with customers operating under conditions of acute life stress. Read More

"This dataset captures customer complaints tied to service and experience failures, offering critical insights into where and how breakdowns occur. Sourced from reviews across 160+ industries, it focuses on moments when expectations weren’t met — and how consumers express that failure.

Key data features:

-Complaint text classified by service failure (e.g., “agent never responded,” “damaged item,” “billing error”) -Sentiment of the review (e.g., positive, negative, neutral) -Optional metadata: company/brand, timestamp, region, platform -Resolution request tagging (e.g., refund, apology, fix, cancellation)

The list may vary based on the industry and can be customized as per your request.

Use this dataset to:

-Train AI models that triage and escalate high-frustration complaints -Monitor systemic failure trends across brands or departments -Detect CX touchpoints that drive dissatisfaction or legal risk -Develop bots and assistants that recognize emotional cues in complaints -Inform service design teams about recurring pain points

Whether for automation, empathy modeling, or escalation tracking, this dataset transforms raw frustration into structured intelligence for customer experience leaders and AI builders."

*******, 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.

US Airlines Twitter (Over time)

Study the trend customer satisfaction over time

About this dataset

The columns in the dataset include index, unit id, golden, unit state, trusted judgments, last judgment at, airline sentiment, airline sentiment confidence, negative reason, negative reason confidence, airline_sentiment_gold and retweet count. There is also text included for each tweet as well as tweet location and user timezone.

Using this dataset, you can get a feel for how customers of various airlines feel about their service. You can use the data to analyze trends over time or compare different airlines. Some research ideas include using airline sentiment to predict the stock market or using the negativereason data to help airlines improve their customer service

How to use the dataset

Looking at this dataset, you can get a feel for how customers of various airlines feel about their service. The data includes the airline, the tweet text, the date of the tweet, and various other information. You can use this to analyze trends over time or compare different airlines

Research Ideas

• Using airline sentiment to predict the stock market - is there a correlation between how the public perceives an airline and how that airline's stock performs?
• Using negativereason data to help airlines improve their customer service - which negative reasons are mentioned most often? Are there certain airlines that are consistently mentioned for specific reasons?
• Use the tweet data to map out airline hot spots - where do people tend to tweet about certain airlines the most? Is there a geographic pattern to sentiment about specific airlines?

Acknowledgements

If you use this dataset in your research, please credit Social Media Data

License

License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for non-commercial purposes only. - Adapt - remix, transform, and build upon the material for non-commercial purposes only. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - You may not: - Use the material for commercial purposes.

Columns

File: Airline-Sentiment-2-w-AA.csv | Column name | Description | |:---------------------------|:-----------------------------------------------------------------------------| | _golden | This column is the gold standard column. (Boolean) | | _unit_state | This column is the state of the unit. (String) | | _trusted_judgments | This column is the number of trusted judgments. (Numeric) | | _last_judgment_at | This column is the timestamp of the last judgment. (String) | | airline_sentiment | This column is the sentiment of the tweet. (String) | | negativereason | This column is the negative reason for the sentiment. (String) | | airline_sentiment_gold | This column is the gold standard sentiment of the tweet. (String) | | name | This column is the name of the airline. (String) | | negativereason_gold | This column is the gold standard negative reason for the sentiment. (String) | | retweet_count | This column is the number of retweets. (Numeric) | | text | This column is the text of the tweet. (String) | | tweet_coord | This column is the coordinates of the tweet. (String) | | tweet_created | This column is the timestamp of the tweet. (String) | | tweet_location | This column is the location of the tweet. (String) | | user_timezone | This column is the timezone of the user. (String) |

"This dataset includes millions of consumer reviews tagged with emotion signals, making it ideal for training AI systems to detect how people feel — not just what they say. Built for sentiment-aware product development, CX strategy, and emotional behavior modeling, it offers deep insight into real consumer experience.

Features include:

-Labeled review sentiment (positive, neutral, negative) -Retail product and service context (e.g., delivery, pricing, quality) -Touchpoint mapping (pre-purchase, usage, return, support) -Optional region, channel, and timestamp data

The list may vary based on the industry and can be customized as per your request.

