Conversational AI :
The Ultimate Guide

How does Conversational AI work?

Conversational AI combines natural language processing (NLP), Machine Learning (ML), natural language understanding (NLU) and other language and speech recognition technologies to interpret, process and contextualize written or spoken words and respond to each input accordingly. 

Conversational AI uses algorithms and workflows the moment an interaction commences when a human makes a request. AI parses the meaning of the words by using NLP, and the Conversational AI platform further processes the words by using NLU to understand the intent of the customer’s question or request. 

Conversational AI also then uses Machine Learning to ensure that responses to customer requests improve over time by learning with each human interaction. The use of data is an asset, as the best Conversational Platforms can also leverage the content and data gathered from each interaction to better understand what people want when they communicate with the platform. 

With this, proficient Conversational AI works by delivering contextualized, personalized and relevant interactions between humans and computers.

Technology that powers Conversational AI

Deterministic Approach vs Mathematical Approach to AI

Artificial Intelligence requires a lot of focus on the nature of algorithms of data. However, Symbolic AI and Machine Learning are also key approaches upon which Artificial Intelligence is founded on. These approaches are also described as deterministic and mathematical, they differ in the outcomes they expect and in their processes. 

Symbolic AI was the dominant form of AI research in its first decades up until the late 1980s, while Machine Learning has gradually become the prevalent approach to the discipline. But what is the difference between the two and how do they differ when applied to Natural Language Processing?

What is Symbolic AI?

Symbolic Artificial Intelligence, also known as Good Old-Fashioned AI (GOFAI), uses human-readable symbols and representations of problems, entities, concepts or logic in order to create ‘rules’ for the manipulation of these symbols, leading to a rule-based system. 

Basing itself on the assumption that many aspects of intelligence can be achieved via the manipulation of symbols, symbolic AI involved the explicit embedding of human knowledge and behavior rules into computer programs.

The symbolic approach applied to NLP

One of the many uses of symbolic AI is linked to Natural Language Processing for conversational chatbots. This approach is also known as the “deterministic approach”, and it is based on the need to teach machines to understand languages,  in the same way that humans learn how to read and write.

Just as humans have had to go to school to learn how to structure language by abiding by rules, grammar, conjugation and vocabulary, computational linguistics do the same. In this case, they use rules, lexicon and semantics to teach the bot’s engine how to understand a language.

How and when is Symbolic AI efficient?

Symbolic AI and the deterministic approach rely on these concepts: humans think using symbols; computers operate using symbols; computers can be trained to think. 

We use symbols to define even the simplest things and to describe what we don’t. For this reason, Symbolic AI can be successful when solving problems that need to satisfy certain constraints or conditions, and that are quite specific and not random. 

When conversational aspects of NLP are rule-based and follow logical inferences, Symbolic AI works as it makes sense of inputs and generates conclusions based on rules and evidence. 

With symbolic AI, everything is visible, understandable, and explainable, leading to what is called a “transparent box” as opposed to the “black box” created by machine learning. 

As a result of this, bot developers have a completely different approach when using Symbolic AI technology than with Machine-Learning based technology, as they need to focus on writing new content for the knowledge base rather than small segments of existing content. Developers also have full transparency on how to fine-tune the engine when it doesn’t work properly as they can understand why a specific decision has been made and have all the tools available to make amendments. 

What is machine learning?

Machine learning is an application of artificial intelligence that focuses on the use of data, algorithms and statistical models by computer systems to perform specific tasks without the need to use explicit instructions to perform a task. The model imitates the way that humans learn to gradually improve its accuracy. Instead of using instructions, machine learning algorithms build mathematical models based on sample data, known as “training data,” to make predictions or decisions.

The algorithms in machine learning technology teach computers to solve problems and gain insights from these processes. That way, computers earn automatically, without human intervention or assistance.  Machines look for patterns in data and use feedback loops to monitor and improve predictions. Computers are not overwhelmed by mass amounts of data, but actually improve by using data to keep learning and make better decisions in the future. 

Deep Learning, Neural Networks and Machine Learning

Often, when talking about Artificial Intelligence, Machine Learning, the term is also used interchangeably with Neural Networks and Machine Learning. However, they have their differences. 

While linked to one another, each one is a component or subset of another. Machine learning is a subfield of Artificial Intelligence. Deep learning is a subfield of machine learning, and neural is a subfield that constitutes the backbone of deep learning. 

The neural networks that are a subfield of deep learning mimic the human brain through a series of algorithms. They are designed to recognize patterns and interpret data through machine perception, where they label or cluster inputs as numerical vectors. 

