Estimates indicate that global spending on conversational commerce channels will total about 41 U.S. billion dollars in 2021. That figure was forecast to grow almost sevenfold by 2025, amounting to some 290 billion U.S. dollars.

In 2022 gross merchandise value (GMV) of conversational commerce amounted to 12 billion U.S. dollars. The projected total value of conversational commerce GMV in Southeast Asia for 2027 was 23 billion U.S. dollars.

According to a survey on usage of conversational commerce conducted in August 2022, 34 percent of Indonesian respondents stated that they had used direct chat to purchase goods less in the last three months. In comparison, seven percent of them have used it more in the last three months.

Commerce Platform Market size was valued at USD 10.2 Billion in 2023 and is projected to reach USD 27.5 Billion by 2031, growing at a CAGR of 21.2% during the forecast period 2024-2031.

Global Conversational Commerce Platform Market Drivers

The market drivers for the Conversational Commerce Platform Market can be influenced by various factors. These may include:

Growing Adoption of Messaging Apps: The increasing user base of messaging applications is a significant driver for the Conversational Commerce Platform Market. With platforms like WhatsApp, Facebook Messenger, and WeChat becoming integral to daily communication, businesses are leveraging these channels to engage with customers. This shift towards familiar communication tools allows brands to enhance user experience, providing personalized customer interactions, instant responses, and proactive service. As enterprises recognize the need for omnichannel engagement, they are investing in platforms that can integrate seamlessly with these applications, thus expanding their reach and facilitating real-time communication that can lead to improved sales conversions.

Rising Demand for Personalized Customer Experiences: Consumers increasingly expect personalized interactions with brands they engage with. The Conversational Commerce Platform Market is experiencing growth as businesses strive to deliver tailored experiences through AI-driven chatbots and automated agents. These tools can analyze customer preferences and behavior, offering recommendations and support that resonate with individual needs. This personalization enhances customer satisfaction and loyalty, driving repeat purchases and fostering long-term relationships. As more businesses implement tailored communication strategies, the demand for sophisticated conversational commerce solutions is expected to rise significantly, positioning this market for continued expansion in the coming years.

Global Conversational Commerce Platform Market Restraints

Several factors can act as restraints or challenges for the Conversational Commerce Platform Market. These may include:

Data Privacy Concerns: As consumers become increasingly aware of their data privacy rights, the Conversational Commerce Platform Market faces significant challenges. Companies must navigate stringent regulations, such as GDPR and CCPA, which impose severe penalties for non-compliance. This creates an environment where organizations are hesitant to collect and utilize personal data, limiting their ability to deliver personalized services. Additionally, breaches or misuse of customer data can lead to loss of trust, damaging reputations and resulting in customer attrition. Addressing these privacy concerns mandates substantial investment in security measures, compliance infrastructure, and transparent communication with users about data use and protection.

Technological Limitations: The effectiveness of conversational commerce platforms is heavily reliant on the underlying technology, such as natural language processing (NLP) and artificial intelligence (AI). However, these technologies are still evolving, which can lead to misunderstandings in customer interactions or limited responses from chatbots. Some platforms may struggle to integrate seamlessly with existing systems, hampering their potential to deliver a cohesive omnichannel experience. Additionally, as technology advances rapidly, keeping conversational tools updated and relevant can prove costly and resource-intensive. These technological constraints may hinder user experience, resulting in missed opportunities and reduced engagement with customers.

Conversational Commerce Market is estimated to reach USD 52.8 Billion By 2034, Riding on a Strong 19.6% CAGR throughout the forecast period.

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.

Conversational commerce is forecast to have widespread significance for customer relations and the consumer experience via social media; this will fuel its growth as consumers increasingly expect a dialogue (even if with a sophisticated, learning AI) and the ability to seamlessly buy the products and services they want within that medium. For FMCG, retail, and foodservice, this means opportunities to brand build and grow sales, but also challenges them to integrate such tools successfully. Read More

A survey conducted globally among retail consumers in 2023 shows the reasons why consumers enjoy conversational commerce powered by artificial intelligence (AI). Over 80 percent of customers enjoy the fact that even while using this tool they have their personal data protected, and around 80 percent like when the tool explains the reasons for recommending such products.

