ConnexAI named by Viva Tech: Top 10 Next Unicorns Read More
By Department
By Industry
About Us
Careers Hub
Locations
Featured Content
Our goal was to incorporate the Omni AI components of ConnexAI to assist us with efficiency goals.
Explore Athena AIIn 2024 Chatbots are omnipresent, finding applications in diverse industries—addressing customer queries, serving as virtual assistants in healthcare, and providing assistance on websites. Their versatility surpasses initial perceptions, making them as ubiquitous and adaptable as essential for business in today’s world.
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 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 specialists 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.
In a nutshell, a chatbot is a computer program designed to simulate and decipher human conversation, encompassing both written and spoken interactions. This technology facilitates seamless communication between individuals and digital devices, creating an interface that mimics human-like interaction.
Chatbots manifest in various forms, ranging from basic programs providing automated, quick responses to routine or simple queries, to highly sophisticated digital assistants. The latter boast an advanced capability to learn and evolve, offering an elevated degree of personalisation over time. As these digital assistants accumulate and process information, they can adapt their responses to individual preferences, creating a dynamic and tailored conversational experience for users.
The origin of chatbots is inextricably linked to one of the oldest Artificial Intelligence problems: the Turing test. As you might already know, the Turing test is a thought experiment suggested in 1950 by Alan Turing (considered today by most to be the “father of AI”) in his famous paper “Computing Machinery and Intelligence”. As you may also know, the test was suggested by Turing (and is still considered today) as a criterion to determine whether a machine can be considered intelligent.
To put it simply, the Turing test works like this: if and only if a machine is able to maintain a coherent conversation with a human agent in a way that is convincing enough for the human agent to be incapable of recognising that they are talking to a machine, then that machine can be considered intelligent.
As you can see, the question of whether it is possible to program a convincing chatbot is not only a concern for today’s AI researchers or business owners and managers in the customer service space: it’s one of the foundations of the history of Artificial Intelligence as a theme and as a discipline.
One of the first attempts to create a program capable of passing the Turing test was also one of the first chatbots in history: in 1966, MIT professor and computer scientist Joseph Weizenbaum created ELIZA, an early Natural Language Processing (NLP) program designed to emulate conversations between a psychotherapist (the program) and a client (the user).
ELIZA’s functioning was based on very simple algorithms that couldn’t be taken to constitute real intelligence. ELIZA worked by recognizing specific words or phrases in what the user said, and then responded with pre-programmed replies. For example, if the user mentioned ‘MOTHER,’ ELIZA would reply with ‘TELL ME MORE ABOUT YOUR FAMILY.’ However, Weizenbaum was surprised when people, even his own secretary, thought the chatbot had human-like feelings. Many believed it could help individuals, especially those with psychological issues, and assist doctors in treating such patients.
Still, despite ELIZA being arguably the first chatbot in history, it was evident for anyone with notions of computer science or psychology that conversations with ELIZA seemed to always fall back into certain predetermined, repetitive patterns; this made it easy to differentiate them from conversations with a human therapist. Therefore, it couldn’t be considered to have passed the Turing test.
ELIZA, being one of the first chatbots in history, was naturally a very simple one. Almost 60 years later, chatbot technology has evolved at an exponential rate. The most sophisticated chatbots today leverage Artificial Intelligence, which, as you know, has also experienced rapid advances over the last years. In 2024, the possibility of a chatbot able to pass the Turing test seems closer than ever before.
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:
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?
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:
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.
Virtual agents represent one step beyond when it comes to the implementation of AI chatbot software.
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.
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 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.
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.
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.
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.
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.
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.
Voice-enabled chatbots are advanced conversational interfaces designed to understand and respond to spoken language. Unlike traditional text-based chatbots, these systems leverage automatic speech recognition (ASR) technology to transcribe spoken words into text, allowing for seamless communication through voice interactions.
Voice-enabled chatbots use natural language processing (NLP) and machine learning algorithms to comprehend the user’s intent, enabling them to interpret complex spoken queries and generate contextually relevant responses. This technology enhances user experience by providing a hands-free and intuitive means of communication, making it particularly valuable in applications such as virtual assistants, customer service hotlines, and voice-activated systems, where users can engage with the bot simply by speaking naturally.
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.
Customer Service Automation is revealing itself as a major gamechanger in how businesses engage with customers, and chatbots are a part of this process. Chatbots are widely employed in call centre software or customer service software solutions for providing instant customer support, answering frequently asked questions, and guiding users through troubleshooting processes. Usually, chatbots in this realm are fairly simple, the majority of them being basic scripted chatbots. This might explain why 60% of customers prefer talking to a live agent than use a chatbot, afraid that a chatbot might not be able to accurately understand their queries.
However, the rapid advancements of AI are making it possible for many businesses to adopt sophisticated AI Agents, like Connex’s Athena. These chatbots, leveraging Deep Learning and Machine Learning are able to not only address routine questions from customers, but also learn from past interactions and anticipate future conversations, which allows them to address increasingly complex and unprecedented queries.
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.
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.
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.
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 experience 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.
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.
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.
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.
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.
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 Interaction Analytics features like Sentiment Analysis, Keyphrase Recognition or Entity Recognition, integrated with Connex’s chatbot Athena, 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.
An Omnichannel 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.
Whether a customer engages with a chatbot on a website, through a messaging app, or on a social media platform, the experience remains cohesive and uninterrupted. This unified Omnichannel approach enhances customer satisfaction by eliminating the need for users to adapt to different interfaces or experiences across various channels. It ensures that businesses can maintain a standardised and seamless communication strategy, reinforcing brand identity and user engagement.
NLP (Natural Language Processing)
Customer Experience Automation
Shaping the Future of Customer Experience.