What is the difference between Neural Networks and Deep Learning?

Deep learning refers to the depth of layers in a neural network. When a neural network consists of more than three layers, this can be considered a deep learning algorithm. These neural networks tend to flow in one direction but can be trained to backpropagate and analyze errors in order to ensure that they can adjust and fit correctly in the algorithm.

What is the difference between Deep Learning and Machine Learning?

Deep learning is a subset of machine learning. A key element that differentiates the two is how each algorithm learns and how much data is used in each process. Deep learning requires less human intervention as it is heavily automated. 

Machine learning depends more on human intervention to learn, as the latter establishes the hierarchy of features to categorize data inputs and ultimately require more structured data than in the case of deep learning. 

Machine learning applied to NLP

Machine learning can be applied to many disciplines, and Natural Language Processing is one of them, as are AI-powered conversational chatbots. 

When applied to NLP and chatbots, machine learning works ensuring that the conversational bot is fed as much relevant data as possible by somebody who oversees the chatbot. The bot then gets asked questions by users and decides which answer to push for in each specific intent it is queried for. Botmasters, the people who oversee the bot, then need to review these responses and manually tell the engine which answers are correct, and which answers are incorrect. This way, the machine learns how to deliver the correct answer to an intent. 

This can be quite time-consuming, as there are many ways of asking or formulating a question. Also, if you bear in mind that knowledge bases tend to hold an average of 300 intents, using machine learning to maintain a knowledge base can be a repetitive task. 

This doesn’t mean that machine learning is not an efficient tool. It has proven to be just that when carrying out tasks such as image and voice recognition, but it can have its limits when it comes to NLP.

When is machine learning an efficient approach? 

Machine learning is an excellent tool that has its drawbacks. While symbolic AI makes things more visible and is more transparent, one of the main differences between machine learning and traditional symbolic reasoning is how the learning happens. In machine learning, the algorithm learns rules as it establishes correlations between inputs and outputs. In symbolic reasoning, the rules are created through human intervention and then hard-coded into a static program. 

Machine learning can be used for projects that require predicting outputs or uncovering trends. The use of data can help machines learn patterns that they can later use to make decisions on new data inputs. However, its lack of transparency and large amounts of required data means that it can be quite inconvenient to use. 

Companies now realize how important it is to have transparent AI, not only for ethical reasons but also for operational ones, and the deterministic approach is gaining credence.

How can Conversational AI be implemented?

Conversational AI uses Natural Language Processing and AI algorithms to engage in contextual dialogue by processing and contextualizing the written or spoken word in order to figure out the best way to handle and respond to user input. This process usually happens in three steps, as follows.

Step #1: Understand what the user’s intent is

First, the application receives information input from the user, which can be either written text or spoken phrases. The AI then uses Natural Language Understanding (NLU) in order to understand the meaning of a question regardless of grammatical mistakes, spelling mistakes, jargon or slang. This capability is very different from recognizing a keyword or phrase and answering with a canned response that was scripted for that specific keyword.

Step #2: Select the correct answer

Based on its understanding of the user’s intent, the AI then must determine the appropriate answer in its knowledge base.

Step #3: Deliver the answer

Finally, the AI uses Natural Language Generation (NLG), the other part of NLP, to generate the appropriate response in a format that is easily understood by the user. Depending on which channel is used, the answer can be delivered by text or through voice, using speech synthesis or text to speech.

Additionally, deciding the conversational AI design is an important process. The interactions in the conversational AI platform must be aligned with the company’s business model, goals and customer personas.

How a Conversational AI solution is implemented and how customers can access or interact with a brand can vary as there isn’t one single approach. Here we will look at some of the ways Conversational AI can deliver solutions to customers.

Chatbots

Chatbots, aka “conversational agents” or “virtual assistants”, are increasingly becoming key players in many company’s digital transformation strategies. Businesses from all sectors including e-commerce, banking, insurance, industry, and telecommunications are adding chatbots to their future plans as they seek to automate processes and handle growing requests for customer service while saving costs. A study by Juniper has highlighted that chatbots are projected to drive cost savings in banking and healthcare of over $8 billion per year by 2022. 

There are different types of chatbots, such as button-based, keywords based or conversational bots. Basic chatbots might be limited to answering standard questions, but intelligent chatbots allow humans to interact contextually at any time of the day with technology using various inputs from text, voice, gesture and touch. 

A chatbot project’s success is not based solely on the interface. It depends above all on the ability to combine your expertise and the provider’s feedback with a natural language solution and an adequate knowledge base. That way, when implemented correctly, chatbots can deliver noteworthy results that can transform your customer service. 

