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Understanding Chatbots: From Customer Service to Conversational AI Innovation
By this point, it’s probably safe to assume that virtually every customer in the world with Internet access has talked to a chatbot at least once in their lives. A chatbot is a software application designed to simulate human conversation, primarily used in a customer service automation context to handle tasks such as answering queries, providing information, and resolving issues. Over the last year, Conversational AI has undoubtedly become one of the most popular and talked about technologies for business owners or decision makers, developers, and also the general public.
When you think about possible chatbot uses for businesses, probably the first thing that comes to mind is customer service: you have an issue with a product or reservation, you contact the company, and a virtual assistant asks you questions to filter your query before directing you to a human agent. However, as you will see throughout this article, the advantages of using chatbots for businesses go beyond its applications in customer service.
Chatbots are increasingly prevalent across different industries and new uses for them emerge almost every day: from addressing customer service queries in the retail and contact centre space to a private healthcare virtual assistant or AI Agent that gathers information about a patient’s symptoms to streamline diagnosis, helps patients book appointments, and sends reminders, or that little window that pops out on your laptop or phone asking if you need help while you visit a company or brand’s website, Chatbot solutions are more ubiquitous and versatile than one might judge at first sight.
However, many still harbour scepticism or even some deal of mistrust towards this technological feature. Some customers, for example, fear that chatbots deployed for customer service purposes might not fully understand their questions. Others, particularly special ists in the customer service space, worry that as chatbot technology advances, their roles will become completely automated. Others wonder what’s the role of Artificial Intelligence (AI) when it comes to chatbot technology: they might not know, for example, whether all chatbots use AI or it’s just some of them, or why that is the case.
We’ll clarify all those questions in this article. We’ll also explain everything there is to know about chatbots, from how they work to the role Conversational AI plays in their performance and what are the different types of chatbots, as well as how businesses can use chatbots to enhance their operations and the advantages and disadvantages of each different use.
But first, let’s start with the basics: what is a chatbot? In the first section, we’ll sketch a detailed definition of the concept.
Summary
Chatbots are software applications that simulate human conversation, mainly to automate customer service tasks like answering queries and providing information. While customer service is the most common use, chatbots are increasingly applied across industries, from healthcare assistants to website support pop-ups. Despite their growth, some users remain skeptical about their understanding, job automation, or the role of AI. This article explains how chatbots work, the role of Conversational AI, types of chatbots, and how businesses can use them to enhance operations.
Chatbots Then and Now: From ELIZA to LLMs
A chatbot is a computer program designed to simulate human conversation—whether written or spoken—so that interacting with a digital system feels natural. They range from simple automated responders to sophisticated AI assistants that can learn, adapt, and personalise their responses.
Although chatbots seem like a recent innovation, their roots go back to the early days of AI. In 1950, Alan Turing proposed a conversational test: if a machine could converse convincingly enough to fool a human, it could be considered intelligent. The first significant exploration of this idea came in 1966 with ELIZA, an NLP program developed at MIT by Joseph Weizenbaum. ELIZA relied on basic pattern-matching and scripted replies, yet many users felt it genuinely “listened,” demonstrating how easily humans attribute understanding to even rudimentary systems. Its limitations were obvious, however: it couldn’t move beyond its predefined loops, and no expert considered it truly intelligent.
The real transformation began in the 2010s with deep learning, which revolutionised natural language processing. Progress accelerated after 2017 with the introduction of transformer architectures, whose self-attention mechanisms allowed models to analyse entire passages of text rather than processing them sequentially. This enabled much greater fluidity, coherence, and context awareness than previous methods.
Modern chatbots are built on this architecture and trained on massive text corpora—from books and articles to web pages and transcripts. Through this pretraining, models learn patterns of grammar, reasoning, dialogue, and style, giving them a remarkable ability to generalise. They are then fine-tuned using methods like Reinforcement Learning from Human Feedback, where human evaluators guide the model toward responses that feel natural, useful, and aligned with human expectations. This alignment is crucial for turning a raw language model into a reliable conversational agent.
Recent advances have added layers of specialisation. Chatbots can now be fine-tuned for specific domains—customer service, education, legal guidance, or technical support—allowing them to function as both general conversationalists and domain experts. Multimodal models extend this further, integrating text, images, audio, video, diagrams, and code into a single, continuous conversation. Retrieval-augmented systems let chatbots query external databases in real time, ensuring up-to-date responses, while personalisation techniques allow them to adapt to users’ tone, interests, and prior interactions.
