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 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 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.

*******, 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.

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.

If you continuously find you have a high customer churn rate, you’ll quickly find that your business isn’t sustainable. Getting new customers to sign up to your service is one thing, but it’s not enough to keep your business afloat for long. To survive, your business needs loyal customers, and that means continuously looking at ways you can improve your service to keep your customers happy. If you don’t, your business will become unviable.

What causes churn in telecoms?

• Poor service experience
• Poor customer service or experience
• Easy to switch providers ## How to reduce churn rate in telecoms?
• Improve customer service
• Create a memorable customer experience
• Invest in new technologies
• Make Better use of data

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Columns description:

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.

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

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

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.

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.

Dataset

This dataset was created by Nimesh Kotadia

Contents

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.

BackgroundThe COVID-19 pandemic resulted in a number of psychosocial and emotional catastrophes, including loneliness. The associated lockdowns, reduced social support, and insufficiently perceived interactions are expected to heighten the level of loneliness during the pandemic. However, there is a dearth of evidence regarding the level of loneliness and what correlates with loneliness among university students in Africa, particularly in Ethiopia.ObjectivesThe general objective of this study was to assess the prevalence and associated factors of loneliness among university students during the COVID-19 pandemic in Ethiopia.MethodsA cross-sectional study was undertaken. An online data collection tool was distributed to voluntary undergraduate university students. The sampling technique used was snowball sampling. Students were requested to pass the online data collection tool to at least one of their friends to ease data collection. SPSS version 26.0 was used for data analysis. Both descriptive and inferential statistics were used to report the results. Binary logistic regression was used to identify factors associated with loneliness. A P-value less than 0.2 was used to screen variables for the multivariable analysis, and a P-value less than 0.05 was used to declare significance in the final multivariable logistic regression.ResultA total of 426 study participants responded. Out of the total, 62.9% were males, and 37.1% attended fields related to health. Over three-fourths (76.5%) of the study participants encountered loneliness. Females (adjusted odds ratio (AOR): 1.75; 95% confidence interval (CI): 1.01, 3.04), non-health-related departments (AOR: 1.94; 95% CI: 1.17, 3.35), ever encountering sexual harassment (AOR: 3.32; 95% CI: 1.46, 7.53), sleeping problems (AOR: 2.13; 95% CI: 1.06, 4.30), perceived stress (AOR: 6.40; 95% CI: 1.85, 22.19) and poor social support (AOR: 3.13; 95% CI: 1.10, 8.87) were significantly associated with loneliness.Conclusion and recommendationA significant proportion of students were victims of loneliness during the COVID-19 pandemic. Being female, working in non-health-related fields, having sleeping problems, encountering sexual harassment, perceived stress, and poor social support were significantly associated with loneliness. Interventions to reduce loneliness should focus on related psychosocial support to reduce stress, sleeping disturbances, and poor social support. A special focus should also be given to female students.

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Employee Engagement Software Market Size 2024-2028

The employee engagement software market size is forecast to increase by USD 325.9 million, at a CAGR of 6.8% between 2023 and 2028.

The market is driven by the increasing need for workforce diversity management and the rising adoption of digital Human Resource (HR) technology. Companies are recognizing the importance of fostering an inclusive work environment and are turning to employee engagement software solutions to manage diversity initiatives, track progress, and promote equal opportunities. Additionally, the shift towards digital HR technology is gaining momentum, as organizations seek to streamline processes, enhance productivity, and improve employee experiences. However, this market also faces challenges.
Technical constraints, such as data security and privacy concerns, can hinder the adoption of employee engagement software. Moreover, poor customer service can negatively impact user experience and hinder the market's growth. To capitalize on opportunities and navigate these challenges effectively, companies must prioritize addressing these issues, ensuring robust data security measures and delivering exceptional customer service to maintain a competitive edge.

What will be the Size of the Employee Engagement Software Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market continues to evolve, with dynamic market activities shaping its landscape. Employee journey mapping tools enable organizations to track and optimize the employee experience, while productivity tracking software ensures efficient workflows. Integrated employee experience platforms offer mobile engagement, peer-to-peer feedback, goal setting, and performance review functionalities. Performance management systems, pulse survey software, and employee recognition programs foster continuous employee feedback and engagement. Knowledge sharing platforms, virtual recognition awards, and workplace collaboration tools promote a culture of innovation and learning. Culture building initiatives, HR analytics dashboards, and employee wellbeing platforms prioritize employee satisfaction and retention. Employee training platforms, team communication tools, talent management systems, and internal communications software streamline work processes and improve team coordination.
Engagement survey tools, employee onboarding systems, employee voice platforms, gamified engagement platforms, leadership development programs, and employee sentiment analysis tools further enhance the employee experience. These solutions adapt to the ever-changing needs of various sectors, ensuring a seamless and engaging employee journey. The integration of these tools fosters a productive and collaborative work environment, ultimately contributing to the overall success of an organization.

How is this Employee Engagement Software Industry segmented?

