A flawless CV, a convincing interview, strong references... and yet, just a few months after starting, the employee leaves their role. This scenario is all too common. Many hires fail not due to a lack of technical skills, but because the individual’s profile doesn’t truly match the role’s requirements or the working environment. This is precisely where predictive matching comes into play.
The principle of predictive matching: a scientific approach to recruitment
Predictive matching is based on a data-driven model that analyses and cross-references three essential dimensions:
- Role success criteria: technical skills, expected behaviours, preferred working style, and interpersonal requirements.
- The candidate’s full profile: personality, cognitive abilities, core motivations, professional values, past experiences and growth potential.
- The organisational context: company culture, team dynamics, management style, level of autonomy, and work pace.
By cross-referencing this data, a compatibility score is generated, which helps predict a candidate’s long-term success and engagement in a given role. The aim is to complement human judgement with objective data to significantly reduce errors caused by cognitive biases or subjective impressions.
Why the “best CV” isn’t always the best choice
Relying solely on past experience is like driving with your eyes on the rear-view mirror. A candidate who excelled in a highly structured company may fail in a fast-paced startup that requires constant improvisation. Likewise, a highly qualified graduate may struggle with adaptability or motivation in a constantly evolving environment.
Take, for example, two sales candidates applying for the same role. One has ten years of experience in a large corporation; the other has just three years in an SME. Assessments show that the second candidate demonstrates strong adaptability, thrives on challenge, and has a relational style that aligns perfectly with the team culture. In this case, predictive matching reveals that the less experienced candidate is actually the better fit.
The keys to successful predictive matching
1. Define success criteria for the role
Effective predictive matching begins with clarity. What skills, attitudes and outcomes are expected within the first 6 to 12 months? Start by identifying 5 to 7 key observable behaviours for the first six months, along with the essential technical competencies.
Review past hiring failures in similar roles to extract lessons learned. A clear benchmark is essential to objectively compare candidate profiles.
2. Assess beyond technical skills
Psychometric tools allow you to assess personality traits, motivations, and cognitive abilities—dimensions that interviews alone often miss, yet are essential for long-term performance. Select an integrated assessment platform that combines personality tests, cognitive evaluations, and situational judgement tests. Define a 45-minute assessment journey tailored to the role’s specific requirements.
Choose your tools wisely: for a sales role, focus on interpersonal skills and resilience; for a management accountant, prioritise logical reasoning and precision.
3. Factor in managerial and cultural context
A candidate’s success also depends on their fit with the team and management style. A highly autonomous person may feel stifled in a structure where every decision requires approval. Conversely, someone who needs clear frameworks may struggle in a highly flexible environment. Map the existing team’s personality to understand current dynamics and identify complementary profiles—or potential friction points.
Use situational exercises or behavioural questions during interviews to validate cultural fit beyond numerical scores.
4. Combine data with human judgement
Predictive matching is not about letting an algorithm make the final decision. It’s a decision-support tool that enhances recruiters' and managers' objectivity, while keeping human judgement and conversation at the core of the process.
To optimise hiring decisions, conduct a 30-minute HR-manager debrief after assessments. Prepare interview questions based on identified weaknesses and compare top candidates not only on scores, but also on their alignment with the role’s priorities.
The benefits for your organisation
By relying on scientific, objective data rather than gut feeling, you significantly reduce the risk of a poor hire.
- Financial impact: For an SME, avoiding a single hiring mistake can save between €30,000 and €80,000 (salary costs, onboarding, training, lost productivity, and HR time to rehire).
- Operational impact: Less time wasted on repeated recruitment, reduced pressure on teams dealing with high turnover.
Tangible benefits:
- Candidates appreciate the transparency and gain a clearer understanding of expectations.
- Objective assessments reduce perceptions of discrimination or favouritism.
- Even rejected candidates leave with a positive impression of your company, protecting your employer brand.
- A modern and innovative approach attracts more qualified and motivated candidates.
Predicting success in a role isn’t about finding the “best profile on paper”—it’s about identifying the right potential for the right role in the right environment.
Predictive matching brings the objectivity needed in a recruitment process often swayed by instinct or time pressure. When used well, it transforms hiring into a strategic driver of performance and team stability.
Recruitment is not just about filling a vacancy. It’s about investing in long-term success, building strong teams, and creating the conditions for sustainable growth.
