In today\u2019s digitally driven world, businesses can no longer rely on traditional service methods to satisfy their customers. Technological advances are paving the way for a new <\/span>customer experience<\/span><\/a>, and chatbots are at the helm.\u00a0<\/span><\/p>\n If you\u2019re curious about what <\/span>chatbots<\/span> are and how they work, you\u2019re in the right place.\u00a0<\/span><\/p>\n In this ultimate guide, we’ll cover:<\/p>\n A<\/span> chatbot<\/span> is a<\/span> computer program <\/span>that enables humans to interact with technology by simulating and processing a conversation on a basic level. Using a variety of written and spoken input methods, humans can interact with digital devices in the same way they would communicate with <\/span>human agents<\/span>.\u00a0<\/span><\/p>\n These tools are designed to automatically respond to questions and messages by drawing on information stored in a database or learning over time with <\/span>artificial intelligence<\/span><\/a> (AI) <\/span>algorithms<\/span>.\u00a0<\/span><\/p>\n The functionality and intelligence of <\/span>chatbot technology <\/span>will vary depending on the software’s complexity and the environment it\u2019s used in. A chatbot can be a rudimentary program that responds to simple questions with short responses, or a more complex <\/span>digital assistant<\/span> learning and evolves as it collects and processes information.\u00a0<\/span><\/p>\n Chatbots <\/span>have primarily been used in customer service to speed up support interactions; however, their uses are rapidly expanding. Nowadays, <\/span>chatbots <\/span>are used by small businesses and enterprises to provide enhanced customer experience<\/a> and create a more efficient work environment.\u00a0<\/span><\/p>\n They\u2019re so prevalent in the digital business environment that they\u2019ve come to be known by many names including<\/span> virtual assistants<\/span>, <\/span>digital assistants<\/span>, virtual agents, conversational agents and more.\u00a0<\/span><\/p>\n Some <\/span>chatbots<\/span> are more intelligent than others. While a basic bot might be used to answer standard questions or carry out transactions, advanced conversational <\/span>AI chatbots<\/span> can deliver sophisticated support and enhanced capabilities to allow enterprises to deliver a customer experience like no other.\u00a0<\/span><\/p>\n Chatbot technology<\/span> is often driven by AI, <\/span>natural language processing<\/span>, and <\/span>machine learning<\/span><\/a>. An ideal <\/span>chatbot <\/span>should respond to all types of requests and do so independently of a human agent. But how exactly does this work?\u00a0<\/span><\/p>\n On a basic level, a bot works by analysing text or voice data input, considering the most appropriate response, and delivering it back to the user via a chat interface. <\/span>Chatbot technology<\/span> can work in several major chat <\/span>messaging<\/span> products (<\/span>Facebook Messenger<\/span>, Slack, Telegram, <\/span>Whatsapp<\/span>, etc.) and an independent platform.\u00a0<\/span><\/p>\n There are two main types of<\/span> chatbot<\/span>: rules-based <\/span>chatbots <\/span>and <\/span>machine learning chatbots<\/span> (AI bots).<\/span><\/p>\n Rules-based chatbots function, as the name suggests, based on a set of rules. This is a less complex type of chatbot, as it\u2019s only designed to respond to specific commands drawing on answers stored in a database. Essentially, a rules-based bot is only as smart as the data set it can draw on.\u00a0<\/span><\/p>\n On the other hand, <\/span>AI chatbots<\/span> function using <\/span>machine learning<\/span> and are programmed to understand natural language, rather than just commands. This means they can understand more nuanced speech. They also grow \u2018smarter\u2019 as they converse with people, learning what questions may be asked and expected responses.\u00a0<\/span><\/p>\n In the next section, we\u2019ll look at how each type of bot functions in more detail.\u00a0<\/span><\/p>\n Chatbot technology<\/span> has become so advanced that understanding the various functions and capabilities of different bots can be challenging. As mentioned in the previous section, we can separate the two main types of <\/span>chatbots<\/span> into linguistic (rules-based) or <\/span>machine learning<\/span> (AI) categories.\u00a0<\/span><\/p>\n It\u2019s important to note that the chatbot space is constantly evolving and adapting to <\/span>customer experience trends<\/span><\/a> and to meet changing customer expectations and business needs.\u00a0<\/span><\/p>\n Rules-based bots are task-oriented, single-purpose programs focused on performing a specific function\u2014this type of <\/span>chatbot<\/span> functions by providing automated but conversational responses to user requests.\u00a0<\/span><\/p>\n According to <\/span>IBM<\/span><\/a>, chatbots can answer 80% of standard questions. They can provide more robust and interactive FAQ responses<\/span>,<\/span> such as questions about business hours or simple transactional queries. They\u2019re currently the most common type of chatbot. However, interactions with rules-based bots are more specific and structured than those with bots that use<\/span> machine learning<\/span>.\u00a0<\/span><\/p>\n This type of task-oriented bot is capable of handling common questions that don\u2019t involve many variables. Though they use <\/span>NLP<\/span> to facilitate conversational interactions, they offer limited capabilities and use if\/then logic to converse with customers and determine their next steps. For this reason, they can be leveraged by businesses to provide consistent customer service.