Together, these developments represent a break from the era of scripted bots. Today’s chatbots can sustain long, coherent dialogues, perform multi-step reasoning, handle complex tasks, and seamlessly switch across modalities. By 2024, the gap between human and machine conversation had narrowed significantly. Whether they meet Turing’s threshold of intelligence is still debated, but modern chatbots already perform tasks that only human agents could handle a few years ago—and ongoing improvements promise even greater sophistication.
Summary
Chatbots, software applications designed to simulate human conversation, have become nearly ubiquitous for anyone with Internet access, primarily serving in customer service but increasingly extending to diverse business functions like healthcare, education, and technical support. While some skepticism remains—ranging from concerns about comprehension to fears of job automation—modern chatbots have evolved far beyond simple scripted responses. Their development accelerated with deep learning and transformer architectures, allowing them to understand context, generate coherent dialogue, and adapt to users. Today’s chatbots can be fine-tuned for specific domains, integrate multiple types of media, query external databases in real time, and personalise interactions, making them versatile tools that bridge the gap between human and machine communication and transforming the way businesses interact with customers.
From Scripts to AI: How Chatbots Really Work Today
Well, that question would require a quite nuanced answer.
There are many different types of chatbots, and they can be categorised either by the type of technology they rely on or by their use. Some of them rely on Artificial Intelligence and some don’t; some of them are built for specific purposes and others are more general; some depend on certain types of algorithms while others don’t, and so on.
The earliest chatbots, like ELIZA, were basic interactive programs with a very limited register of responses, largely predefined by their developers. They responded to a set of common questions with pre-written answers and relied on users choosing keywords to guide the conversation.
In the case of ELIZA, you have seen how that worked: for the first businesses that used these early chatbots as a part of their customer service resources, they looked more or less like interactive FAQ guides. These early chatbots struggled with complex queries and couldn’t handle questions that developers hadn’t anticipated.
As time passed, chatbot algorithms evolved, incorporating more sophisticated rules-based programming and NLP (Natural Language Processing). This ushered in a new breed of chatbots that were contextually aware and equipped with Machine Learning (ML), enabling them to continually improve their ability to understand and predict user queries based on exposure to human language.
Modern AI chatbots go a step further by employing natural language understanding (NLU) to grasp the meaning of open-ended user input, overcoming issues like typos or language translation challenges. Advanced Conversational AI models map this meaning to the specific “intent” the user wants the chatbot to address, using conversational AI to generate an appropriate response.
These technologies, blending machine learning and deep learning, build a detailed knowledge base of questions and responses through user interactions. This level of sophistication, fueled by recent advancements in large language models (LLMs), has significantly boosted customer satisfaction and expanded the range of applications for chatbots.
Simply put, an effectively designed chatbot will:
Use past conversation data to comprehend the kinds of questions users typically pose.
Evaluate accurate responses to those questions.
Employ Machine Learning and Natural Language Processing (NLP) to grasp context, progressively enhancing its ability to provide better answers over time.
However, as we have said earlier, not all chatbots use Artificial Intelligence. At this point, it would be useful to consider one question: what’s the difference between a chatbot and Conversational AI?
Summary
Chatbots range from simple rule-based programs to advanced AI systems. Early examples like ELIZA used predefined responses and struggled with unexpected queries. Modern chatbots leverage AI, Natural Language Processing (NLP), and Machine Learning to understand user intent, handle typos or language variations, and improve through interactions. Advanced conversational AI can generate context-aware responses, build knowledge over time, and enable more accurate, personalized interactions, enhancing customer satisfaction and automating complex tasks beyond basic support.
Chatbot vs Conversational AI: What’s the difference?
Sometimes, the terms “chatbot” and “Conversational AI” are used interchangeably, or it’s assumed that all chatbots incorporate some form of Artificial Intelligence. However, that couldn’t be farther from the truth.
In its general sense, a chatbot is a computer program engaging in conversations with users. As we have seen earlier, conversations may be rule-based or incorporate more advanced technologies, like natural language processing (NLP) and machine learning, to enhance responsiveness.
