semantic analysis

Semantic analysis – exposing the value of your company’s data

Big Data may well be a familiar phenomenon to some, but that doesn’t make it any easier to apprehend. How can a company exploit this raw data? How can it be organised, examined, structured, and finally, used? Well, one initial solution to these problems would be semantic analysis. Not only is semantic analysis useful in quantitative terms (as a time-saver), it is also effective on a qualitative level (for data organisation). 

Simply put, businesses have two types of data in their possession; structured and unstructured. Whilst the majority of exploited data comes from an organised source, such as a client database for instance, unstructured data such as opinions or reactions gathered on social media do not, since they are not only time-consuming to collect but on a larger scale also bring very few results.

From data we can’t read to information we can exploit

Big Data can be characterised by the all important “Three V’s”: Volume, Variety, and Velocity. To exploit the data lines correctly, it’s important for each business to include two additional V’s: Veracity, and Value. Whilst we already know how important these elements are, we now need to find out how businesses can check the accuracy and source of the data.

With regard to Value, in just a short time semantic analysis can qualify texts or voices out of a large set of different elements, selecting only the most relevant ones to the exercise. The selected data will then be displayed in a spreadsheet/dashboard format. Semantic analysis really is the link between computer and human processing.

The importance of modelling the landscape  

As semantic analysis is the link between these two different worlds, first of all it must fully comprehend the world in which it is being developed, as concepts, languages and communication differ from one company to the next. Businesses must therefore create a model based on their way of working so as to integrate it into their semantic analysis. The model, whose aim is to deliver an entire, complete lexicon, requires the time and energy from everyone involved in the project. Where the IT system hosts the semantic analysis tool, API’s scatter the lexicon throughout the whole of the IT system.

Semantic analysis is also extremely useful when it comes to the recruitment process. It can gather all the applications received and compare the keywords from the job offer with each application. The CVs with the best match-rate will then be put forward.

Some semantic analysis tools can also function through grammatical modelling (verb or noun group recognition), where the tool can identify the skills and expertises written on the CV. To check the reliability of the information, the semantic analysis tool can cross-reference with data readily available online, such as on professional social media sites.

Improving customer and employee experience

Tech tools also work to improve customer satisfaction, employee satisfaction, and also company turnover.

Combining semantic analysis and artificial intelligence mechanisms means that we can unravel customer behaviour, their characters and the insights that they tell us rather than put to us in writing. This real-time analysis allows the company to put into place better response times and also means that they can be more active when listening to their customers. Starbucks, for instance, has already put this into action. Their tools revealed that customers wanted to have free Wi-Fi in stores and to be able to pay directly from their smartphones. The changes were made in the weeks following the analysis.

From a customer service point of view, teams are submerged with emails. Semantic analysis can automatically sort the incoming emails by order of priority, therefore allowing the employee to improve their response times. This will not only greatly please customers but will also go towards improving the company’s reputation.

Embrace new concepts

The speed at which semantic analysis operates also means that businesses can detect weak signals more easily. These signals represent new concepts, strategies or guides which are slowly but surely emerging into our professional language. The data will be sent back to the employee even quicker because the tool doesn’t require any prior information to analyse it. These signals are extremely useful for businesses and represent real added value.

Besides working on texts and voices, semantic analysis solutions can also interpret feelings. This capacity is used for prioritising information or isolating an unhappy customer, for instance, in order to speed up and improve the customer – and employee – experience. Thanks to smart bracelets, we will soon see the arrival of facial analysis, a tool that will capture emotional insights in real-time, allowing us to live out ever more personalised experiences.

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Mathilde Thébault

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