Predictive Recruiting: Fad or True Recruitment Leverage?

Predictive recruiting is a new HR method that uses data and algorithms to estimate, from the selection phase, the probability that a candidate will succeed in a position. This approach, aimed at making better decisions and reducing hiring mistakes, is increasingly appealing to companies.

According to a LinkedIn study, 73% of HR professionals now consider data analysis a priority. Faced with talent shortages, can predictive recruiting become a true recruitment leverage? JobAffinity explains everything about this HR innovation: definition, advantages, limitations, tools and concrete implementation in your company:

Predictive recruiting in summary

• Predictive recruiting uses big data and AI to anticipate a candidate's future success and performance in a given position.
• When used properly, predictive recruiting can be a true decision-making tool, making recruitment fairer and more effective.
• It aims to make decisions more reliable, reduce casting errors and optimize recruitment time. It also helps prioritize applications, objectify choices and respond to volume and shortage challenges.
• For effective implementation of predictive recruiting, it's necessary to set clear objectives, have reliable data, and use a testing phase, involving teams.
• ATS, like JobAffinity, psychometric tests and AI solutions for scoring are used for predictive recruiting.

What is predictive recruiting?

Predictive recruiting, or predictive hiring, consists for HR of using artificial intelligence and data to recruit better. Thanks to machine learning, recruiters analyze available data on candidates (their background, skills, past performance, or personality) to estimate their chances of success in a given position.

Predictive recruiting thus relies on statistical models and algorithms with a process that unfolds in 3 steps:

• Data collection: gathering maximum information about candidates, which can come from multiple sources (CV, personality tests, application history, etc.).
• Statistical analysis: AI compares current data with that of profiles already recruited, with their results in the company. The algorithm learns to identify common points between candidates who succeeded and those who didn't hold the position.
• Predictive scoring: for each new candidate, a score is calculated that should reflect the probability that they will succeed in the proposed position, according to criteria identified as relevant. This score is not an absolute truth, but it's an indicator to guide the recruiter.

For example, for a sales position, an algorithm can identify that candidates with specific training, experience in cold calling and good verbal ease succeed better. Following this analysis, it will then prioritize similar profiles during selection.

What are the challenges of predictive recruiting for HR?

In a context where companies face talent shortages, pressure on hiring quality and the need to save time, predictive recruiting offers a reliable and practical solution. It aims to enable recruiters to make more reliable, faster and fairer decisions during recruitment.

It's a strategic tool that allows HR teams to face both the volume of applications to process and the complexity and responsibility of the recruitment process:

• Facing pressure on recruitment: recruiters either have too many applications for certain positions, or a shortage on others, with little time to analyze them. Predictive recruiting can help quickly prioritize the most relevant profiles.
• Making the process more objective: even with experience, a good recruiter can be influenced by impressions or biases. Predictive recruiting helps overcome this by relying on quantifiable criteria, making selection fairer and more consistent. Moreover, by structuring selection criteria, predictive recruiting improves recruitment communication with candidates, with clear and targeted messages.
• Reducing recruitment errors: predictive recruiting helps limit the risks of bad hires that often cost companies dearly, by improving the match between the candidate's profile and the position's requirements.
• Integrating a responsible AI approach: the use of algorithms involves respecting transparency, non-discrimination and GDPR, strategic issues that HR must master.

The use of artificial intelligence in recruitment can have a significant impact on candidates, which is crucial to consider before integrating predictive recruiting into your process.

Indeed, trained on data provided by HR, the algorithm can develop a bias that automatically excludes certain CVs that actually matched the position (due to past recruitment trends or poorly defined criteria). Thus, it's essential to master AI tools well, to ensure optimized recruitment that remains human and transparent.

Do you want to know how to optimize your recruitment process with AI without dehumanizing it? Discover our webinar on recruitment and AI, presented by Alexandre Noto (Intuition Software) and Anaïs Le Digarcher (Culture RH).

What are the advantages and limitations of predictive recruiting?

Predictive recruiting is a recruitment tool offering many advantages, but also several drawbacks to consider. While it saves recruiters time, makes decisions more reliable and is a lever promoting diversity during hiring, it presents risks related to algorithm flaws, lack of transparency or technology dependence.

The advantages of predictive recruiting

• Time savings for recruiters: by automating the sorting of applications according to precise criteria, predictive recruiting helps reduce time spent on pre-selection. Recruiters can then focus on qualitative analysis of the best profiles, rather than spending hours examining CVs.
• Predicting matches between candidate profile and position needs: algorithms identify correlations between certain backgrounds, behaviors or candidate skills, and success in a given position, improving recruitment precision.
• Potential lever to promote diversity: when well designed, predictive models can help overcome certain subjective filters such as name, age, gender, or origin, sometimes barriers to recruiting certain profiles.
• Optimized candidate experience: for candidates, predictive recruiting helps avoid excessively long waiting times or vague criteria, which contributes to strengthening and improving the satisfaction rate of talents, even those not selected.