However, not all chatbots necessarily integrate sophisticated AI components. Some, which we may call “traditional” or “basic” chatbots, operate on simpler rule-based systems mostly based on keyphrase or keyword recognition, like ELIZA. They are also known as task-oriented or declarative chatbots; they are often designed for a single purpose or particular set of purposes, focusing on executing a specific function.
These chatbots depend on predefined rules, Natural Language Processing (NLP), and sometimes minimal Machine Learning (ML) to produce automated yet conversational responses to user queries. They’re characterised by specificity and structure, making them particularly suitable for support and service functions. Again, you can think of them as comprehensive and interactive FAQs. They’re proficient in addressing uncomplicated questions, such as queries about business hours or straightforward transactions with limited variables. Despite incorporating NLP for a conversational user experience, their capabilities are fundamentally limited.
Conversational AI, on the other hand, is a comprehensive term encompassing a wide range of technologies and systems designed to facilitate natural language interactions between humans and computers. An AI chatbot, within the context of conversational AI, explicitly denotes integration with advanced AI capabilities. Some examples of these include:
Machine Learning (ML): Allows chatbots to learn from interactions, predict responses, and make decisions without explicit programming, enhancing their adaptability to user queries.
Deep Learning (DL): Enables chatbots to recognize complex patterns in user interactions, improving accuracy and sophistication in understanding conversational nuances.
Natural Language Understanding (NLU): Equips chatbots to interpret human language in context, understand user intent, and provide relevant, context-aware responses, making conversations more human-like.
Transformers: Modern architectures like GPT and BERT allow chatbots to process all words simultaneously, maintain context across long conversations, and generate coherent, contextually relevant responses.
Self-Supervised Learning: Trains chatbots on large amounts of unlabelled text by predicting missing parts, giving them a broad understanding of language, grammar, and facts without relying on costly human annotation. This makes chatbot responses more fluent and informed.
Reinforcement Learning from Human Feedback (RLHF): Fine-tunes chatbots using human-ranked outputs, aligning their behavior with user expectations. This improves safety, relevance, and coherence in chatbot responses.
Retrieval-Augmented Generation (RAG): Enhances chatbots by combining language generation with real-time information retrieval, allowing them to provide accurate, up-to-date, and domain-specific answers beyond what they were originally trained on.
In short, Conversational AI chatbots excel in one thing that sets them apart from “traditional” or “basic” chatbots: their ability to learn from user interactions, adapting to diverse contexts, and progressively improving performance over time.
But yet, there is another concept, “virtual agent” that is sometimes mistakenly taken to be synonymous with chatbot. Let’s clarify the distinction.
Chatbot vs AI Agent: What's the difference?
Virtual agents represent one step beyond when it comes to the implementation of AI chatbot software.
AI agents are autonomous software programs that perform tasks independently using advanced AI techniques. Like AI Chatbots, AI Agents also use Conversational AI for interactive dialogues and incorporate deep learning to refine their capabilities over time. What distinguishes virtual agents is their integration of robotic process automation (RPA) within a unified interface, allowing them to directly act upon user intent with minimal human intervention.
In customer service, they go beyond traditional automation by handling dynamic, unpredictable interactions without constant human input. Unlike generative conversational AI, which responds to prompts under human direction, agentic AI can plan and execute strategies autonomously, adapt to new information, and navigate complex situations. This allows it to resolve routine issues, manage multi-step processes, and escalate only the most complex cases.
Customer service AI agents handle a wide range of goals, from common queries to specialized scenarios requiring deep domain knowledge, such as assisting with insurance claims, guiding mortgage refinancing, explaining consumer rights, or navigating regulatory compliance.
Modern AI agents, deployed as chatbots or virtual assistants, can carry out sophisticated conversations, integrate with backend systems, and continuously improve. They resolve most routine yet specialized queries independently while escalating only the truly complex cases. They also provide insights through Customer Interaction Analytics, helping businesses understand client behavior, uncover trends, and refine services. Some AI agents even assist human agents in real time.
For example, consider a user scenario related to travel plans. In a traditional chatbot interaction, a user might request flight information, and the chatbot responds with basic details. With an AI chatbot, the user could ask the question in a freer, more colloquial way, like “Tell me about flights for my next trip.” The chatbot, interpreting the question correctly, provides relevant flight information. But a virtual AI agent would go even further.