This dataset enables:

-Training empathetic AI agents and emotion-detecting LLMs -Mapping customer sentiment across retail segments or journey stages -dentifying emotional drivers behind repeat purchases and churn -Benchmarking brand sentiment versus competitors -Segmenting user feedback for trend and CX impact analysis

Available in clean, structured formats and optimized for large-scale NLP, this dataset is indispensable for data science, product, and CX teams focused on emotional intelligence and experience-driven growth."

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

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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) ...

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

Japanese consumers were unlikely to share poor experiences they had with a product or service, according to a survey conducted in 2024. While ** percent of respondents did not tell anyone about dissatisfying experiences with a company, family and friends were the most likely to hear about the feedback among those who tended to share it.

Customers

Maintaining current customers is very important as acquiring new customers is very expensive compared to maintaining current customers. So to understand what rate the customers are leaving Churn is calculated. The dataset contains the customer churn which is calculated by the number of customers who leave the company during a given period. The target variable in the dataset is 'Churn'. There may be many reasons for customer churn like bad onboarding, poor customer service, less engagement, and others.

Data Set Characteristics:

Classification

1. Bivariate - Target Churn
2. Multivariate - Target Contract

Regression

Target 1. Total charges 2. Monthly charges

Number of Instances: 6499

Features

CustomerID Gender Senior Citizen Partner Dependents Tenure Phone Service Multiple Lines Internet Service Online Security Online Backup Device Protection Tech Support Streaming TV Streaming Movies Contract Paperless Billing Payment Method Monthly Charges Total Charges Churn

** Acknowledgment**

The dataset was provided by Squark

Malaysia Consumers: Not Conducting: Bad Experience With Online Shop data was reported at 4.000 % in 2018. Malaysia Consumers: Not Conducting: Bad Experience With Online Shop data is updated yearly, averaging 4.000 % from Dec 2018 (Median) to 2018, with 1 observations. Malaysia Consumers: Not Conducting: Bad Experience With Online Shop data remains active status in CEIC and is reported by Malaysian Communications and Multimedia Commission. The data is categorized under Global Database’s Malaysia – Table MY.S026: E-Commerce Consumer Survey.

The Customer Service Automation market is experiencing robust growth, driven by the increasing need for businesses to enhance customer experience, reduce operational costs, and improve efficiency. The market, estimated at $25 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $80 billion by 2033. This expansion is fueled by several key factors. The rising adoption of AI-powered chatbots, virtual assistants, and self-service portals is significantly impacting customer interaction, allowing for 24/7 availability and personalized support. Furthermore, the increasing volume of customer interactions across various channels, coupled with the desire for instant resolutions, is pushing businesses towards automation solutions. Data analytics capabilities embedded within these platforms provide valuable customer insights, enabling businesses to tailor their strategies for enhanced customer loyalty and improved sales conversion rates. Major players like Oracle, Iflytek, Google, Amazon, Microsoft, and IBM are actively investing in research and development to innovate and expand their market share in this rapidly evolving landscape. However, the market's growth is not without challenges. Integration complexities with existing CRM systems, concerns regarding data security and privacy, and the potential for a negative customer experience due to limitations in AI technology remain significant restraints. Overcoming these challenges requires a strategic approach focusing on seamless integration capabilities, robust security protocols, and continuous improvement of AI algorithms to ensure accurate and personalized responses. The market is segmented by solution type (e.g., chatbots, IVR, knowledge bases), deployment model (cloud, on-premise), industry vertical (e.g., BFSI, retail, healthcare), and geography. The North American region currently holds a significant market share, followed by Europe and Asia-Pacific, with emerging markets showing promising growth potential. Future growth will depend on successful navigation of these challenges and continued innovation within the technology itself.

The dataset is from a B2C e-commerce platform in China, with massive product negative reviews of four representative sectors including Computers, Phone&Accessories, Gifts&Flowers and Clothing.Here the negative reviews are defined as the reviews with scores 1. After the raw data was collected, deduplication, user anonymization & categorization and text classification was employed to process the raw data. The data contains fields of id for comment, anonymous id for user, review text, timestamp of the posting, negative reason label and user level.

