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.

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 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.

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.

This survey details U.S. consumers' decisions on whether or not to conduct a business transaction based on customer service experiences. Some ** percent of respondents said that they had decided not to make a purchase due to poor customer service experienced in the past year.

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.

United States CCI: Present Situation: sa: Business Conditions: Bad data was reported at 16.100 % in Apr 2025. This records a decrease from the previous number of 16.500 % for Mar 2025. United States CCI: Present Situation: sa: Business Conditions: Bad data is updated monthly, averaging 19.600 % from Feb 1967 (Median) to Apr 2025, with 637 observations. The data reached an all-time high of 57.000 % in Dec 1982 and a record low of 6.000 % in Dec 1968. United States CCI: Present Situation: sa: Business Conditions: Bad data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H049: Consumer Confidence Index. [COVID-19-IMPACT]

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.

Problem Statement

When consumers are not happy with some aspect of a business, they choose to reach out to the customer service and might raise a complaint. Businesses try their best to resolve the complaints that they receive. However, it might not always be possible to appease every customer.

Unhappy consumers might raise follow-up questions/complaints about the resolutions provided, and this is detrimental to the business as it points to systemic failures in the Customer Support division and could lead to poor brand image. Disputed complaints which are being/have been resolved could be a critical dataset to derive essential learnings for any business.

Predicting whether a complaint resolution will be accepted or rejected by a consumer can enable a business to proactively look at complaints which might be disputed and hence save unnecessary escalation as well as their reputation. Systemic issues can be identified by noticing which complaints have a higher potential to be disputed, and customer support agents can be trained to pay more attention or enhance the quality of communication for certain types of complaints.

The Consumer Financial Protection Bureau (CFPB) in the United States receives several consumers’ complaints about the dealings of financial companies. It sends these complaints about their products and services to them for eliciting a response. The CFPB makes sure that these complaints are published here soon after the company responds or after 15 days since sending the complaint to the company.

Dataset

You have been provided with a dataset containing the following columns –

● Date received: Date when the complaint was received

● Product: Type of product identified in the complaint, e.g., “Student loan”

● Sub-product: Type of sub-product identified in the complain

● Issue: The issue raised in the complaint, e.g., “Struggling to repay your loan.”

● Sub-issue: E.g., “Problem lowering your monthly payments.”

● Consumer complaint narrative: This is a consumer-submitted description of “what happened”. Reasonable steps have been taken to remove personal information that could be used to identify the consumer

● Company public response: The response to a consumer’s complaint. It can be from a pre-set list of options, e.g., “Company believes the complaint is the result of an isolated error”

● Company: For which the complaint is about

● State: Derived from the consumer’s mailing address

● ZIP Code: Derived from the consumer’s mailing address

● Consumer consent provided: Flag to specify whether the consumer allowed the publishing of their complaint description

● Submitted via: E.g., “Web” or “Phone.”

● Date sent to the company

● Company response to consumer

● Timely response: Flag specifying if the response was timely

● Consumer disputed: Flag specifying if the consumer disputed the resolution

● Complaint ID: Identifier for each complaint

Two files have been provided.

● Training Data: Consumer_Complaints_train.csv

● Test Data: Consumer_Complaints_test.csv

Dataset

This dataset was created by Nimesh Kotadia

Contents

It contains the following files:

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.

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

Colombia Consumer Price Index (CPI): Weights: Poor: Misc: Personal Care: Other data was reported at 4.000 % in 2024. This stayed constant from the previous number of 4.000 % for 2023. Colombia Consumer Price Index (CPI): Weights: Poor: Misc: Personal Care: Other data is updated yearly, averaging 4.000 % from Dec 2019 (Median) to 2024, with 6 observations. The data reached an all-time high of 4.000 % in 2024 and a record low of 4.000 % in 2024. Colombia Consumer Price Index (CPI): Weights: Poor: Misc: Personal Care: Other data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.I015: Consumer Price Index: by Class of Good and Services: COICOP: Dec2018=100: Weights.

The Customers Observing and Reporting Experience (CORE) program is an inspection program that rates facility conditions and customer service at over 300 of the City’s walk-in service centers. CORE scores are calculated using the following points scale: Excellent=100, Good=67; Fair=33; Poor=0.

Data Quality Management Service Market Outlook

The global data quality management service market size was valued at approximately USD 1.8 billion in 2023 and is projected to reach USD 5.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.1% during the forecast period. The primary growth factor driving this market is the increasing volume of data being generated across various industries, necessitating robust data quality management solutions to maintain data accuracy, reliability, and relevance.

One of the key growth drivers for the data quality management service market is the exponential increase in data generation due to the proliferation of digital technologies such as IoT, big data analytics, and AI. Organizations are increasingly recognizing the importance of maintaining high data quality to derive actionable insights and make informed business decisions. Poor data quality can lead to significant financial losses, inefficiencies, and missed opportunities, thereby driving the demand for comprehensive data quality management services.

Another significant growth factor is the rising regulatory and compliance requirements across various industry verticals such as BFSI, healthcare, and government. Regulations like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) necessitate organizations to maintain accurate and high-quality data. Non-compliance with these regulations can result in severe penalties and damage to the organization’s reputation, thus propelling the adoption of data quality management solutions.

Additionally, the increasing adoption of cloud-based solutions is further fueling the growth of the data quality management service market. Cloud-based data quality management solutions offer scalability, flexibility, and cost-effectiveness, making them an attractive option for organizations of all sizes. The availability of advanced data quality management tools that integrate seamlessly with existing IT infrastructure and cloud platforms is encouraging enterprises to invest in these services to enhance their data management capabilities.

From a regional perspective, North America is expected to hold the largest share of the data quality management service market, driven by the early adoption of advanced technologies and the presence of key market players. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, owing to the rapid digital transformation, increasing investments in IT infrastructure, and growing awareness about the importance of data quality management in enhancing business operations and decision-making processes.

Component Analysis

The data quality management service market is segmented by component into software and services. The software segment encompasses various data quality tools and platforms that help organizations assess, improve, and maintain the quality of their data. These tools include data profiling, data cleansing, data enrichment, and data monitoring solutions. The increasing complexity of data environments and the need for real-time data quality monitoring are driving the demand for sophisticated data quality software solutions.

Services, on the other hand, include consulting, implementation, and support services provided by data quality management service vendors. Consulting services assist organizations in identifying data quality issues, developing data governance frameworks, and implementing best practices for data quality management. Implementation services involve the deployment and integration of data quality tools with existing IT systems, while support services provide ongoing maintenance and troubleshooting assistance. The growing need for expert guidance and support in managing data quality is contributing to the growth of the services segment.

The software segment is expected to dominate the market due to the continuous advancements in data quality management tools and the increasing adoption of AI and machine learning technologies for automated data quality processes. Organizations are increasingly investing in advanced data quality software to streamline their data management operations, reduce manual intervention, and ensure data accuracy and consistency across various data sources.

Moreover, the services segment is anticipated to witness significant growth during the forecast period, driven by the increasing demand for professional services that can help organizations address complex dat

The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

The Great Britain Historical GIS Project has also produced digitised boundary data, which can be obtained from the UK Data Service Census Support service. Further information is available at census.ukdataservice.ac.uk

NHSDP is a five-year, USAID- and DFID-funded project designed to increase quality of and access to an essential package of health services (ESP) in Bangladesh, especially among poor and under-served rural and urban populations. In order to achieve this, the project will directly engage with the Sujer Hashi network of 26 service delivery NGOs to strengthen the delivery and local ownership of health services through the provision of clinical and organizational technical support and capacity building.

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.