Now, with a AI Agent, the user can ask, “Can you help me plan my next trip?” The virtual agent would not only offer detailed flight options but also proactively suggest accommodations, transportation, and even local attractions, showcasing its ability to go beyond simple responses and actively assist users in planning a comprehensive travel itinerary.
Overall, call center AI agents transform customer service in knowledge-intensive sectors by streamlining operations, delivering expert-level support, optimizing strategies, and maintaining a competitive edge in specialized markets.
Hopefully, by now you understand the difference between a traditional chatbot, an AI chatbot, and an AI Agent. However, chatbots can be categorised even beyond their use of AI or lack of it; as we have said earlier, there are many different types of chatbots depending on their purpose and functioning. In the next section, we’ll go through some particular types of chatbots and explain what sets each of them apart.
Summary
Not all chatbots use Artificial Intelligence—while traditional chatbots rely on rule-based systems and keyword recognition to handle specific tasks, AI chatbots integrate technologies like Machine Learning, Deep Learning, and Natural Language Understanding (NLU) to interpret user intent, adapt to diverse contexts, and improve over time. Conversational AI encompasses these advanced systems, enabling dynamic, human-like interactions, while virtual agents take this further by combining AI with robotic process automation (RPA) to act on user requests directly, proactively performing tasks rather than just providing information.
Chatbot Types: How They Store, Understand, and Respond
Stateless vs Stateful Chatbots
Depending on the way they manage information and context throughout user interactions, chatbots can be either stateless or stateful. But what does that mean?
Stateless chatbots treat each user inquiry as an independent, isolated event. These chatbots do not retain any information about the user or the context of previous conversations, so every response is generated from scratch based solely on the immediate input. Stateless chatbots are suitable for handling simple queries where maintaining context across interactions is not crucial.
Stateful chatbots do retain information about the user and the context of the conversation across interactions. These chatbots have the capability to remember past interactions, creating a conversational state that enables them to reference previous user inputs. This ability to maintain context allows stateful chatbots to offer a more personalised and context-aware conversational experience. They excel in handling more complex and continuous conversations, providing a seamless and engaging user experience.
Scripted or quick reply chatbots
They’re the simplest type of chatbot. Scripted chatbots work as a hierarchical decision tree, engaging users through predetermined questions, guiding the interaction until the chatbot arrives at an answer to the user’s query. They’re similar menu-based chatbots, where users are prompted to choose from a predefined list or menu.
Keyword recognition-based chatbots
Chatbots relying on keyword recognition attempt to understand user input and formulate responses based on identified keywords within customer responses. This type of bot blends customizable keywords with AI to generate appropriate replies. However, if their AI component isn’t advanced enough, these chatbots encounter challenges when faced with repetitive keyword usage or redundant questions.
Contextual Chatbots
More intricate than their counterparts, contextual chatbots demand a data-centric approach. These bots leverage artificial intelligence (AI) and machine learning (ML) to retain and recall user conversations and interactions, utilising this stored information to evolve and enhance their performance over time. In contrast to relying on specific keywords, these bots analyse the nuances of customer inquiries and their phrasing to deliver responses and autonomously enhance their capabilities.
AI Voice enabled chatbots
AI voice chatbots are emerging as a transformative force in customer experience. According to recent research by Zendesk, 42% of customer experience leaders anticipate that generative AI will significantly influence voice-based interactions within the next two years. Beyond traditional phone support, voice AI can enhance engagement across multiple channels, including messaging apps and digital platforms. In fact, 74% of consumers report that the ability to interact with AI through natural voice conversations would greatly improve their overall experience.
AI Voice-enabled chatbots are advanced conversational systems designed to understand and respond to spoken language. Unlike traditional text-based chatbots, these systems rely on automatic speech recognition (ASR) to accurately transcribe spoken words into text, enabling seamless voice communication.
Once the speech is transcribed, natural language processing (NLP) and machine learning algorithms interpret the user’s intent, allowing the chatbot to understand complex queries and generate contextually relevant responses. This hands-free, intuitive mode of interaction enhances usability and convenience, making voice AI particularly valuable for virtual assistants, customer service hotlines, and voice-activated systems. By allowing users to engage naturally through speech, these chatbots are redefining the way people interact with technology.
Hopefully, by now you have a more complete understanding of the diverse range of chatbots available and of the differences between them. At this point, you’re probably wondering something: what do businesses use chatbots for? Are some types of chatbots better suited for certain businesses and processes than others? We’ll explore some chatbot use cases in the next section.
