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
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.
NLU
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.