Customers want and expect immediate access to information to help them solve problems or make an end-to-end transaction. When these expectations are not met, customer satisfaction rates, and therefore brand loyalty, can dwindle. 

This is why it is so valuable to have an AI chatbot that understands what a customer is trying to tell it, and quickly provides the relevant information. By using AI chatbots that understand context, customers feel heard and understood, while human agents also have increased productivity as they can focus on solving more complex issues or on improving their skills. 

Despite some trends that inaccurately tend to vilify chatbots, the myth that chatbots are not intelligent enough has been debunked. In fact, there is no longer an issue of whether a chatbot should be used or not, but rather which conversational AI platform best suits your business, your clients and your customers

Advanced conversational AI bots like the Inbenta AI chatbot can help businesses supercharge their customer interactions while automatically engaging in complex conversations with minimal training. 

Unlike keyword-based chatbots, the Inbenta Chatbot uses Symbolic AI to power its Natural Language Processing technology, enabling it to understand human languages in all their variations. It can detect the meaning of words without requiring the lengthy data training usually associated with machine learning algorithms. This results in an intelligent chatbot that can be swiftly deployed in a matter of days and that delivers high response rates and can increase case resolutions from 30 to 50%.

Businesses need to choose chatbot platforms that are easy to build, deploy and maintain, while delivering personalized, seamless, omnichannel capabilities. Additionally, chatbots can do more than interpret a user’s request, detect sentiments and recall past requests and conversations to deliver tailored responses, by providing actionable insights with collected data to further increase personalization and to automatically suggest improvements or upselling opportunities to further drive revenues.

Enhanced FAQ and Search

With users expecting companies to include self-service applications, many companies are looking to optimize their FAQs and search pages to guide prospects towards making purchases or resolve their problems and maintain brand loyalty.

Conventional FAQs have been little more than a sequence of answers to typical problems that can be accessed on a static web page. Customers have usually had to figure out how to navigate to the specific question they are looking for and to be meticulous with the phrases and keywords they use.

Businesses need to improve their FAQs and deliver information to visitors on their terms, without frustrating them by having them search through the webpage. Chatbots and automated communication tools that process natural language leverage existing information in an FAQ with NLP to cross-reference the meaning of a query with the data already stored in the company knowledge base.

The result is an interactive experience that goes beyond the binary features of a typical FAQ and that resembles asking a live human agent for help finding a specific point, even if the keywords that are typed are not exact.

The answers provided are also different from conventional FAQs in that they are not long, general, and imprecise. The use of advanced chatbots can deliver personalized responses and offer links to other related content and topics to ensure that the customer is fully satisfied with the query being made. This increases self-service rates, boosts customer experience, and reduces inbound customer support tickets.

Importantly, these new platforms allow you to take advantage of advanced NLP technologies to optimize your FAQs into a proficient chatbot experience can be delivered in weeks instead of months.

Knowledge Management

With customers wanting to access information seamlessly and easily, dynamic and intelligent knowledge management systems can save valuable time and reduce bounce rates and customer frustration when they are looking for specific information or products. 

A knowledge base is a database containing all the information the user can be asking for. In particular, it gathers the questions/answers and media that are offered as answered to the end-users.

Knowledge management systems help users find, manage and create knowledge bases by organizing frequently asked questions, product details and more, and making it easy to access. With this, customers can benefit from self-service and staff can receive better support by accessing updated, accurate and homogenous information. Additionally, knowledge content can be indexed, which actually helps google ranking because of its long-tail SEO functionality. 

By using a Symbolic AI, a.k.a. meaning-based search engine, knowledge management systems like Inbenta’s can interpret human language in order to swiftly answer user queries and boost customer satisfaction. 

By improving customer experience with Knowledge Management systems, businesses can reduce costs and better understand consumer habits and preferences. Users learn to self-serve and find solutions as they navigate through product pages or conversion funnels, and proficient Knowledge Management centers like Inbenta Knowledge act as a deflection tool that drastically reduces the number of incoming support queries. 

Importantly, it is easy to monitor the performance of these knowledge management systems at any time in the back-office via dashboards that provide real-time views. These insights and usage reports can be leveraged to optimize existing knowledge bases by identifying potential gaps in content and discovering areas of improvement. 

Managing content in Inbenta Knowledge is simple too. Its powerful and easy-to-use Workspace lets you add, edit and organize content using a tool that integrates with a large catalog of applications such as the AI-powered semantic search engine, and other integrations to ensure that the help site seamlessly blends with the rest of the website.  

