It’s important to define what engagement of candidates and employees means in the context of HR.
A candidate who interfaces with a company for the first time to propose their CV and apply for an open position is similar to a user who comes across a new brand: the emotions and perceptions arising from that first impression can compromise the user or consumer experience.
A candidate takes part in the hiring experience knowing that they will make decisions that will have consequences. If the application process is particularly long and complex, if the recruiter (or the company) do not respect the established response times or, even, if a response never arrives, it is easy to be influenced by emotions and reactions. All of this can lead the candidate to judge the company negatively and potentially abort their candidate journey. They could even go as far as to share their negative experience online or directly with their network.
How can all of this be avoided? With the spread of artificial intelligence solutions, affective computing technologies and sentiment analysis systems, it is now possible to verify employee engagement and candidate experience and better understand how to intervene.
Affective Computing is a solution taken from Artificial Intelligence and is used to investigate aspects of human-machine interaction. Specifically, it is focused on developing systems that are capable of recognising and expressing emotions.
Let’s work with a common theory: technology must always be used in a conscious way, particularly if we are talking about introducing AI solutions into companies. Using AI, companies can be proactive to recognise and respond adequately (and thoughtfully) to a specific need: they can give value to the users’ online experience, understand the emotional association they develop with the company or brand and discover the dynamics behind certain consumer decisions (this often also uses neuroscience).
The main objective is to have information that relates to the candidate experience and employee engagement.
Artificial intelligence is able to collect and analyze data within a variety of fields. Specifically it is able to use natural language, monitor images and videos with analysis of facial expressions and body language, and make use of speech recognition systems.
Sentiment Analysis, also known as Opinion Mining, can also be used to analyse texts and extract opinions. By comparison, Natural Language Processing (NLP) algorithms are able to recognise text forms and types, while sentiment analysis detects for example the positive or negative sentimenti with which a comment has been written.
In the HR field, sentiment analysis can be applied to evaluate a potential candidate’s behavior on social networks. The algorithm analyze the interaction (a post or a comment) and provides data on the candidate.
As we have already mentioned, there are several systems that are used in the recruitment phase to evaluate the candidate experience and engagement. At the core is the perception that a candidate has of the company or brand, and this is complemented by the feeling that the candidate or employee associates with the company and their reactions in certain situations.
In general, the first contact between a candidate and the company is through the website. Through Eye Tracking it would be possible to monitor the eye movements of the candidate to understand what they look at and for how long, also discovering through their navigation choices what arouses their interest. A similar solution could also be adopted in the management of pre-screening questionnaires: having data relating to response times for a specific question would be an additional element to understand the candidate’s profile.
Many companies have also added chatbots to their websites. These are digital assistants which are constantly active and available to interact with candidates. In this case, text analysis technologies (Natural Language Processing and Sentiment Analysis) facilitate human-machine interaction: it is easier to understand the questions and provide adequate answers with a Natural Language Generation process.
Video interviews (live or deferred) further enrich the candidate’s profile. Through voice detection and the analysis of facial expressions, it is possible to identify anxiety, tension and stress through variations of the tone of voice or in eye or jaw movements.
These and other technologies can be adopted at different phases of the recruitment process (before or after hiring) and also in completely new areas in order to help improve the engagement of future employees in any sector.
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