Callbots, NLP, and conversational AI – how to decode artificial intelligence

The ABCs of conversational AI. The arrival of artificial intelligence is far from being a passing trend, and it is proving to be a real revolution in customer relations.

13 July 2021
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The ABCs of conversational AI

The arrival of artificial intelligence is far from being a passing trend, and it is proving to be a real revolution in customer relations. It has created an opportunity for companies to considerably improve their service to users and consumers by establishing a responsive and seamless customer experience.

To help you better understand the jargon used, we will provide you with a clearer understanding of the most commonly used terms that are frequently asked about.

Artificial Intelligence geared towards an omnichannel journey

The phone is still the most popular communication channel and has been for many years, with customers and clients expecting an immediate response from companies and organizations. Unfortunately, busy lines and long waiting times can be particularly irritating, often leading to frustration and deep dissatisfaction.

By using the latest technology, solutions can be developed to get rid of advisors’ repetitive tasks and let them focus on high value-added tasks instead.

Artificial Intelligence or AI

This is a field of computer science specialized in creating machines capable of performing tasks such as learning or reasoning in the same way as the human brain. The aim is to have a machine imitate the cognitive abilities of a human being. In a technological sense, artificial intelligence is a completely different way of programming from that of traditional methods of development. Instead of starting from the rules and moving towards the data, we start from the data and move towards the rules. Artificial intelligence algorithms are able to conceptualize rules from data.

With AI, machines are taught to learn, to recognize their environment and to achieve their results.

It is no longer a question of coding the rules manually but of letting the computers figure them out by correlation and classification on the basis of a massive amounts of data. In other words, the aim of machine learning is not really to acquire knowledge that has already been formalized, but to understand the structure of data and to integrate it into models, in particular to automate tasks.

Artificial intelligence is based on Machine Learning and Deep Learning algorithms.

Machine Learning

In the past, rules were programmed to imitate human intelligence, with developers trying to teach computers the minute details of every decision they had to make.

For example, in order for a machine to be able to recognize a bird, recognition rules were constructed which stated that it was a two-legged animal with wings, feathers and that it flew. In the case of artificial intelligence and machine learning algorithms, the machine is provided with a huge number of bird images and told that each image represents a bird, which is what we call training the algorithm. The computer then has to analyze them to determine the common characteristics of these images, and thus acquire the ability to recognize a bird by itself by analyzing a new image.

Machine Learning is a set of statistical learning algorithms programmed to look for relationships between data.

Deep Learning

Deep Learning is a subset of Machine Learning. It is the result of a combination of the appropriation of knowledge of neural networks and the rise in the computational power of computers, which has become available at a lower cost. The machine still learns from data, but it is based on the functioning of the neurons and synapses of the human brain by using several layers of processing, each one progressively integrating increasingly complex data representations. For instance, deep learning is a revolution in visual recognition and natural language processing.

Artificial intelligence has made it possible to revolutionize the human-machine relationship by acquiring skills that were previously reserved for humans alone – examples include the understanding and use of natural language, translation, and speech recognition.

Hybrid or augmented advisor

This refers to the bot’s ability to handle part of the call and to switch it to a physical advisor if necessary. It can also give the advisor information to answer the call as the conversation progresses by using data analysis. This allows the advisor to be more efficient and to focus on the customer relationship.

Augmented supervisor

This is a technology based on artificial intelligence allowing real-time supervision of all conversations managed by the conversational agent. The augmented supervisor provides an automatic and qualitative measurement of conversations.

This value-generating solution allows supervisors to either identify high-risk conversations and take over or transfer them, or to build up on best practices.

Conversational Artificial Intelligence

Artificial intelligence now makes it possible to set up a speech recognition system as well as words associated with conversational bots to improve the customer experience.

Whether with an advisor or with a bot, users want to talk to their customer service as they do in their daily lives, i.e. in “natural” language and not in machine-like or robotic language.

In order to ensure a smooth dialogue, the technologies use a natural language processing system. Let’s learn more about how it works.


NLP or “Natural Language Processing” is a set of text recognition solutions that can comprehend the words and sentences formulated by users. The objective is to understand a need and respond to it.