\u00a0<\/span><\/p>\n Most of us have interacted with this type of bot in the past, on <\/span>social media<\/span> or a business\u2019s eCommerce website, whether we were aware of it or not. This kind of bot can be a valuable asset to a business looking to enhance customer support teams’ control and flexibility <\/span>and increase human agent productivity by handling common requests.\u00a0<\/span><\/p>\n Here\u2019s where things get more interesting.\u00a0<\/span><\/p>\n Chatbots that use AI and <\/span>machine learning<\/span> are sometimes referred to as virtual or<\/span> digital assistants<\/span>. They use sophisticated technology to provide more interactive, personalised, and data-driven experiences than rules-based bots.\u00a0<\/span><\/p>\n When a customer interacts with an <\/span>AI chatbot<\/span>, they\u2019ll have a more <\/span>conversational experience<\/span> than if they were to interact with a rules-based one. As the bot responds to data patterns and AI <\/span>algorithms, <\/span>they become more contextually aware as time goes on and leverage <\/span>natural language understanding<\/span> to replicate human behaviour.\u00a0<\/span><\/p>\n This bot can apply predictive intelligence and advanced analytics to personalise interactions based on user profiles and their past behaviour.\u00a0 They can even learn the user\u2019s preferences, initiate conversations, and anticipate the user\u2019s needs. Some examples of advanced AI bots that you might even have around your home include Amazon\u2019s <\/span>Alexa<\/span>, Google Assistant, and Apple\u2019s Siri.\u00a0<\/span><\/p>\n It\u2019s taken a lot of time and effort for <\/span>chatbots<\/span> to arrive at where they are now. Programming a machine to understand a human isn\u2019t an easy task. Human speech is nuanced, complicated, and extremely challenging to replicate artificially.\u00a0<\/span><\/p>\n Conversational bots use several key natural language principles to understand and replicate human speech and behaviour. Let\u2019s take a closer look at how this works.<\/span><\/p>\n NLP<\/span> refers to the process of breaking down sentence structure, analysing broken grammar, understanding idioms and other elements of speech, and recognising patterns to determine what the user is saying. In everyday speech, we often have various ways to say the same thing;<\/span> NLP<\/span> allows bots to understand this and respond appropriately.\u00a0<\/span><\/p>\n There are two main processes involved in<\/span> natural language processing<\/span>: understanding and generating.\u00a0<\/span><\/p>\n NLU allows the bot to understand user intent by using language features like lexicons, synonyms, and more complex themes. Used along with rules and <\/span>algorithms<\/span>, NLU identifies parts of speech to understand what the user is attempting to do.\u00a0\u00a0\u00a0<\/span><\/p>\n Once the bot knows what the user is trying to do, they need to translate their answer from machine code into human language. NLG allows bots to deliver personalised responses beyond scripted ones (the kind used by rules-based bots). With NLG, the <\/span>chatbot <\/span>can create a response based on autonomous deep learning.\u00a0<\/span><\/p>\n Source: <\/span>Bold360<\/span><\/a>\u00a0<\/span><\/p>\n Using NLU and NLG, AI chatbots can engage in advanced dialogues with users and deliver realistic, nuanced responses that mirror human speech.\u00a0<\/span><\/p>\n An AI chatbot can not only answer a customer\u2019s questions within a dialogue<\/span>\u00a0box; they\u2019re also capable of carrying out several different tasks.\u00a0<\/span><\/p>\n For instance, it could fill out a form, recommend a product or service, upsell, or even book an appointment for a customer. Moreover, AI chatbot applications can be integrated with third-party or backend software systems such as enterprise resource planning (ERP) or customer relationship management (<\/span>CRM<\/span>) to complete other complex tasks.<\/span><\/p>\n The goal of AI chatbots is to provide the most human-like interaction possible. Despite this, <\/span>40%<\/span><\/a> of people still don\u2019t care whether a chatbot or a real human helps them – as long as they\u2019re getting the help they need!<\/span><\/p>\n It\u2019s clear that the key to successful customer engagement is understanding the customer\u2019s request and catering to their needs with a personalised and accurate response, regardless of where it comes from.<\/span><\/p>\n A hybrid model provides the best of both worlds.\u00a0<\/span><\/p>\n With a hybrid approach, users can benefit from the features of rules-based chatbots and <\/span>machine learning<\/span>\n
Chatbot Fundamentals<\/strong><\/h2>\n
What is a chatbot?<\/strong><\/h3>\n
![\"The](\"\/gb\/en\/blog\/wp-content\/uploads\/2021\/01\/ultimate-guide-chatbots-important.png\")
<\/p>\n![\"AI](\"\/gb\/en\/blog\/wp-content\/uploads\/2021\/01\/ai-chatbot-smart-digital-customer-service-application-concept-1-1536x1024.jpg\")
<\/p>\nHow do chatbots work?<\/strong><\/h3>\n
Types of Chatbot Technology<\/strong><\/h2>\n
![\"The](\"\/gb\/en\/blog\/wp-content\/uploads\/2021\/01\/ultimate-guide-to-chatbts-2020.png\")
<\/p>\n1. Rule-Based Chatbots<\/strong><\/h3>\n
![\"The](\"\/gb\/en\/blog\/wp-content\/uploads\/2021\/01\/ultimate-guide-chatbots-11.png\")
<\/p>\n2. AI (Conversational) Chatbots\u00a0<\/strong><\/h3>\n
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Natural Language Processing (NLP)<\/strong><\/h4>\n<\/li>\n<\/ul>\n
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Natural language understanding (NLU)<\/strong><\/h4>\n<\/li>\n<\/ul>\n
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Natural language generation (NLG)<\/strong><\/h4>\n<\/li>\n<\/ul>\n
![\"The](\"\/gb\/en\/blog\/wp-content\/uploads\/2021\/01\/ultimate-guide-chatbots-8.png\")
<\/p>\nWhat does an AI chatbot do?<\/strong><\/h4>\n
3. Hybrid Chatbots<\/strong><\/h3>\n