What Are Chatbots Used For? Business Use Cases and Real-World Impact
Customer Service
Customer Service Automation is rapidly transforming how businesses engage with customers, with chatbots at the center. Integrated into customer service software, chatbots provide instant support, answer FAQs, and guide users through troubleshooting. Organizations are increasingly investing in Conversational AI to streamline operations, improve communication, and enhance customer engagement. Today, as revealed by a Salesforce report, 69% of service professionals report using at least one form of AI, with 39% leveraging agentic AI and 53% using Generative AI. The same report estimates that AI resolved 30% of customer cases in 2025, projected to rise to 50% by 2027.
Conversational AI can emulate human language and respond accurately to queries, enabling businesses to handle more interactions efficiently while maintaining high satisfaction. Research shows that in 2025, 42% of customers said Conversational AI handles complex inquiries as effectively as humans, up from 28% in 2024—a 50% increase. Other research also shows that also show that 67% of consumers now ask AI more diverse and varied questions.
The past five years have seen remarkable advances in conversational AI. Modern systems understand intent, recognize sentiment, and reason through multi-step problems. They integrate with CRM and backend platforms, personalize interactions using historical data, and continuously learn to improve accuracy. These improvements allow AI to resolve queries autonomously, address nuanced customer needs, and deliver richer, more sophisticated experiences than ever before.
Customer attitudes have shifted as well. In 2022, 60% preferred waiting in a queue over chatting with a bot. Today, 67% of consumers are willing to delegate tasks to AI assistants, using them in increasingly varied ways. Today, 60% of regular AI users feel these systems have become more helpful over the past year, and 70% perceive a growing divide between organizations that leverage AI effectively and those that do not.
Companies using Call Centre Software with Conversational AI features also report higher query resolution, greater efficiency, and increased customer satisfaction and revenue. Generative AI’s impact on customer service revenue surged in 2024: McKinsey found that organizations reporting over 10% revenue growth linked to AI jumped from 3% in H1 to 18% in H2. Meanwhile, 14% saw 6–10% revenue growth and 31% up to 5% revenue growth, showing AI’s rapidly measurable effect on business performance.
AI agents capable of independent reasoning are becoming more widespread, further expanding AI’s role in customer service. Salesforce reports that AI agent deployment surged 119% in the first half of 2025, with sales and customer service as the leading applications. Gartner estimates that by 2029, Agentic AI will autonomously resolve 80% of routine customer service issues.
A prime example is ConnexAI’s AI Agent. Its advanced architecture allows it to handle interactions with deep domain expertise and nuanced understanding. By learning from past conversations and the organization’s knowledge base, it delivers increasingly accurate, comprehensive, and personalized responses—providing detailed information about products, services, market, operations, and customer needs, far surpassing traditional chatbots.
Lead Generation
Businesses also employ chatbots to assess leads, collect information from prospective customers, and kickstart the sales process through the provision of pertinent details about products or services. Studies show that 61% of salespeople believe generative AI will help them better serve their customers and sell efficiently. 71% of customer-facing sales agents to automate personalised sales communications, and 74% use it to analyse market data.
Through intelligent and interactive conversations, chatbots engage with website visitors or users on various contact centre software platforms, guiding them through a series of questions to understand their needs and preferences. By collecting relevant information and assessing user responses, chatbots efficiently qualify leads based on predefined criteria. This enables businesses to prioritise and allocate resources effectively, focusing on leads with the highest probability of conversion.
Furthermore, chatbots contribute to a seamless and personalised experience, offering real-time assistance and information tailored to the individual user’s interests. The efficiency and automation that chatbots bring to lead generation processes significantly enhance a business’s ability to build a robust sales pipeline and nurture meaningful relationships with potential customers.
Appointment Scheduling
Chatbots have become indispensable tools for streamlining appointment scheduling processes in various industries. These intelligent conversational agents assist users in efficiently booking appointments, making reservations, or setting up meetings without the need for human intervention.
By engaging users in natural language conversations, chatbots can understand scheduling preferences, check calendar availability, and provide real-time options, ensuring a seamless and user-friendly experience. This automation not only saves time for both businesses and clients but also reduces the likelihood of scheduling conflicts.
