According to a 2024 survey, over eight in ten Spanish consumers would engage with chatbots powered with generative AI technology to receive support. Italians followed with ** percent while another ** percent of Irish shoppers would use Gen AI chatbots for an element of customer service.

According to a survey conducted among users in the United States in May 2024, half of respondents reported that ease of access and convenience is the most appealing aspect of Artificial Intelligence (AI) chatbots used for metal health, wellness, and access to therapy. Around four in 10 respondents reported that privacy and confidentiality would be the second most appealing feature of using AI chatbots for mental health and receiving therapeutic assistance.

This dataset contains responses from a survey conducted for a master's thesis at Erasmus University Rotterdam. The survey investigated how consumer perceptions of privacy and trust in interactions with centralized versus decentralized AI-powered chatbots influence customer satisfaction. The survey included a predetermined simulated conversation with an AI-powered chatbot.Purpose of the Study:The main research question addressed in this study is: "How do consumer perceptions of privacy and trust in interactions with centralized versus decentralized AI-powered chatbots influence customer satisfaction?" The study aims to compare the differences in customer satisfaction, privacy concerns, and trust between centralized and decentralized AI-powered chatbots.Data Description:This dataset includes responses from 175 participants after data cleaning and removal of incomplete and biased responses. Participants were randomly assigned to one of three groups:Unaware of the chatbot typeInformed they would interact with a centralized chatbotInformed they would interact with a decentralized chatbotVariables:Customer Satisfaction: Measured with Likert scale questions on a 5-point scale from Strongly disagree to Strongly agree.Consumer Privacy Concerns: Measured with Likert scale questions on a 5-point scale from Strongly disagree to Strongly agree.Consumer Trust in AI-Powered Chatbots: Measured with Likert scale questions on a 5-point scale from Strongly disagree to Strongly agree.Consumer AI Familiarity: Measured with Likert scale questions regarding prior usage and understanding of AI technology on a 5-point scale from Strongly disagree to Strongly agree.Demographic Information: Age group, gender, highest education finished, nationality, and occupation.Chatbot Type: Categorical variable with values: 0 for not aware, 1 for aware of interacting with a centralized chatbot, and 2 for aware of interacting with a decentralized chatbot.Usage Notes:The dataset is provided in a XLSX file format and includes all necessary variables for analysis. The dataset can be used to conduct various statistical analyses, including descriptive statistics, hypothesis testing, and regression analysis.

One of the reasons behind AI-powered customer service is the preference for conversational AI over phone calls. In 2024, 82 percent of consumers stated they would use a chatbot instead of waiting for a customer representative to take their call. An outstanding 96 percent of surveyed shoppers believed that more companies should opt for chatbots over traditional customer support services.

Introduction

Conversational Commerce Statistics: Conversational commerce is transforming consumer-brand interactions through the use of messaging apps, chatbots, and voice assistants. The idea is to develop real-time, independent, and interactive communication to provide a seamless transition from online browsing to decision-making for purchasing.

In 2024, it will become an essential component of any digital commerce strategy worldwide. This article will indicate the key conversational commerce statistics and their trends.

Dataset Description:

This dataset comprises transcriptions of conversations between doctors and patients, providing valuable insights into the dynamics of medical consultations. It includes a wide range of interactions, covering various medical conditions, patient concerns, and treatment discussions. The data is structured to capture both the questions and concerns raised by patients, as well as the medical advice, diagnoses, and explanations provided by doctors.

Key Features:

• Doctor and Patient Roles: Each conversation is annotated with the role of the speaker (doctor or patient), making it easy to analyze communication patterns.
• Medical Context: The dataset includes diverse scenarios, from routine check-ups to more complex medical discussions, offering a broad spectrum of healthcare dialogues.
• Natural Language: The conversations are presented in natural language, allowing for the development and testing of NLP models focused on healthcare communication.
• Applications: This dataset can be used for various applications, such as building dialogue systems, analyzing communication efficacy, developing medical NLP models, and enhancing patient care through better understanding of doctor-patient interactions.

Potential Use Cases:

• NLP Model Training: Train models to understand and generate medical dialogues.
• Healthcare Communication Studies: Analyze communication strategies between doctors and patients to improve healthcare delivery.
• Medical Chatbots: Develop intelligent medical chatbots that can simulate doctor-patient conversations.
• Patient Experience Enhancement: Identify common patient concerns and doctor responses to enhance patient care strategies.

This dataset is a valuable resource for researchers, data scientists, and healthcare professionals interested in the intersection of technology and medicine, aiming to improve healthcare communication through data-driven approaches.