The limitations of predictive recruiting

• Algorithm biases: an algorithm makes its decisions based on the data provided to it. If this data is biased, for example, if it reflects a past tendency to favor certain profiles over others, the algorithm will learn to do the same. This risks excluding certain candidates, not for their skills, but because of poorly defined criteria.
• Transparency issues: some algorithms give results that are difficult to explain. However, in recruitment matters, GDPR requires being able to justify any decision made based on automated processing and being able to explain why a candidate was selected or not.
• Technology dependence: recruitment remains above all a human process that allows judging motivation, the quality of interview exchanges and the candidate's personality. Predictive tools, unable to account for these elements, therefore cannot replace a real recruiter.

How to implement a predictive recruiting strategy?

To implement an effective predictive recruiting strategy, three steps are necessary:

• 1st step – clearly define objectives: what are the company's priorities in terms of recruitment? Do you want to reduce recruitment time, improve hiring quality or identify high-potential profiles? These choices will determine the data to analyze and the indicators to track.
• 2nd step – properly prepare the data to use: for predictive recruiting to be effective, it must be based on reliable and quality data: former employee performance, integration success rates, departure reasons, manager feedback, etc. It can also be useful to exploit data from your career site: click-through rates, candidate journeys, conversion rates, to enrich predictive models.
• 3rd step – implement predictive recruiting progressively: it's important to test predictive recruiting on a first designated type of position or service before extending it to the entire company.

In this logic, all stakeholders must be involved: recruiters, managers, management, as well as legal teams, because these are the people who will use it.

What tools to use to put predictive recruiting into practice?

A good predictive tool relies on reliable data, but also on methods with strong predictive validity:

• ATS software integrating predictive features
• Psychometric tests,
• Dedicated artificial intelligence solutions.

By predictive validity we mean the ability of a tool or selection method to predict a candidate's future performance in a given position. In other words, the more a test or recruitment process has strong predictive validity, the more reliable it is for anticipating whether a candidate will perform once in position.

ATS integrating predictive features

ATS software integrates predictive features that can be very effective for structuring and optimizing the recruitment process.

Our JobAffinity recruitment software offers you a complete tool to optimize your recruitment, providing you with:

• Automatic application sorting tools,
• Customizable dashboards to track recruitment performance,
• A recording of each action, which makes the process clearer and more organized,
• Real-time tracking of each application's progress.

This set of features from our JobAffinity ATS allows collecting and cross-referencing data to quickly identify the most suitable profiles, while allowing recruiters to keep control over the final decision.

Psychometric tests

Psychometric tests measure key elements such as candidate personality, their logical and verbal aptitudes, and their behaviors in certain situations. Predictive recruiting compares test results with those of employees who succeeded, to identify personality traits or aptitudes that most often recur among the best profiles.

For example, in a sales environment, an algorithm can identify that high resilience scores are often present among the best sellers.

AI solutions dedicated to predictive scoring

AI solutions dedicated to scoring go further and combine several data sources: CV, tests, video interviews, HR history, etc. to automatically produce a compatibility score between the candidate and the position. These artificial intelligence solutions are particularly useful for companies that process a very large volume of applications.

Some professional platforms, like LinkedIn, also integrate these predictive features to analyze profiles, backgrounds, interactions or even user intentions, and recommend the most relevant candidates for a position. These tools are useful upstream, particularly for sourcing and proactive recruitment approach.

Is predictive recruiting right for you?

Predictive recruiting is not reserved for large groups or data experts. These advances in recruitment can be useful for any organization facing volume, reliability or recruitment structuring issues. To know if this approach can help you, here's a list of questions to consider.

*Does your organization check several of these boxes? *

• You receive a large number of applications and lack time to analyze everything.
• You have difficulty quickly identifying the right profiles despite precise job descriptions.
• You want to reduce turnover rate or avoid recruitment errors.
• You often recruit for similar or high-stakes positions.
• You would like to rely on reliable data to objectify your choices.
• You want to structure your recruitment without making it more complex.
• You are attentive to issues of equity, transparency and GDPR compliance.

If you recognize yourself in these challenges, then predictive recruiting can greatly help you structure and make your processes more reliable. At JobAffinity, we believe in recruitment that is both human and data-driven. Predictive recruiting, when used well, is not a revolution nor an end in itself, but a decision-making tool, serving your human resources challenges.