By combining knowledge across multiple systems, Knowledge Management systems help people access information regardless of where it resides.  

Inbenta Knowledge is also easy to monitor in the back-office through a dashboard that can detect potential gaps in content and discover areas of improvement. These can be easily edited in a Workspace that includes integrations like Inbenta’s AI-powered semantic search engine, help-site manager and an SEO optimizer to make it easier to organize.

Overall, there are numerous benefits to Knowledge Management systems. They can help people within an organization share, access and update important company information, while also helping boost creativity and decision-making processes and minimizing risks.

Voicebots and IVRs

As user demands for optimal customer service are growing, consumers expect immediate replies, avoiding waiting times on the phone and autonomy, preferring self-service ahead of phone conversations. However, this does not mean that they avoid using their phones or defer from using voice applications while looking for answers. 

The increasing use of voice-activated devices further highlights how consumers are becoming used to making requests using their voice and without having to type their questions. 

The adoption of voicebots is increasingly popular among younger generations. 51% of consumers aged 14-17 have said that they have already interacted with some sort of voice or speed recognition device. Coincidently, these younger generations are also raising the bar when it comes to the standards and expectations towards customer service. The more digitally savvy they are, the likelier they are to prefer new ways to communicate with brands and avoid manual typing. 

In a similar way to chatbots, voicebots and IVRs can receive, analyze, interpret and respond to customer inquiries spoken to them in natural language, with the objective of answering a customer request in real-time or maintain a verbal interaction with the aim of clarifying the customer’s question and pass the inquiry to a human agent if necessary. With this, users experience a swifter customer experience through conversation, streamlining the customer journey and alleviating the number of contacts of a customer support team.  

Voice can deliver substantial benefits to a business’ customer services, many of these like chatbots. For example, voicebots can answer to standards regardless of how many people are contacting a call center. The fact that they are scalable means that there is never a need for waiting lines because of a lack of staff, and voicebots can carry end-to-end transactions and support, contributing to the automation of business operations and increasing efficiency while reducing time. 

There are differences between voicebots and chatbots, however. Firstly, text-based channels are generally easier to implement, and it is easier for bots to understand what a customer wants and parse through data to find a solution. Voicebots specifically require added speech recognition capabilities to understand and discern the intent of customer requests in order to reply accurately. While doing so, voicebots still need to access customer information like chatbots do to build a customer profile and deliver personalized responses. 

Voicebots achieve this by synthesizing voice requests, including interjections like “Okay” and “Umm”, and converting this information into text for further processing and then coming up with a reply in a matter of seconds.

Proactive Chatbots

We have already explored the importance of chatbots when it comes to delivering customer experience. Most chatbots successfully fulfil the role of assisting users when they need more information and contact the chatbot for information. 

These chatbots are reactive, because they are automated chat instances that wait for the customer or visitor to reach out before communicating with them. 

This means they won’t send any messages unless the user asks a question. 

Proactive chatbots are another approach to using chatbots and conversational AI. Instead of waiting for a user to engage with them, proactive chatbots initiate conversations to encourage the user to carry out a certain action based on a series of behaviors. 

These behaviors can come in different forms, for example:

Pages Visited: depending on the pages a user visits, chatbots can recommend certain content or suggest different actions that lead to conversions. 

Time on page: Chatbot interactions can be triggered depending on how long a user has been browsing on a specific page. 

Scrolling: Chatbots can be activated when a user reaches a certain point of their scroll down, in a less invasive way to a pop-up, to recommend related or similar content, offer discounts or facilitate processes. 

Shopping cart: Proactive chatbots can also use other metrics, such as the number of items in a shopping basket, or the total value that order would have in case it is converted. This means that a chatbot can offer discounts only to customers considering a purchase over a certain amount of money.

Proactive chatbots are assets because they can provide substantial benefits to businesses. A study by Microsoft showed that 70% of customers tend to have a better image of brands that offer proactive notifications. Along with strengthening a brand’s image, proactive chatbots excel in anticipating customer needs, and using data and behavioral insights to assist users at the right time. Almost 90% of successful businesses are sure that anticipating their customer needs and assisting them along their journey is essential to foster business growth. 

By engaging proactively with customers, there is less risk of shoppers abandoning their purchase, and can substantially improve customer satisfaction rates and brand loyalty. 

Of course, care and thought must be given to know when and how to proactively engage with a customer to not seem pushy, and to use the proactive chatbot at specific points when visitors may be leaving to ensure that their customer journey continues. 