The dataset consists of responses collected via an online questionnaire targeting Generation Z individuals in Portugal. It focuses on understanding the adoption of AI-driven chatbots in the tourism and hospitality industries. The data includes demographic information, behavioral variables, and responses to constructs from the AI Device Use Acceptance (AIDUA) model, such as emotional reaction, performance expectancy, anthropomorphism, and social influence.

This statistic demonstrates the different ways that service organizations use artificial intelligence (AI) chatbots worldwide in 2018. During the survey, ** percent of organizations said they use AI chatbots for self-service in simple scenarios.

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Chatbot Market Size 2025-2029

The chatbot market size is forecast to increase by USD 9.63 billion, at a CAGR of 42.9% between 2024 and 2029.

The market is witnessing significant growth, driven by the integration of chatbots with various communication channels such as social media, websites, and messaging apps. This integration enables businesses to engage with customers in real-time, providing instant responses and enhancing customer experience. However, the market faces challenges, including the lack of awareness and standardization of chatbot services. Despite these obstacles, the potential benefits of chatbots, including cost savings, increased efficiency, and improved customer engagement, make it an attractive investment for businesses seeking to enhance their digital presence and streamline operations. Companies looking to capitalize on this market opportunity should focus on developing chatbot solutions that offer customizable features, seamless integration with existing systems, and natural language processing capabilities to deliver human-like interactions. Navigating the challenges of awareness and standardization will require targeted marketing efforts and collaborations with industry partners to establish best practices and industry standards.

What will be the Size of the Chatbot Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, with dynamic market dynamics shaping its growth and applications across various sectors. Conversational AI, a key component of chatbots, is advancing with the integration of sentiment analysis, emotional intelligence, and meteor score to enhance user experience. Pre-trained models and language understanding are being utilized to improve performance metrics, while neural networks and contextual awareness enable more accurate intent recognition. Deployment strategies, including policy learning and cloud platforms, are evolving to support cross-platform compatibility and multi-lingual support. Performance metrics, such as F1-score and response time, are crucial in evaluating model effectiveness. Reinforcement learning and knowledge base integration are essential for chatbot development and lead generation. Error rate and character error rate are critical in speech recognition, while API integration and dialogue state tracking facilitate seamless conversational experiences. Technical support and customer engagement are primary applications of chatbots, with sales conversion and automated responses optimizing business operations. Deep learning architectures and transfer learning are driving advancements in question answering and natural language processing. Contextualized word embeddings and dialogue management are essential for effective user interaction. Overall, the market is an ever-evolving landscape, with continuous innovation and integration of advanced technologies shaping its future.

How is this Chatbot Industry segmented?

The chatbot industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userRetailBFSIGovernmentTravel and hospitalityOthersProductSolutionsServicesDeploymentCloud-BasedOn-PremiseHybridApplicationCustomer ServiceSales and MarketingHealthcare SupportE-Commerce AssistanceGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKMiddle East and AfricaEgyptKSAOmanUAEAPACChinaIndiaJapanSouth AmericaArgentinaBrazilRest of World (ROW)

By End-user Insights

The retail segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth, particularly in the retail sector. E-commerce giants like Amazon, Flipkart, Alibaba, and Snapdeal are leading this trend, integrating chatbots to improve customer experience during online product searches. These AI-powered bots facilitate quick and effective resolution of payment-related queries, enhancing the shopping experience. However, retailers face challenges in ensuring a seamless user experience, as consumers increasingly prefer mobile shopping. Deep learning architectures and natural language processing (NLP) are crucial components of chatbot development. NLP enables intent recognition, sentiment analysis, and entity extraction, while deep learning models provide contextual awareness and dialogue management. Speech recognition and dialogue state tracking further enhance the user experience. Cross-platform compatibility and multi-lingual support are essential features for chatbots, catering to diverse user bases. Pre-trained models and transfer learning enable faster development and deployment. Reinforcement learning and policy learning optimize bot

As per Cognitive Market Research's latest published report, the Global Chatbot market size was USD 3.02 Billion in 2022 and it is forecasted to reach USD 24.58 Billion by 2030. Chatbot Industry's Compound Annual Growth Rate will be 21.58% from 2023 to 2030. Chatbot Market Dynamics

Key Drivers of Chatbot Market

High integration of chatbot in various industrial verticals:

Use of chatbots is rising exponentially in both the business sector as well as in consumer market. It is an instant messaging app that creates natural conversations between businesses and customers. The demand for chatbot has increased in recent years attributed to the rising inclination of people across the world towards online shopping. In online shopping platforms, sales team uses chatbots to answer non-complex product questions which helps in improving the satisfaction level and convenience of customers.

