While Big Data is a well-known phenomenon, it is nevertheless difficult to tackle. How to exploit this raw material in a company? How to organize, examine, structure, use? Semantic analysis would offer an initial answer. It is also useful in quantitative (time-saving) and qualitative (data organization).
Companies have two types of data: structured and unstructured. Today, the majority of the data analyzed comes from an organized file (such as the client database). Unstructured data such as opinions or reactions on social networks are usually not fully exploited.
From illegible data to exploitable information
Big Data is characterized by 3V: Volume, Variety and Velocity. In order to operate properly its lines, it is important that every company establishes two additional Vs: Veracity and Value. If their importance is to be demonstrated, it remains to be seen how the entities can verify the accuracy and the source of these data.
Concerning value, semantic analysis qualifies texts or voices in a very short time and on a large number of elements to select only the most relevant ones. They will then be highlighted in the form of, for example, a dashboard. Semantic analysis represents the link between computer reading and human treatment.
The importance of landscape modelling
If semantic analysis is the link between the two worlds, it must first understand the world in which it develops. From one company to another, concepts, languages, and exchanges differ. The company concerned must model its functioning so that it is integrated into the semantic analysis. This modelling with the aim of delivering complete thesauri requires time and energy on the part of all the actors.
Semantic analysis is very useful in a recruitment process. It compiles all the applications received and compares the similarities between the key words of the offer and the candidatures. The CVs displaying the best rates of correspondence are thus retained.
Some semantic analysis tools work with grammatical modelling (which makes it possible to recognize a verb or a nominal group). The solution identifies the know-how, skills and expertise on the CV. In order to verify the reliability of the assertion, the tool can overlap with data available on the Internet, especially on professional social networks.
Enhance customer and collaborator experience
Technological tools are also working to improve customer satisfaction, employee satisfaction and turnover.
The combination of mechanisms of semantic analysis and Artificial Intelligence dissects the behaviour, characters and insights of oral clients. This real-time review offers the company greater responsiveness and active listening to its customers. For example, Starbucks has put it in place: tools have found that customers want free Wi-Fi in the cafes and the ability to pay directly via their phone. These changes were made in the weeks that followed.
On the customer service side, teams are overwhelmed with e-mails. The semantic analysis automatically sorts them according to their degree of urgency, allowing the agent to improve his reaction time for the greater happiness of the clients and the reputation of the company.
Embracing new concepts
The speed of semantic analysis also enables companies to detect weak signals. These signals embody new concepts, strategies or guides that emerge slowly in professional language. The mechanism does not require a prerequisite to analyze these data: they will therefore arrive more quickly to the agent concerned. These signals are very useful for companies and represent added value.
Beyond the work on texts and voices, semantic analysis solutions are able to interpret feelings. This skill is used to prioritize information or to isolate an unhappy customer in order to accelerate and improve the customer experience, as well as employee experience. With the bracelets connected, we will soon see the facial analysis that will capture the emotional insights in real time for an even more personalized experience.