The dataset contains four JSON files, with each file titled by the corresponding sector name.In each JSON file, each line represents a record of a negative review from this sector, in which the filed ‘id’ is the unique code we created for reviews, the filed ‘userID’ is the unique code we created for users, the field ‘userLevel’ is the user’s level in the platform, the field ‘creationTime’ is the timestamp a review was posted, the filed ‘content’ is the review text in Chinese and the field ‘label’ represent why the consumers post the negative reviews, in which 0 for Logistic, 1 for Product function, 2 for Consumer Service and 3 for False Marketing.

The dataset comes from our paper:

Sun M, Zhao J. Behavioral Patterns beyond Posting Negative Reviews Online: An Empirical View. Journal of Theoretical and Applied Electronic Commerce Research. 2022; 17(3):949-983. https://doi.org/10.3390/jtaer17030049

If it is helpful, please cite the paper.

This work was supported by NSFC (Grant No. 71871006).

Data Quality Management Software Market Outlook

The global data quality management software market size was valued at approximately USD 1.5 billion in 2023 and is anticipated to reach around USD 3.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.8% during the forecast period. This growth is largely driven by the increasing complexity and exponential growth of data generated across various industries, necessitating robust data management solutions to ensure the accuracy, consistency, and reliability of data. As organizations strive to leverage data-driven decision-making and optimize their operations, the demand for efficient data quality management software solutions continues to rise, underscoring their significance in the current digital landscape.

One of the primary growth factors for the data quality management software market is the rapid digital transformation across industries. With businesses increasingly relying on digital tools and platforms, the volume of data generated and collected has surged exponentially. This data, if managed effectively, can unlock valuable insights and drive strategic business decisions. However, poor data quality can lead to erroneous conclusions and suboptimal performance. As a result, enterprises are investing heavily in data quality management solutions to ensure data integrity and enhance decision-making processes. The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in data quality management software is further propelling the market, offering automated data cleansing, enrichment, and validation capabilities that significantly improve data accuracy and utility.

Another significant driver of market growth is the increasing regulatory requirements surrounding data governance and compliance. As data privacy laws become more stringent worldwide, organizations are compelled to adopt comprehensive data quality management practices to ensure adherence to these regulations. The implementation of data protection acts such as GDPR in Europe has heightened the need for data quality management solutions to ensure data accuracy and privacy. Organizations are thus keen to integrate robust data quality measures to safeguard their data assets, maintain customer trust, and avoid hefty regulatory fines. This regulatory-driven push has resulted in heightened awareness and adoption of data quality management solutions across various industry verticals, further contributing to market growth.

The growing emphasis on customer experience and personalization is also fueling the demand for data quality management software. As enterprises strive to deliver personalized and seamless customer experiences, the accuracy and reliability of customer data become paramount. High-quality data enables organizations to gain a 360-degree view of their customers, tailor their offerings, and engage customers more effectively. Companies in sectors such as retail, BFSI, and healthcare are prioritizing data quality initiatives to enhance customer satisfaction, retention, and loyalty. This consumer-centric approach is prompting organizations to invest in data quality management solutions that facilitate comprehensive and accurate customer insights, thereby driving the market's growth trajectory.

Regionally, North America is expected to dominate the data quality management software market, driven by the region's technological advancements and high adoption rate of data management solutions. The presence of leading market players and the increasing demand for data-driven insights to enhance business operations further bolster market growth in this region. Meanwhile, the Asia Pacific region is witnessing substantial growth opportunities, attributed to the rapid digitalization across emerging economies and the growing awareness of data quality's role in business success. The rising adoption of cloud-based solutions and the expanding IT sector are also contributing to the market's regional expansion, with a projected CAGR that surpasses other regions during the forecast period.

Component Analysis

The data quality management software market is segmented by component into software and services, each playing a pivotal role in delivering comprehensive data quality solutions to enterprises. The software component, constituting the core of data quality management, encompasses a wide array of tools designed to facilitate data cleansing, validation, enrichment, and integration. These software solutions are increasingly equipped with advanced features such as AI and ML algorithms, enabling automated data quality processes that si

Dataset

This dataset was created by Nimesh Kotadia

Contents

It contains the following files: