Your next hire is already in your ATS.
Recruiters often overlook quality candidates already sitting in their CRM or ATS. Here is how to find them again — and fill your pipeline faster.
What is matching in recruitment?
Recruitment matching consists of assessing the candidate fit between available profiles and a company's needs. The goal is to align a role's requirements (technical skills, experience, soft skills, location, and so on) as closely as possible with each applicant's characteristics.
Matching now sits at the heart of modern hiring processes, because it streamlines application screening and quickly surfaces the most relevant talent.
Why matching transforms recruitment.
The main benefit of matching is the time saved on recruitment. By automating the analysis and pre-selection of applications, recruiters can focus on high-value profiles. It also improves the candidate experience through faster feedback and more objective evaluation.
Using a matching algorithm reduces certain human biases, and favours a more objective evaluation of applications.
How matching actually works.
Matching combines several technologies and methods:
- Semantic CV analysis: tools scan and interpret the content of CVs and job ads through advanced CV parsing.
- Artificial intelligence algorithms: they compare selection criteria (hard skills, soft skills, experience, availability) and assign a compatibility score to each application.
- Predictive matching: some solutions estimate a candidate's potential success through models based on history or online assessments.
In practice, a platform like Jemmo offers automated sourcing in natural language, an explainable AI matching, and rediscovery of your internal talent pool, to speed up and strengthen every step of recruitment.
Which tools for matching.
Matching tools have multiplied:
- ATS and HRIS (Applicant Tracking System and HR Information System) increasingly include matching modules and automated application screening.
- Specialised platforms leverage semantic analysis for affinity or predictive matching.
- Solutions like Jemmo enrich a public candidate database, making it easier to rediscover your internal talent pool and optimise automated sourcing.
AI and algorithms make it possible to go beyond simple keywords and take into account the full career path, soft and hard skills, and even geographic location.
Bringing matching into your HR strategy.
To get the most out of matching, it is essential to:
- Define precise selection criteria, including soft skills and technical expertise.
- Ensure the quality and structure of job ads and CVs to ease semantic analysis.
- Combine automated matching with human validation to avoid misinterpretation or recruitment micro-biases.
- Train teams to use the tools and interpret the generated compatibility scores.
The right approach. Use matching to pre-select, but always validate qualified applications through in-depth interviews.
The limits of matching.
Despite its strengths, matching has its limits:
- Algorithms can reflect or amplify existing biases if the input data is biased.
- Automated matching does not replace human intuition and judgement, especially when assessing potential or motivation.
- Some atypical or career-changing profiles risk being filtered out if the criteria are set too rigidly.
It is therefore recommended to monitor results regularly, adjust the algorithms and gather user feedback to improve the relevance of matching.
Who is it for, and in which contexts?
Matching is for any organisation looking to optimise its recruitment process: agencies, HR directors, talent acquisition teams, SMEs or large groups. It is particularly suited to high application volumes, or to cut time-to-hire on scarce or technical profiles.
Finally, it helps reduce bias and supports objective evaluation in selection procedures, while improving the speed and quality of application matching.
