According to a global survey from 2024, the age group 25 to 34 is most likely to use chatbots when visiting brand websites. Approximately ** percent of users within this age group utilized chatbots on a direct-to-consumer (D2C) site. The age group between 35 and 44 ranked second, with nearly ** percent of respondents. Those aged 55 and 64 were the least likely to use this type of software application.

This statistic presents the results of a survey by LivePerson about the usage of chatbots in the last year in customer care conducted in Japan in April 2017. In the period examined, about **** percent of respondents stated to have used chatbots in customer care for fun, representing the majority of the survey objects. The second most common claim for chatbot usage was to receive customer support.

A survey in October 2022 in the United States, found out that ** percent of business-to-business (B2B) marketers using chatbots in their marketing programs gained between ten and 20 percent more lead generation volumes. Another ** percent said that they gained between five and ten percent increase in lead generation. An additional ** percent of American B2B marketers stated that chatbots helped them increase their lead generation with more than 30 percent.

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.

The data set is the data acquired through a survey among chatbot users of online travel agencies (OTAs) in India.

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 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.

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.

A dataset from an online studies on a simulated social trading platform using a chatbot to give participants advice on investment. 64 participants interacted with a chatbot across 4 conditions: human-like/not human-like, and with reply suggestion buttons/without reply suggestion buttons embedded in the user interface. They were shown 10 different portfolios to follow or unfollow at 5 separate month intervals, basing their decision on the advice of the chatbot or a separate news feed that would try to predict the next change in portfolio value. Participants were assigned an initial virtual balance of £1000. Image tagging was included as a distracting secondary task. All the messages exchanged to and from the chatbot are included, as well as the user actions and image tagging. Participant demographic data included in a separate file.

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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

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.

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.

In 2023, roughly ** percent of people in Germany said they would find a customer service chatbot useful for flights and hotels, as well as utility services. ** percent of people were not interested in the help of a chatbot. The rise of chatbots ChatGPT was launched in November 2022, and although chatbots existed prior, it was the first one that allowed users to dictate the length, and style of, as well as direct a conversation. Since this AI technology is so versatile, there are many different purposes for which it can be used. For example, some people use the software to help them understand complex theories they are learning for their studies, whilst others ask the chatbot to plan their meals for the week. Almost ** percent of ChatGPT users were aged 18 to 34 in 2023, whilst only **** percent were over the age of 55. When it comes to creating chatbots companies are facing challenges since the technology is new and highly complex. For most companies, the biggest difficulty is data management. This is due to the fact that so much data is required to train AI programs and when they are used, there is also a huge amount of data generated. Commercial usage of chatbots One industry that has been using chatbots for the past couple of years is the online shopping industry. The most popular function of chatbots among online shoppers globally was searching for product information. This was also the top result for consumers in Germany, followed by customer service and sending of updates about products. However, Germany did have a **************** of chatbots than the global average. Similarly, when it came to the share of those shopping online who considered chatbot customer service useful, Germany also ranked quite low, with only ** percent of respondents stating that they found it useful. Other countries such as India, UAE, and Indonesia had a *********** uptake rate.

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 Chatbot market size is expected to reach $29.5 billion by 2029 at 30%, chatbot market surges with the growing smartphone user base

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.

The chatbot market attained a value of USD 839.33 Million in 2024. The market is expected to grow at a CAGR of 24.90% during the forecast period of 2025-2034. By 2034, the market is expected to reach USD 7754.56 Million.

The chatbot market revenue is expanding with businesses increasingly recognizing the importance of providing round-the-clock customer service. Chatbots meet this demand by offering instant, consistent responses without breaks or delays. With global customers operating in different time zones, companies leverage chatbots to ensure uninterrupted support. In May 2025, French AI startup Mistral AI launched Le Chat Enterprise, a corporate-focused chatbot designed for deep integration with enterprise content systems and offer scalable solution for global businesses. Such launches improve customer satisfaction, reduces churn, and allows human agents to focus on complex issues.

The chatbot industry is revolutionizing e-commerce via conversational interfaces that guide users from product discovery to purchase. Consumers increasingly prefer real-time conversations over static websites, particularly on mobile. Brands use chatbots to simulate the in-store assistant experience, reducing cart abandonment and increasing sales. In June 2025, Walmart launched its new generative AI shopping chatbot Sparky to help users find, plan, compare, and repurchase products effortlessly. As digital transactions rise and mobile commerce dominates, the need for interactive, conversational sales channels propels the market forward.