Used wisely, with efficient copy and a chatbot that is visually appealing and dynamic, proactive chatbots can be a game-changer on any brand’s website.

Mailbots

Used more as a tool to assist human agents, mailbots can support customer service teams by automatically replying to contact center’s incoming emails by filtering, redirecting or redistributing incoming emails to the right person by parsing through the content. Once the main issue of concern has been detected, pre-built templates serving different objectives such as status updates, product recommendations or informative replies can be sent, saving time usually spent drafting an email or newsletter. 

How to apply conversational AI to a self-service strategy?

Customers are increasingly turning to self-service to avoid waiting lines and to find solutions to their requests on their own. A Zendesk study shows that 81% of customers try to resolve problems on their own before reaching out to support channels. Businesses are aware of this and are looking to fine-tune their customer self-service solutions to enable web users to access information or perform simple tasks autonomously, without needing assistance from a customer service representative. 
Customers may want to use self-service for numerous tasks, such as tracking a package, requesting a quote, or paying a bill online without having to talk to a human agent at the company to carry out these actions.

The benefits of automated self-service strategies

While there are still queries that cannot be handled by self-service due to their complexity, self-service solutions are very efficient at solving tier-1 repetitive queries. These types of requests represent approximately 80% of the questions received by customer service agents, and take up a large amount of time, so automating these results are an effective way of saving time and resources and allowing human agents to focus on the more complex tasks. 

When customer service departments are overburdened with numerous online requests, as was witnessed during the first months of the Covid-19 pandemic, the implementation of one or more self-service solutions becomes imperative. Additionally, self-service also caters to new customer demands for greater autonomy and faster service delivery. 

The stakes are high if companies fail to meet these demands, as word-of-mouth or bad customer reviews can have a damaging impact on business and drive away potential prospects, to the extent that 76% of clients have stopped doing business with an organization due to its poor client service, and 39% have immediately abandoned their purchase to switch to another supplier when this occurs. 

Businesses therefore must look for the best forms of ensuring self-service to their clients. These can be chatbots, dynamic FAQs, semantic search engines, customer knowledge bases and more. The solutions they choose to implement must be tied to their needs and be able to cater to customer demands for 24/7, seamless omnichannel services. 

By leveraging the features of Natural Language Processing technology, these solutions can understand the true intentions behind customer’s questions and instantly retrieve the right answer from a knowledge base. Whether the solution is found in a knowledge base or through a chatbot, these self-service solutions will provide the desired autonomy customers want, answer their queries, and reduce the number of incoming contacts to your customer service. 

Automating customer services will also help reduce queues in contact centers and allow human agents to concentrate on more complex queries or dedicate more time to winning back dissatisfied customers. 

Customers are quick to voice their discontent when their needs are not met, so it is important to have effective dissatisfaction management tools. Human agents can make the most of automated self-service tools to spend time identifying signs of frustration, often with the help of auxiliary dissatisfaction detection tools such as those provided by Conversational AI platforms such as Inbenta’s. These tools can proactively trigger a case escalation to an agent, guaranteeing a direct treatment to a frustrated customer. 

Internal customer service teams can also benefit from self-service as they can use intelligent FAQs, knowledge bases and conversational chatbots to assist them in finding the answers to customer requests.  Human agents can have access to predefined responses or to an entire dissatisfaction management procedure.

These solutions can help both customers and advisors at the same time, helping to seamlessly harmonize the customer service process and ensure that responses are consistent, accurate and updated. 

Conclusion of our Ultimate Guide

Businesses know that there is a growing need to automate their services and save time and resources. However, they must rely on solutions that can optimize these resources while providing faster, better support to boost customer engagement and brand loyalty. 

Conversational AI has become a key element in nearly every company’s digital transformation strategy and this has been further enhanced since the Covid-19 pandemic. Recognizing the need to implement conversational AI is a given, but choosing the ideal solution can still be a challenge. 

With the need to find quality packages with proven use-cases promptly, Inbenta has stood out as a provider that can guarantee guidance and a quality solution that can perfectly fit each company’s needs.

Whether it’s a chatbot, a knowledge base or advanced site-search, Inbenta delivers numerous solutions that can adapt to each business’ needs and transform their revenues and customer experience. It is not only customers who can benefit from Inbenta’s conversational AI solutions, but employees and HR teams too. 
Businesses and customers can be better informed on products and services and can access information 24/7, in multiple languages and through multiple channels, while also receiving personalized prompts and actionable insights.

With 15 years of experience and over 250 customers globally, Inbenta has built a solid reputation and can help you determine how you interact with your users. 
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