Learn More About Predictive Recruitment
Predictive recruitment is set to profoundly disrupt company methods. This system, which largely relies on Big Data, enables a more refined candidate search and helps find the profiles best suited to a position.
What Does Predictive Recruitment Involve?
Predictive recruitment allows companies, through an algorithm, to better target candidates who may match a given position.
To achieve this, the company must identify the key performance factors for the position to be filled. This involves analyzing a wide range of parameters. This range includes, among others, personality traits, behavior, teamwork skills, stress resilience, competencies, experience, motivation factors, satisfaction and recognition drivers, etc. Beyond seeking the candidate’s skills and interpersonal qualities, the goal is also to find candidates who will thrive in their role and whom the employer can retain.
Predictive recruitment involves identifying the factors within the company that have driven employee performance. Data is collected from individuals holding similar positions and from N+1 and N+2 management levels. The objective is to define the highest-performing profiles internally and identify similar talents in a candidate pool.
The collected data enables the definition of a profile model.
Advantages and Disadvantages of Predictive Recruitment
Predictive recruitment is a system designed to “accelerate” and enhance the reliability of the candidate recruitment process. Recruitment often proves costly in terms of time and financial resources. Technology thus provides valuable assistance to recruiters in reading, sorting and selecting relevant applications. They can focus their activity on interviews and confirming the results of tests and algorithm-based selection. Beyond all these elements, it will always remain necessary to verify the candidate’s motivation for the position, their ability to adapt to the company culture and values, and the fit with the management style of the hierarchy and the team composition.
For candidates, this system brings greater neutrality to the selection process and can surface atypical profiles with high potential. Indeed, certain candidates who do not exactly meet the recruiter’s specifications for a position (education, experience, industry, company size…) may emerge and be deemed interesting, whereas they might not have been shortlisted in a traditional recruitment process. As such, predictive recruitment proves to be more objective.
But let us not forget that, despite all existing algorithms, it is always the recruiter who has the final say and who will choose the candidate best suited to the position and its environment.
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