Semantic analysis in customer relations

What is semantic analysis?

Semantic analysis is a crucial branch of natural language processing (NLP) that aims to understand the meaning of words and phrases in a given context.

Unlike other language processing methods, such as lexicography or morphology, which focus on the literal meaning of words or their grammatical structure, semantic analysis focuses on contextual meaning.

In practical terms, semantic analysis goes beyond simple word recognition (such as comparing a character string with the same string in a dictionary) to explore the relationships between them. It takes into account the overall context, nuances and connotations that can influence the meaning of an utterance. Unlike syntactic analysis, which focuses on grammatical structure, semantic analysis goes beyond the formal rules to grasp the real meaning.

Let’s take an example. In the sentence: “The black cat sleeps on the carpet”, the syntactic analysis will focus on the grammatical structure of the sentence, identifying the components such as the subject (“The black cat”), the verb (“sleeps”), and the object (“on the carpet”). It will show how the words are organised to form a grammatically correct sentence. Semantic analysis, on the other hand, interprets that the ‘black cat’ is a domestic animal, that ‘sleep’ implies a state of rest, and that the ‘carpet’ is probably a comfortable place to rest. In addition, it captures the semantic relationship between these elements, concluding that the cat is sleeping peacefully on the carpet.

Semantic analysis also differs from lexical analysis, which looks at the meaning of individual words, by broadening its scope to include understanding the intentions, emotions and ideas expressed in a text.

Here’s another example. In the sentence: “The film was a rollercoaster of emotions”, lexical analysis will focus on the meaning of the individual words, identifying that “film” refers to a cinematographic work, that “rollercoaster” is a metaphorical expression evoking emotional highs and lows, and that “emotions” refers to feelings. But semantic analysis will go further, beyond the individual meanings, to understand the overall meaning of the sentence. It will interpret that the film provoked a variety of emotions in the viewer, ranging from excitement to anxiety, creating an experience similar to that of an emotional rollercoaster.

How can semantic analysis be used in customer relations?

Data needs to be collected from interactions between companies and their customers. This data can come from a variety of sources, such as telephone calls, e-mails, chats, online reviews, etc.

Once the data has been collected, it needs to be processed by a semantic analysis tool. These tools use algorithms to analyse the meaning of words and phrases.

There are many tools on the market, some generic, others (such as Akio Insights) dedicated to customer relations.

What is the use of semantic analysis in customer relations?

Semantic analysis is primarily used in customer relations to understand customer needs and expectations.

It is used to analyse customer comments, opinions and questions. This opens up opportunities for companies to improve their products and services, for a more personalised customer experience.

A Forrester study in 2022 showed a 22% improvement in the recommendation rate for companies using an AI semantic analysis engine to process customer reviews.

Semantic analysis can also be used to detect potential problems that could affect customer satisfaction. For example, analysing customer comments can help to detect recurring problems with a product or service.

For example, a major airline was forced to review the size of the seats on some of its aircraft thanks to feedback from the semantic analysis carried out by the Akio Insights software on its customers.

Of course, semantic analysis is widely used by the artificial intelligence engines of the various software packages dedicated to customer services, particularly for its ability to automate low value-added tasks – such as answering standard questions like “What are your opening hours? In certain situations, it can also provide agents with relevant information, making their work easier.

For example, analysing data from past interactions with customers (stored in the exchange history) will provide clues as to topics of particular interest to the caller.

Conclusion: many advantages

As we can see, semantic analysis provides a better understanding of customer expectations, with the corollary of offering more appropriate products and services and creating a more personalised customer experience.

It improves the work of customer advisers by providing them with a list of needs or highlighting a particular point to watch out for. It can also lead to significant cost savings by automating certain tasks, such as:

– Intelligent routing, which automatically detects the queue to which an incoming email should be sent.

– On-the-fly queue creation, for example when a hot news item generates a large number of identical requests over a limited period of time.

– Pre-qualification, which consists of pre-qualifying the customer request by analysing either the conversation with a chatbot or the content of the written request.

– Or prioritisation by sentiment, which is useful for responding first to requests and complaints relating to contentious issues or irritants.

The applications of semantic analysis are set to multiply over the coming months… and the tools will become even more effective and powerful. These are developments that our AI teams observe and take into consideration before integrating them into AKIO products.

* Gartner, “The Future of Customer Service: A Gartner Hype Cycle Report”, 2023