With the convenience of 24/7 availability, chatbots for appointment scheduling contribute to enhanced customer satisfaction and operational efficiency, allowing businesses to manage appointments with greater ease and accessibility.
Feedback Collection
Chatbots can also play a pivotal role in gauging customer satisfaction and collecting valuable feedback. By proactively engaging with users, chatbots can swiftly and unobtrusively gather opinions and insights about products or services. Through tailored questions and interactive dialogues, chatbots efficiently measure customer satisfaction levels, allowing businesses to identify areas for improvement and address concerns promptly.
What’s more, this feedback collection process can be automated even further. For example, Flow, Connex’s customer service automation tool, enables to schedule automated chatbot prompts after a sale or the close of a ticket to gather customer satisfaction. This real-time feedback mechanism allows businesses to implement data-driven strategies to enhance overall customer satisfaction, creating a feedback loop that contributes to continuous improvement and positive customer relations.
Also, implementing additional features like AI Sentiment Analysis can allow businesses to review how customers feel during conversations with chatbots, providing an accurate representation of the actual levels of customer satisfaction as well as actionable insights to inform future strategies.
Key Benefits of Chatbots for Customer Engagement and Business Growth
Improve customer engagement and brand loyalty
Before chatbots, human responses were the only available solution to customer questions or issues, irrespective of their scale. Handling these matters, even during off-hours, weekends, or holidays, incurred significant challenges and costs for customer service departments.
Chatbots have changed this: enabling the continuous management of customer interactions 24/7. Not only do chatbots ensure timely responses, but they also enhance response quality and efficiency while reducing costs.
By automating Customer Experience processes, chatbots alleviate employees from repetitive tasks and eliminate extended wait times across various support channels. This streamlined and responsive approach results in a superior user experience, fostering customer satisfaction and loyalty.
Boost operational efficiency
Maintaining a customer support centre around the clock is expensive, and outsourcing comes with its own costs and a potential loss of control over brand interactions. However, chatbots offer a cost-effective solution, capable of answering queries 24/7. They serve as an efficient first line of support, assisting during peak periods, and handling repetitive questions.
This enables human agents to concentrate their time and skills on more complex queries and high-value interactions, ultimately reducing the need for human intervention and allowing businesses to efficiently scale up staff to meet increased demand or off-hours requests.
Better lead generation and conversion
Chatbots can also play a role in enhancing sales lead generation and increasing conversion rates. For example, imagine a scenario where a customer is exploring a website for a product or service, seeking information about various features, attributes, or plans. In this context, a chatbot can deliver immediate answers, guiding the customer through the decision-making process and facilitating the path to purchase.
In cases involving more intricate purchases with a multistep sales funnel, the chatbot can further contribute by posing lead qualification questions and, if necessary, seamlessly connecting the customer directly with a knowledgeable sales agent.
Scalability
Chatbots possess a remarkable ability to efficiently handle a multitude of interactions simultaneously, showcasing their high scalability. This means that they can engage with and respond to numerous users or inquiries concurrently without compromising performance.
This scalability is particularly advantageous for managing large volumes of inquiries or transactions, allowing businesses to provide prompt and responsive interactions to a significant number of users.
Data Collection and Analysis
Chatbots can also help to collect valuable data regarding user preferences and behaviours. This information empowers businesses with the ability to delve into trends, make informed, data-driven decisions, and refine their offerings accordingly.
By analysing the data gleaned from chatbot interactions, businesses gain insights into customer preferences, pain points, and behavioural patterns. This wealth of information becomes a strategic asset, guiding businesses in tailoring their products, services, and overall customer experience to better align with the evolving needs and expectations of their user base.
For example, AI Customer Interaction Analytics features like Sentiment Analysis, Keyphrase Recognition or Entity Recognition, integrated with Connex’s AI Agent, provide insights into what customers feel like at every moment of the conversation, as well as what they’re mentioning most often. This information can be incredibly valuable to steer future Customer Experience decisions.
Omnichannel presence
An Omnichannel software solution like ConnexAI offers businesses the advantage of seamlessly integrating chatbots across a range of communication channels. This integration spans websites, messaging applications, and social media platforms. The key benefit lies in providing users with a consistent and unified experience, regardless of the channel they choose for interaction.
This unified Omnichannel approach enhances customer satisfaction by eliminating the need for users to adapt to different interfaces or experiences across various channels