Moreover, the world is moving rapidly towards digitalization. Amid COVID-19 pandemic, the world has been turned totally into digital world. Hence, healthcare industry, like all other industries have started using chatbot aggressively which helps in connecting patients with hospitalists for general diagnosis and treatment. It also allows in scheduling appointments with physicians without needing to travel to the hospital.

Chatbot have been connected through websites, mobile applications, along with social media platforms which further drives the growth of market. As AI implementation in chatbot is rising, it is revolutionizing the business processes in multiple industries. AI-powered chatbot has thus no limits for its usage in various sectors, including BFSI, telecommunication, e-commerce, and others accrediting the growth market across the world.

Increasing need for customer analytics and emergence of messenger apps to drive the market

Key Restrains of Chatbot Market

Drawbacks regarding the full understanding of natural language:

In order to ensure that chatbot is providing correct and relevant information to the customers, it must be updated with the correct information. However, people in today's world widely uses shortforms out of their habit for speedy responses. Such kind of slangs or misspellings are frequently misunderstood by these chatbots. Hence, inability in understanding this kind of natural language may hamper the growth of chatbot market. However, rising use of cloud services by various enterprises will help chatbot to retrieve huge amount of data from the cloud which will enhance the understanding of natural language and further stimulating the growth of chatbot market.

Key Trends in Chatbot market:

AI chatbots with high emotional intelligence will drives the market in coming years:

Using artificial intelligence and real time data, chatbot is now able to do sentiment analysis by using facial emotion recognition, eye tracking technology and video interactions in real-time. This allows it to understand the mood, pitch, and feelings and customize their responses to deliver custom-made communication.

Thus, it will not be wrong to say that AI-powered chatbot is going to enhance values in business sectors by providing limitless applications in large, medium and small enterprises. When more companies use the cloud, their ability to manage customer interactions, data management, and internal communication effectively will greatly increase their business agility without having to worry about increased infrastructure costs or security risks.

What is the impact of the COVID-19 pandemic on Chatbot Market:

Advent of COVID-19 pandemic has reshaped the lives of people across the globe by changing the way of work, shop, and learn. Every sector has been impacted due to the sudden out-break of pandemic. Lockdowns were announced and many customer service centers were closed. Disruption in supply chain occurred and online services failed to handle additional volumes effectively. Hence, to handle this chaos effectively, companies started investing in new technologies to provide additional support and allow workers to adapt to work-from-home setups.

Lockdown during year 2020, embraced digital world like never before. Thus, digital literacy rate during the pandemic increases exponentially which results in stimulation of chatbot use. Retail businesses increases the use of chatbot during COVID-19 to fulfil consumer needs and giving retailers...

The chatbot market size is projected to grow from $ 5.84 billion in 2024 to $61.97 billion by 2035, representing a CAGR of 23.94% during the forecast period 2024-2035.

Mental Health Chatbot Pairs

AI-based Tailored Support for Mental Health Conversation

By Huggingface Hub [source]

About this dataset

This dataset contains a compilation of carefully-crafted Q&A pairs which are designed to provide AI-based tailored support for mental health. These carefully chosen questions and answers offer an avenue for those looking for help to gain the assistance they need. With these pre-processed conversations, Artificial Intelligence (AI) solutions can be developed and deployed to better understand and respond appropriately to individual needs based on their input. This comprehensive dataset is crafted by experts in the mental health field, providing insightful content that will further research in this growing area. These data points will be invaluable for developing the next generation of personalized AI-based mental health chatbots capable of truly understanding what people need

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How to use the dataset

This dataset contains pre-processed Q&A pairs for AI-based tailored support for mental health. As such, it represents an excellent starting point in building a conversational model which can handle conversations about mental health issues. Here are some tips on how to use this dataset to its fullest potential:

• Understand your data: Spend time getting to know the text of the conversation between the user and the chatbot and familiarize yourself with what type of questions and answers are included in this specific dataset. This will help you better formulate queries for your own conversational model or develop new ones you can add yourself.

• Refine your language processing models: By studying the patterns in syntax, grammar, tone, voice, etc., within this conversational data set you can hone your natural language processing capabilities - such as keyword extractions or entity extraction – prior to implementing them into a larger bot system .

• Test assumptions: Have an idea of what you think may work best with a particular audience or context? See if these assumptions pan out by applying different variations of text to this dataset to see if it works before rolling out changes across other channels or programs that utilize AI/chatbot services

• Research & Analyze Results : After testing out different scenarios on real-world users by using various forms of q&a within this chatbot pair data set , analyze & record any relevant results pertaining towards understanding user behavior better through further analysis after being exposed to tailored texted conversations about Mental Health topics both passively & actively . The more information you collect here , leads us closer towards creating effective AI powered conversations that bring our desired outcomes from our customer base .