More specifically, NLP makes it possible to understand what a human being says, to process the data in the message and to provide a natural language response.


Natural Language Understanding (NLU) consists of taking a written or spoken text in natural language and understanding its intentions.  This means that it is a subset of NLP.

NLP interprets the client’s words verbatim, while NLU identifies the intentions and deeper meaning.

Paralinguistic Artificial Intelligence

Paralinguistic AI is the use of AI to detect characteristics of the person other than the verbal content spoken. Examples include detecting the age, gender, tone, or emotion of a written or spoken interaction.

Emotional AI

Emotional AI is just one of the aspects of paralinguistic AI, but certainly one of the most complex and fascinating.

It is defined by the ability to detect an emotional state such as anger, stress, joy, irritation, and to reason with these emotions in mind.

Since robots are not human, they are not naturally equipped with empathy. Emotional AI compensates for this and introduces emotion management into human-machine conversations to respond more appropriately to the customer’s needs by considering their state of mind. For instance, if the customer is angry or in a situation of extreme stress, the bot will instantly direct them to a human. The same applies to written communication: by taking into account the verbal comments and words used by the customer, the bot can deduce an emotional state and provide a contextualized response.

So who are the new contacts for consumers?

What is a bot and what is it used for?

It’s the new buzzword. All brands want their own bot. Is this automated conversation software the future of customer service?

The term “bot” means “robot”, which was originally a computer program that performed automated tasks to help humans with a specific process.

There are several types of bots:

Conversational Agent

This is a bot with Artificial Intelligence, capable of communicating with a human in natural language via a voice or text channel.

Vocal Callbot – Robot

This callbot is a conversational voice agent. It answers the phone when consumers call the customer number.

As soon as the call is received, the Welcomebot greets the customer, identifies them, and understands their request, all of which is expressed in natural language. If this request can be automated, the Processingbot takes care of the entire process. If not, the call is transferred to the appropriate advisor.

When advisors are unavailable, the Overflowbot takes over the calls, which will be transcribed into a structured email to the advisor for asynchronous processing.

The implementation of a Callbot is ideal for managing large volumes of calls and reducing customer waiting time on the phone.

This will relieve call center phone lines and provide an accessible and responsive after-sales service.


In contrast to the Callbot, the Chatbot is a conversational agent that interacts in writing on a website.

There are two types of chatbots. The first is an interface that serves as an additional communication channel between a customer and a human advisor. The second is a real bot, programmed to respond to requests from users who simply type their queries on their keyboard and chat with the bot in a dedicated window. In this case, NLP allows the bot to analyze semantics to provide suitable responses to customer requests.


This is a conversational agent that also interacts in writing, but on instant messaging apps like Whatsapp or Messenger.

Unlike the first two channels, this channel lets the user choose between instantaneous and asynchronous communication. Users can choose to carry on a live conversation from start to finish, or to come back later to see the answer given by viewing the conversation history.

The Messagingbot also generates less formal conversations, such as integrating smileys or pre-defined customer journeys with more empathy. Brands have a huge interest in using this technology to optimize their customer experience, especially in our hyper-connected world.

The more a brand offers its customers different but interconnected communication channels, the more their experience becomes integrated and seamless, which in turn increases their satisfaction and loyalty.

How do you choose your bot?

When choosing a bot, it is important to determine from the get-go whether or not it should be voice-enabled, which is closely linked to:

  • The company’s purpose: to increase online shopping, to strengthen customer relations by being more responsive, to improve the customer experience through the introduction of new communication channels
  • The types of requests to be processed: after-sales service, information on product use, prospect information, sales assistance, reissuing of contract documents
  • The most frequently used communication channels
  • The complexity of the requests

Thanks to the deployment of bots, you will most certainly be able to:

  • Improve your customer experience by offering a 24/7 service with immediate response, multilingual support and solutions tailored to your organization,
  • Speed up your company’s digital transformation by offering multi-channel management of your customer journey and reducing the role of the phone channel
  • Lower operational costs by automating the management of contacts
  • Enhance the value of your company’s personnel by focusing on versatility, cross-functionality, the ability to handle high value-added tasks and thus limit turnover

Are you interested in our bot solutions? Feel free to contact our team – we will be happy to give you a personalized demo.

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