Article: Empowering people decisions with predictive analytics

A Brand Reachout InitiativeRecruitment

Empowering people decisions with predictive analytics

As the business landscape undergoes unprecedented waves of transformations, predictive analytics plays a unique role in recruiting talent that will drive productivity and unleash innovation.
Empowering people decisions with predictive analytics

Faced with complex challenges such as the hybridisation of professions, the shortage of talent, and the lack of skills, being able to anticipate an individual - be they a candidate or an employee - and their potential, skills and behaviours are crucial for any organisation.

In the context of recruitment, traditionally collected information such as educational qualification, past experiences, reference checks and interactions during interviews is insufficient to indicate a solid indication of success in a position.

While past behaviour could help predict future behaviour, pre-employment experience does not. Schmidt & Hunter's landmark study on the predictive validity of recruitment methods found that work experience predicts only 16% of future performance.

And the study goes even further by highlighting the correlation of performance when several methods are used together. For example, the combination of cognitive ability and personality assessments can predict future performance with 78% accuracy.

Hence, a multi-criteria assessment approach based on predictive analytics is one of the best indicators for better determining an individual’s ability to succeed in a position.

What is predictive analytics?

Predictive analytics is already transforming talent acquisition and management processes. As an application of artificial intelligence, predictive analytics makes it possible to use the power of data to anticipate the behaviours of candidates/employees and predict their future performance.

Built on several years of research, Central Test’s Predictive Model responds to new paradigms, thus making it possible to establish a prognosis of an individual’s matching, centred around targeted roles and projects from multiple assessments to job success,“ says Patrick Leguide, CEO of Central Test.

The premise of the Predictive Model is to combine a pragmatic HR approach, based on a set of success criteria, with a scientific outlook that focuses on the full scope of the assessments. This leads to more reliable and easy-to-interpret predictions, as well as an enhanced candidate experience when the results are fed back.

Central Test’s Predictive Model combines the success criteria associated with jobs, roles or projects with evaluating these criteria via multiple and converging methods to generate reliable predictions.

It can, therefore, predict the future success of candidates or employees by identifying those best suited for the targeted positions or roles while highlighting their strengths and areas for improvement.

What are the benefits of using predictive analytics?

Our decisions are a combination of intuition and analyses based on objective data. However, in many cases, our intuition alone reigns supreme, generating many cognitive biases that lead to poor decision-making.

“While there is a consensus on the need for objectivity in our evaluations, one of the challenges we face is, defining the requirements for jobs and setting up a matching process that is both predictive and easily interpretable, which thereby facilitates meaningful discussions between HR, managers or coaches and the individuals being assessed,” explains Patrick Leguide, CEO of Central Test.

Hence, this is the ultimate goal of any predictive analytics tool. 

Assessing potential must therefore meet a specific purpose, i.e. to ensure a good match between the “target profile” for a role, and the characteristics of the candidate or the employee being assessed by identifying any mismatches or areas for improvement. Not only does this address recruitment and development needs for a given role, but it can also be helpful for career guidance and mobility, where job specifications are also required to identify suitable projects more accurately.   

If predictive analytics tools support HR players in optimising their recruitment processes, they are also of great help in other HR sectors, such as:

  • In internal mobility, to measure the gap between the profile of an employee and the skills required to perform new functions;
  • In onboarding, by combining the corporate culture and the candidate's mode of operation, to maximise the integration of incoming talent and build on commitment;
  • In development and training, by targeting the development potential, style and learning abilities of each employee to create tailor-made paths that guarantee the development of new skills;
  • In educational and professional guidance, the data gathered generates a range of current and perfectible skills of an individual and opens them up to possibilities of professions in coherence with their potential;
  • In psychosocial risk prevention (PSR), thanks to combined data on sources of dissatisfaction, interests and stress factors, motivational actions are adapted to each profile;
  • In the management of high-performance teams, the analytics produce a team map that makes it possible to assign to each talent the tasks that stimulate their potential and to exploit a team's capital skills.

A predictive analytics tool performs finer, faster, more complex and less biased actions than humans. It processes and provides reliable and objective information that HR professionals use to accomplish their objectives more effectively. The objective of a predictive analytics tool is not to replace the HR professional but to support and help them in their decision-making.

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Topics: Recruitment, Talent Acquisition, HR Analytics

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