Research Ideas

• Developing a chatbot for personalized mental health advice and guidance tailored to individuals' unique needs, experiences, and struggles.
• Creating an AI-driven diagnostic system that can interpret mental health conversations and provide targeted recommendations for interventions or treatments based on clinical expertise.
• Designing an AI-powered recommendation engine to suggest relevant content such as articles, videos, or podcasts based on users’ questions or topics of discussion during their conversation with the chatbot

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 | |:--------------|:------------------------------------------------------------------------| | text | The text of the conversation between the user and the chatbot. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

Global Artificial Intelligence (AI) Chatbot market size is expected to reach $31.11 billion by 2029 at 29.3%, segmented as by solution, ai chatbot software, ai chatbot platforms, natural language processing (nlp) solutions

Research Hypothesis:

The hypothesis is that service quality and trust significantly influence customer satisfaction with Telkomsel’s Veronika chatbot. Key dimensions include reliability, responsiveness, and empathy in service quality, and trust based on the chatbot's ability, benevolence, and integrity.

Data and Data Collection:

Data for this study were collected from Generation Z users who have experience using Telkomsel’s Veronika chatbot. A structured questionnaire was administered to 240 respondents, 52.9% of whom were female and 47.1% male, with ages ranging from 18 to 22 years. The data collection occurred between May and June 2024, and the questionnaire was distributed via social media platforms such as Instagram, Line, and WhatsApp. Non-probability sampling methods, specifically purposive and quota sampling, were used to ensure that only those familiar with the chatbot were surveyed.

The questionnaire comprised 31 questions designed to assess three key variables: service quality, trust, and customer satisfaction. A five-point Likert scale, ranging from "Strongly Disagree" to "Strongly Agree," was employed for all questions. Service quality was evaluated using the SERVQUAL model, while trust was measured through dimensions of ability, benevolence, and integrity. Customer satisfaction was assessed using items adapted from the Customer Satisfaction Index (CSI).

Key Findings:

1.Service Quality: A significant positive impact on customer satisfaction was found (β = 0.496, p < 0.001), with reliability and responsiveness being key factors. The highest loading (0.837) was on Veronika’s ability to provide alternative solutions.

2.Trust: Trust was also a significant predictor (β = 0.337, p < 0.001), with confidentiality being the most important trust factor (outer loading = 0.835).

3.Customer Satisfaction: Satisfaction was strongly influenced by both service quality and trust, with outer loadings from 0.908 to 0.918, particularly in terms of the chatbot's clarity and communication effectiveness.

Data Interpretation:

Both service quality and trust are essential to customer satisfaction, with service quality being a stronger predictor. Users value reliability and responsiveness more than trust, though both are necessary for high satisfaction. The reliability of the questionnaire was confirmed with high Cronbach’s alpha values, such as 0.938 for service quality.

Conclusion and Implications:

Improving service quality, especially reliability and responsiveness, will enhance user satisfaction. Strengthening trust, particularly in data security, is also crucial. Future research should explore broader demographics and long-term effects, while qualitative studies could offer more insights into user experiences.

The AI Therapy Chatbots market has emerged as a transformative force in mental health care, redefining how individuals access therapeutic support and guidance. As a fusion of artificial intelligence and behavioral therapy, these chatbots serve as readily available mental health companions, offering everything from p

The Chatbot Market report segments the industry into End-User Vertical (BFSI, Healthcare, IT and Telecommunication, Retail, Travel and Hospitality, Other End-user Verticals) and Geography (North America, Europe, Asia, Australia and New Zealand, Latin America, Middle East and Africa). Get five years of historical data alongside five-year market forecasts.

In a survey conducted across four Southeast Asian countries in February 2023, more than **** of the respondents in each country stated that they would likely or very likely use AI-powered chatbots such as ChatGPT for online search purposes in the future. At ** percent, Singapore had the highest share of respondents that were either unlikely or very unlikely to use chatbots in the future.

According to Cognitive Market Research, the global AI Chatbots market size will be USD 474.88 million in 2024 and will expand at a compound annual growth rate (CAGR) of 19.46% from 2024 to 2031.

The North America AI Chatbots market size was USD 1,336.33 Million in 2019 and it is expected to reach USD 12,529.12 Million in 2031.
The Europe AI Chatbots market size was USD 906.17 Million in 2019 and it is expected to reach USD 8,950.15 Million in 2031.
The Asia Pacific AI Chatbots market size was USD 831.48 Million in 2019 and it is expected to reach USD 8,776.80 Million in 2031.
The South America AI Chatbots market size was USD 146.70 Million in 2019 and it is expected to reach USD 1,341.50 Million in 2031.
The Middle East and Africa AI Chatbots market size was USD 74.69 Million in 2019 and it is expected to reach USD 662.37 Million in 2031.