Introduction

Welcome to the Canadian English Call Center Speech Dataset for the Retail domain designed to enhance the development of call center speech recognition models specifically for the Retail industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 hours of call center audio recordings covering various topics and scenarios related to the Retail domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Retail domain call center conversational AI and ASR models for the Canadian English language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of

According to a survey released in 2024, about one-third of interactions with e-commerce AI chatbots were related to product information such as product advice, product availability, or product details. Another 20 percent of analyzed interactions covered order and shipping, whereas four percent of them were conversations to arrange a product return.

According to a global 2022 survey, Indian consumers appreciated customer service via chatbots the most, with 36 percent of them finding it useful when shopping on mobile devices. Respondents from the United Arab Emirates and Indonesia followed with 30 percent each, while 27 percent of Mexican shoppers had the same opinion. Scandinavian respondents showed the biggest skepticism regarding the use of this AI tool in mobile shopping, as only eight percent of consumers in both Denmark and Sweden valued chatbots.

Chatbots drive conversational commerce

By simulating natural language, chatbots go under so-called conversational commerce, a shopping channel expected to generate increasing revenue in the upcoming years. All players, from marketplaces to online e-commerce brands, implemented chatbots to automate pre-and post-sale services. In Europe, one in five direct-to-consumer (D2C) e-commerce companies planned to invest in Artificial Intelligence (AI) and chatbots, a survey from 2022 revealed.

Messaging apps in conversational commerce

Even more than chatbots, established messaging apps such as WhatsApp or chat apps connected to social media are the main tools for conversational commerce. In Southeast Asia, 27 percent of online consumers used Facebook for this type of shopping in 2022. Besides product recommendations, messaging apps are used for information on delivery as an alternative to email or SMS. In 2021, WhatsApp remained the third-most preferred channel for delivery notifications in Europe.

The Conversational AI market for retail and e-commerce is experiencing rapid growth, driven by increasing consumer demand for personalized and efficient customer service experiences. This is fueled by the rising adoption of chatbots, virtual assistants (IVAs), and other conversational AI technologies across large enterprises and SMEs. The market's expansion is further propelled by advancements in natural language processing (NLP) and machine learning (ML), enabling more human-like interactions and improved customer understanding. While precise figures for market size aren't available from the provided data, a reasonable estimation based on industry reports and the stated CAGR (let's assume a CAGR of 25% for illustrative purposes, this should be replaced with the actual CAGR from the source data) suggests significant expansion. If we assume a 2025 market size of $5 billion (this needs to be replaced with the actual value from the provided data), the market would reach approximately $10 billion by 2028, growing to over $20 billion by 2033 based on the assumed 25% CAGR. The market is segmented by solution type (IVA, Chatbots) and application (large enterprises, SMEs), with significant opportunities across various geographical regions. North America and Europe currently dominate, but strong growth is anticipated in Asia-Pacific driven by expanding e-commerce markets and increasing technology adoption. The continued success of conversational AI in retail and e-commerce hinges on factors like improved accuracy in natural language understanding, seamless integration across various platforms, and enhanced security and data privacy measures. Challenges include the need for ongoing training and maintenance of AI models, concerns regarding bias in algorithms, and the need to effectively manage customer expectations regarding AI capabilities. Companies such as Google, Microsoft, IBM, and Amazon Web Services are at the forefront of innovation, offering a range of solutions to meet the evolving needs of businesses in this dynamic market. Competitive landscape analysis highlights strategic partnerships, acquisitions, and technological advancements as key drivers shaping the market's future. The focus will remain on improving personalization, enhancing customer experience, and streamlining operational efficiency through AI-powered conversational interfaces. This will create numerous opportunities for both technology providers and retail businesses alike.

Introduction

Welcome to the Egyptian Arabic Call Center Speech Dataset for the Retail domain designed to enhance the development of call center speech recognition models specifically for the Retail industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 40 hours hours of call center audio recordings covering various topics and scenarios related to the Retail domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Retail domain call center conversational AI and ASR models for the Arabic language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Arabic call

In a 2022 report on social commerce conducted across Southeast Asia, 27 percent of respondents used Facebook for conversational commerce. Moreover, over 20 percent of the respondents used WhatsApp for conversational commerce in Southeast Asia in 2022.