The employee engagement software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud-based
  On-premises


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

Cloud-based employee engagement software is experiencing significant growth due to its ability to provide a unified platform for gathering, storing, and accessing employee data from anywhere in the world. This includes features such as productivity tracking, goal setting, performance reviews, peer-to-peer feedback, and employee recognition programs. The use of cloud technology enables enterprises to accommodate unique HR requirements, ensure better reliability, and improve visibility into employee engagement metrics. Cloud-based solutions also offer advantages in terms of cost and flexibility. Instead of large, one-time investments and periodic expenses for maintenance and updates associated with on-premises software, cloud-based applications require regular payments.

This business model allows enterprises to allocate resources more effectively and adapt to changing needs. Additionally, cloud-based employee engagement software supports various tools and platforms, such as pulse surveys, knowledge sharing, team communication, and talent management systems. These tools contribute to a more immersive and harmonious employee experience, fostering a culture of collaboration, continuous learning, and open communication. Moreover, cloud-based solutions facilitate culture building initiatives, employee wellbeing platforms, and employee sentiment analysis, all essential components of a successful engagemen

Address Cleansing Software Market Outlook

According to our latest research, the address cleansing software market size globally was valued at USD 1.54 billion in 2024. The market is projected to grow at a robust CAGR of 11.2% from 2025 to 2033, reaching approximately USD 4.12 billion by 2033. This remarkable growth is primarily driven by the rising demand for accurate customer data management, regulatory compliance, and the increasing adoption of digital transformation initiatives across various sectors. The need for reliable address data to support advanced analytics, personalized marketing, and seamless delivery processes is propelling organizations worldwide to invest in cutting-edge address cleansing solutions.

One of the most significant growth factors for the address cleansing software market is the exponential increase in data generation and the need for data quality management. As organizations continue to collect vast amounts of customer and operational data, the potential for errors and inconsistencies in address information rises. Poor quality address data can lead to delivery failures, customer dissatisfaction, regulatory penalties, and increased operational costs. Address cleansing software offers automated tools to validate, standardize, and correct address records, ensuring that businesses can rely on accurate information for their critical processes. The integration of artificial intelligence and machine learning within these solutions further enhances their ability to detect anomalies, deduplicate records, and enrich datasets, making them indispensable in todayÂ’s data-driven environment.

Another crucial driver of market growth is the surge in e-commerce and omnichannel retailing, which demands impeccable address accuracy for logistics and customer engagement. The rapid expansion of online shopping and home delivery services has made address cleansing software essential for retailers and logistics companies aiming to minimize delivery errors and optimize route planning. Additionally, regulatory standards such as GDPR and CCPA require organizations to maintain accurate and up-to-date customer information, further fueling the adoption of address cleansing solutions. The ability to integrate address cleansing tools with CRM, ERP, and marketing automation platforms enhances the overall value proposition, enabling seamless data flow and improved decision-making across the enterprise.

The growing focus on customer experience and personalization is also accelerating the adoption of address cleansing software. Businesses across industries recognize that accurate address data is fundamental to delivering personalized communications, targeted offers, and responsive customer service. Address cleansing solutions not only validate and correct addresses but also enrich data with geolocation, demographic, and behavioral insights. This enables organizations to segment their audiences more effectively, execute location-based marketing campaigns, and provide tailored services that drive customer loyalty. The shift towards cloud-based software delivery models further simplifies deployment and scalability, making advanced address cleansing capabilities accessible to organizations of all sizes.

From a regional perspective, North America and Europe are leading the adoption of address cleansing software, driven by mature IT infrastructures, stringent data quality regulations, and high digitalization rates. However, Asia Pacific is emerging as a high-growth region due to rapid urbanization, the proliferation of e-commerce, and increasing investments in digital transformation. Countries such as China, India, and Japan are witnessing a surge in demand for address validation and geocoding solutions to support their expanding logistics, retail, and financial sectors. Meanwhile, Latin America and the Middle East & Africa are gradually embracing address cleansing technologies as businesses in these regions seek to improve operational efficiency and customer engagement. Regional disparities in data quality standards, infrastructure, and regulatory requirements present both challenges and opportunities for market participants.

As organizations continue to prioritize data quality and digital transformation, the role of Data Sanitization Software has become increasingly important. This type of software

This dataset contains results from 311 customer surveys. Someone who calls 311 for an issue is sent a small survey after the City believes it has addressed the issue. Not everyone is surveyed, due to some calls being anonymous, or not being able to locate the requester's mailing address.

 Results are provided on a 1-5 scale. 1 is unacceptable, 2 is poor, 3 is acceptable, 4 is good, 5 is excellent. 

 Because the cards are physically mailed out there is a time delay between when a service request is closed and when the City is able to enter the survey results into our system. This data set refreshed daily. 

 Multiple results per 311 case are possible due to multiple people requesting the same service for the same location. For example, if 10 people ask 311 to have the City repaint a crosswalk at 12th and Grand Street, each of them will be mailed a survey and the results will show in this dataset.