Market Dynamics of AI Chatbots Market

Key Drivers for AI Chatbots Market

Advancements in AI and NLP Technologies are propelling the growth of AI chatbots Market

The rapid evolution of Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies has been a primary driver of growth in the global AI chatbot market. These advancements have significantly enhanced chatbot capabilities, enabling them to provide more human-like, context-aware, and efficient interactions. The introduction of deep learning models, transformer-based architectures, and generative AI has revolutionized how chatbots understand, process, and respond to human language. These are the reasons why players across the industry are focusing more on creating intuitive chatbot solutions. For instance, in October 2024, JSW and MG Motor collaborated with Google Cloud to launch gen Al chatbots. These are capable of understanding complex queries and responding with simple words to ensure the customer is satisfied with the response. Overall, the advancements in AI and NLP technologies have made AI chatbots more intelligent, efficient, and scalable, driving their widespread adoption across multiple industries. As AI continues to evolve with enhanced contextual learning, emotional intelligence, and ethical AI frameworks, the chatbot market is expected to experience sustained growth, further transforming customer service, automation, and digital engagement on a global scale.

Key Restraints for AI Chatbots Market

Integration challenges and data privacy concerns are restraining the growth of AI chatbots market

Despite the rapid adoption of AI chatbots across industries, integration challenges and data privacy concerns are key restraints limiting market growth. As businesses deploy AI chatbots to enhance customer engagement and automate processes, they often face complexities in integrating these solutions with existing enterprise systems, databases, and applications. Additionally, increasing concerns about data security, regulatory compliance, and ethical AI usage are raising barriers to widespread adoption. For instance, in April 2023, OpenAI taken ChatGPT offline in Italy after the government's Data Protection Authority temporarily banned the chatbot and launched a probe over the artificial intelligence application's suspected breach of privacy rules. These issues presents challenges for chatbot creators to align with the data security norms of the countries to function appropriately Overall, while AI chatbots offer immense potential for customer service automation and business efficiency, integration challenges and data privacy concerns remain significant roadblocks to their widespread adoption. Overcoming these restraints will require standardized AI frameworks, improved interoperability, stronger data security measures, and enhanced regulatory compliance strategies to unlock the full potential of AI chatbots Introduction of AI Chatbots Market

The global AI chatbots market is experiencing rapid expansion, fueled by advancements in artificial intelligence, natural language processing (NLP), and machine learning. Businesses across industries are adopting chatbots to enhance customer service, automate responses, and improve user engagement. The growing demand for AI-driven automation and personalized interactions is expected to continue driving the market forward. AI chatbots can be categorized into multiple types based on their functionality and capabilities. Q&A chatbots are the most common, designed to answer predefined questions based on r...

This is the data for AI-chatbot

The AI-powered chatbot market is experiencing robust growth, driven by the increasing adoption of automation across industries and the rising need for enhanced customer service and operational efficiency. The market, estimated at $15 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), exhibiting a Compound Annual Growth Rate (CAGR) of 25%. This growth is fueled by several key factors: the increasing sophistication of Natural Language Processing (NLP) and Machine Learning (ML) algorithms, leading to more human-like interactions; the rising demand for 24/7 customer support across various sectors, including e-commerce, healthcare, and finance; and the cost-effectiveness of AI chatbots compared to traditional human-based support systems. Significant market penetration is observed in large enterprises, which are adopting AI-powered chatbots for streamlining operations and improving customer engagement. However, the Small and Medium-sized Enterprises (SME) segment is also witnessing accelerated growth as chatbot solutions become more accessible and affordable. The messenger and web widget chatbot types dominate the market, demonstrating the adaptability of AI chatbots across various communication channels. Geographic expansion is another key driver. North America currently holds the largest market share, owing to early adoption and the presence of major technology companies. However, regions like Asia-Pacific and Europe are witnessing substantial growth, driven by increasing digitalization and investments in AI technologies. Despite the optimistic outlook, the market faces certain challenges. Concerns surrounding data privacy and security, the need for continuous improvement and training of AI models, and the initial investment costs involved in chatbot implementation can act as restraints. Nevertheless, the ongoing technological advancements and the growing demand for efficient customer service are expected to outweigh these challenges, propelling the market towards substantial growth in the coming years. Key players like IBM, [24]7.ai, Google, and others are constantly innovating and expanding their offerings, intensifying competition and further driving market expansion.