Chatbot Arena Conversations Dataset

This dataset contains 33K cleaned conversations with pairwise human preferences. It is collected from 13K unique IP addresses on the Chatbot Arena from April to June 2023. Each sample includes a question ID, two model names, their full conversation text in OpenAI API JSON format, the user vote, the anonymized user ID, the detected language tag, the OpenAI moderation API tag, the additional toxic tag, and the timestamp. To ensure the safe release… See the full description on the dataset page: https://huggingface.co/datasets/lmsys/chatbot_arena_conversations.

This study explored the usability and desirability of an AI-driven chatbot designed to support adults with hearing loss. A mixed-methods exploratory study was conducted, incorporating mobile analytics, in-app feedback, and qualitative interviews. A prototype AI-driven chatbot was developed using GPT Creator and integrated into a simple app interface. Participants used the chatbot over a 2-week period, with their interactions and feedback recorded for analysis. Ten adults with hearing loss (mean age: 58.6 years, SD = 8.7) participated in the study. Participant individual chatbot usage ranged from one to eight interactions. Key areas of inquiry included hearing aid functionality, tinnitus management, and audiologist-related concerns. The chatbot was perceived as user-friendly and useful for basic support, but experienced users desired more personalised responses. Suggested improvements included conversation memory, better handling of multiple questions, and enhanced voice-to-text functionality. This study provides preliminary evidence that AI-driven chatbots may offer valuable support for adults with hearing loss. While usability and desirability were generally favourable, enhancements in personalisation and accessibility are needed to improve engagement and long-term adoption. Future iterations should incorporate user-centred refinements to maximise the chatbot’s effectiveness in hearing health management.

215296273/2021/12As chatbots become more popular, the insurance industry has adopted their use. Although chatbot has been used a lot in customer relationship management (CRM), there is a lack of data security and privacy control strategies for data in chatbots. During data exchange, the client's data may be compromised through computer security breaches, thus exposing the client to possible fraud and theft. The lack of data security and privacy control strategies for data in chatbots has become a major security concern in financial services institutions. Chatbots access a lot of company and client information and that makes the data contained in chatbots to be the target of hackers which can cause harm to companies and customers.This study explored how data security in chatbots in South African insurance organisations can be attained. To realise the aim of this study, five objectives were formulated as follows, to: 1) identify the potential use cases of chatbots for CRM in a South African insurance organisation; 2) identify the challenges of securing data in a chatbot in a South African insurance organization; 3) determine the security goals, threats, and vulnerabilities associated with the use of chatbots in a South African insurance organisation; 4) develop a threat model for the security and privacy of data in chatbots for a South African insurance organization; and 5) evaluate the threat model for security and privacy of data in the chatbots for a South African insurance organisation.The mixed-methods research methodology was adopted for the study. A case study research strategy that involved data collection from a South African insurance company was used. Semi-structured interviews were conducted with participants that were purposively selected. Also, the STRIDE modelling approach was used to collect data on the security threats and vulnerabilities that pertain to each insurance use case with for each component of STRIDE — Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege. Based on the outcome of the STRIDE modelling, a threat model for data security in chatbots for the South African insurance industry was developed using the Attack Defence tool. The threat model reveals the data security threats in chatbots, and how they can be mitigated. An evaluation of the threat model was conducted using security experts who assessed the quality of the threat model. They also provided qualitative feedback on the threat model. The evaluation of the threat model adopted the System Usability Scale (SUS) questionnaire which is a standard questionnaire to evaluate a system or product. The SUS score for each evaluator was calculated, and a mean SUS score was obtained.From the expert evaluation, the developed threat model for data security in insurance chatbots obtained a mean SUS of 79.4 which corresponds to a grade B rating, which is a good rating based on the rules for the SUS scores. From the qualitative feedback, the security experts observed that the threat model can help to improve overall security and protect against potential attacks, and also proactively identify and mitigate potential threats in chatbots.The insurance industry and academia will benefit from this study. Insurance organisations can implement security using the proposed threat model for the security of data in their business chatbots. Also, this study contributes new information to the body of knowledge since this is the first study to develop a threat model for data security in