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 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.

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 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."

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 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 dataset contains transcribed customer support calls from companies in over 160 industries, offering a high-quality foundation for developing customer-aware AI systems and improving service operations. It captures how real people express concerns, frustrations, and requests — and how support teams respond.

Included in each record:

• Full call transcription with labeled speakers (system, agent, customer)
• Concise human-written summary of the conversation
• Sentiment tag for the overall interaction: positive, neutral, or negative
• Company name, duration, and geographic location of the caller
• Call context includes industries such as eCommerce, banking, telecom, and streaming services

Common use cases:

• Train NLP models to understand support calls and detect churn risk
• Power complaint detection engines for customer success and support teams
• Create high-quality LLM training sets with real support narratives
• Build summarization and topic tagging pipelines for CX dashboards
• Analyze tone shifts and resolution language in customer-agent interaction

This dataset is structured, high-signal, and ready for use in AI pipelines, CX design, and quality assurance systems. It brings full transparency to what actually happens during customer service moments — from routine fixes to emotional escalations."

The more you purchase, the lower the price will be.

Analyzing Customer Support Calls

Insights into Customer Interactions and Service Efficiency

Dataset Overview
This dataset contains transcriptions of customer support calls along with their sentiment labels, allowing for an analysis of customer interactions, common issues, and overall service efficiency. The dataset consists of the following columns:
- text: The transcription of the customer call.
- sentiment_label: The sentiment associated with the call, indicating whether it is neutral, negative, or other.

Sample Data Insights

Here are a few entries from the dataset:

Data Files

The dataset includes: - customer_call_transcriptions.csv: Contains call transcriptions and sentiment labels. - sample_customer_call.wav: A sample audio file for reference.

Analysis Objectives

• Assess common customer issues based on call transcriptions.
• Evaluate the sentiment of customer interactions to identify areas for service improvement.
• Analyze trends over time in customer support requests.

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:

During a global survey in the no third quarter of 2024, approximately ** percent of responding consumers chose service delivery issues as a reason for a poor experience with a brand. Communication problems and employee interactions followed, respectively selected by ** and ** percent of the respondents.

The dataset contains real-world customer feedback data collected from various digital channels like social media, customer service chats, and feedback forms on energy company websites. It includes interactions that capture customer sentiments, which are categorized into positive, negative, or neutral. The data also identifies the specific topics discussed, such as billing issues, service outages, or general support requests. This feedback serves to enhance customer engagement by understanding their needs and tailoring responses accordingly.

When Jennifer switched banks, it wasn’t because of a bad experience; it was because her new bank offered predictive financial insights tailored to her spending patterns. This story isn’t unique. Across the United States and globally, customers today expect more than standard banking; they seek personalized, digitally fluent, and emotionally...

The Bad Credit Loans Service market has emerged as a vital component of the financial landscape, catering specifically to individuals struggling with poor credit scores. This sector provides crucial financial solutions for those who may otherwise find themselves excluded from traditional lending avenues. As many con

Context

Customers freely express their thoughts in today's digital environment through online evaluations, which have a significant impact on how people perceive products and how they make judgments about what to buy. Companies use this input to learn what consumers enjoy, do not like, and anticipate from their goods and services. Data analysts, researchers, and machine learning enthusiasts who wish to investigate consumer behavior and sentiment trends might benefit from this dataset, which has been assembled to offer insightful information on customer experiences and sentiments.

Content

Along with significant variables including review text, ratings, sentiment labels, and other fields that represent the customer experience, this dataset includes customer reviews. A customer's feedback on a product is represented by each record, which indicates whether the client had a favorable, negative, or neutral experience. Sentiment analysis, text categorization, EDA, data visualization, and predictive model construction can all be done with this dataset. It provides an organized perspective of actual customer feedback to assist in identifying trends in customer preferences, product performance, and satisfaction.

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.

This dataset comprises natural, unscripted audio recordings from real-world customer service calls, providing high-quality material for training speech-aware AI systems, automatic transcription engines, and customer experience analytics.

All recordings are:

• Original, not synthetically generated
• Captured from real customer-agent interactions across 160+ industries
• Available in multiple formats (WAV) and aligned with transcriptions

Each record includes:

-Raw audio file of the customer service call (typically 3–15 minutes) -Accompanying human transcription and summary -Call duration, timestamp, and caller location (city, state, country) -Sentiment label: positive, neutral, or negative

Use this dataset to:

• Train speech-to-text models using real-world, non-scripted data.
• Build natural voice interaction systems or audio-aware LLMs
• Simulate real support situations for AI customer service agents.
• Perform acoustic sentiment analysis across tones, pauses, and speech speed.
• Support multilingual or accent-aware voice modeling (future expansion possible).

The dataset reflects the complexity of real communication: interruptions, overtalk, informal language, and emotional variation — perfect for teams working on voice interfaces, speech analytics, and conversational AI.

China Consumption Exp per Capita: Urban: Poor: Recreation, Educational & Cultural Service data was reported at 613.940 RMB in 2012. This records an increase from the previous number of 541.460 RMB for 2011. China Consumption Exp per Capita: Urban: Poor: Recreation, Educational & Cultural Service data is updated yearly, averaging 280.530 RMB from Dec 1985 (Median) to 2012, with 23 observations. The data reached an all-time high of 613.940 RMB in 2012 and a record low of 29.280 RMB in 1985. China Consumption Exp per Capita: Urban: Poor: Recreation, Educational & Cultural Service data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Consumption Structure by Income Level: Urban.

"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."