The Conversational AI in Retail market is experiencing a transformative renaissance as businesses increasingly recognize the power of artificial intelligence to enhance customer engagement, streamline operations, and provide a personalized shopping experience. This technology, which leverages natural language proces

In 2023, close to six out of ten global industry decision-makers had already integrated generative artificial intelligence to generate product recommendations utilized by associates in physical stores. Meanwhile, 39 percent were in the process of evaluating its adoption. Moreover, 55 percent employed generative Artificial Intelligence (AI) to develop conversational digital shopping assistants, 52 percent utilized it for constructing virtual models for product pages, and 51 percent applied it to curate personalized product bundles.

AI-driven personalization Utilizing artificial intelligence to craft personalized shopping experiences has become a cornerstone strategy for e-commerce retailers. In 2023, nine of ten businesses surveyed worldwide employed AI-driven personalization to fuel growth. To measure the success of AI in personalization, companies primarily look at the accuracy and speed of real-time data alongside metrics like customer retention and repeat purchases. As AI technologies advance, the potential for increasingly refined and impactful personalization within e-commerce will expand even further.

The consumer experience AI helps e-commerce businesses understand and respond to consumers' preferences, needs, and behaviors. One crucial area of online shopping where people anticipate AI improvements is price comparison, as indicated by half of the participants in a 2023 survey. Consequently, consumers are eager to uncover relevant promotions, offers, and products. However, the swift pace of these advancements also breeds skepticism among online shoppers, especially among older demographics, many of whom express discomfort with this technology's use for personalization.

India conversational AI market size reached USD 516.8 Million in 2024. Looking forward, IMARC Group expects the market to reach USD 4,936.9 Million by 2033, exhibiting a growth rate (CAGR) of 26.4% during 2025-2033. The increasing automation of business operations and numerous technological advancements are primarily driving the market growth across the country.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2025-2033. Our report has categorized the market based on component, type, technology, deployment mode, organization size, and end user.

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.

As of December 2023, the company ASAPP was the most funded chatbot/ conversational AI worldwide, with around 380 million U.S. dollars. By contrast, the next company operating in the same field had a little over 300 million U.S. dollars.

What are AI chatbots?

A chatbot, also known as a conversational bot, is an AI software that simulates human conversation via audio or text on the internet. They are designed to answer basic questions, recommend products, and provide customer support so that organizations and companies can save manpower, money, and time. Recent developments have produced more advanced chatbots that utilize deep learning algorithms to produce answers to complex problems and questions. There are different types of chatbots, such as menu-based, keyword-based, social messaging, and voice bots. Popular chatbots are Netomi, atSpoke, and the new ChatGPT, which was launched in November 2022.

Artificial Intelligence

Artificial intelligence (AI) is the ability of a computer or machine to mimic human competencies, learning from previous experiences to understand and respond to language, decisions, and problems. A growing number of companies and startups are engaging in the artificial intelligence market, which is expected to grow rapidly in the near future. Popular tech companies involved in the industry are IBM, Microsoft, and Tencent, which owns the highest number of AI and ML patent families.

Introduction

Welcome to the Odia Scripted Monologue Speech Dataset for the Retail & E-commerce Domain. This meticulously curated dataset is designed to advance the development of Odia language speech recognition models, particularly for the Retail & E-commerce industry.

Speech Data

This training dataset comprises over 6,000 high-quality scripted prompt recordings in Odia. These recordings cover various topics and scenarios relevant to the Retail & E-commerce domain, designed to build robust and accurate customer service speech technology.

Each scripted prompt is crafted to reflect real-life scenarios encountered in the Retail & E-commerce domain, ensuring applicability in training robust natural language processing and speech recognition models.

Transcription Data

In addition to high-quality audio recordings, the dataset includes meticulously prepared text files with verbatim transcriptions of each audio file. These transcriptions are essential for training accurate and robust speech recognition models.