CV matching & recruitment: definition and uses

Definition of the matching in recruitment

Matching is an English word that designates the compatibility of two elements according to precise criteria.

Today, it's often associated with technology (particularly artificial intelligence), historically on dating sites; but also in many other sectors, notably retail / e-commerce or education / training.

And of course in human resources, more specifically in recruitment.

CV matching: a recruitment definition

Matching applied to recruitment is often referred to as “CV matching”. CV matching can identify relevant profiles. But a “good” profile is only highlighted by a matching algorithm if it's compared to a specific job offer on precise criteria such as the candidate's skills, experience or even aspirations.

Data from the vacancy and the profile are compared and translated into a compatibility index, sometimes called “scoring”.

Good to know: CV matching can also be an opportunity for candidates. If it's practical for a recruiter to have an initial sorting of applications based on a matching technology, it's also true for a candidate who wants to have an initial sorting among a huge quantity of job offers. The focus is always on compatibility between the offer, its missions and benefits & the candidate's profile and experience.

How does CV matching work, this dynamic, referential-based technology?

CV matching is a technology based on a more or less complete data referential. Once the data has been read, understood and structured (thanks to a parsing technology we'll explain below), the aim of CV matching is to calculate “distances”.

For example, are candidates' skills close to the skills expected in the job offer? The closer the match, the better.

But matching doesn't stop at a single parameter: the aim is to offer a CV matching that capitalizes on several criteria, such as skills, experience, level of education, languages spoken, driving license, etc. Depending on the importance of the criteria, they may or may not be predominant.

CV matching technology is therefore based on this reference system, but it needs to be updated regularly for several reasons:

• there are many terms for the same thing
• a term can have several meanings, depending on the context
• new words may appear over time (new techniques, new professions, etc.)
• etc.

The matching algorithm therefore needs to be “trained” to understand these subtleties, and to add those it didn't catch before. Solution providers always check that new terms and contexts are fully understood, so that CV matching technology is as relevant as possible.

Good to know: at CleverConnect, our databases are “alive”, because we work with around forty job portals that enable us to process the data on a daily basis and update our referential as accurately as possible, on an ongoing basis.

Parsing & recruitment: the essential technology for matching

For there to be CV matching... there has to be CV parsing! We'll explain.

Parsing is an English term that means “grammatical analysis”. When we talk about CV parsing, we're talking about analyzing the information contained in a CV.

CV parsing involves extracting and structuring data from a CV (or profile), transforming this information into formatted elements that the matching algorithm can analyze and compare with its referential. This is the initial stage in the CV matching process.

Here's how it works:

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CV matching: what a recruiter would do, faster

CV matching, like all technologies, is nothing more than a speed-up for actions that could be carried out manually. Its aim is to make the recruiter's life easier, but it obviously cannot replace it. However, it does make it possible to identify relevant profiles more quickly, increasing the recruitment process's efficiency and offering recruitment teams the chance to reduce their time-to-hire.

CV matching therefore replaces / accelerates the first stage of the process: CV screening. This automatic pre-selection must then be perfected by the recruiter. He/she will use the matching scores to guide his/her actions for the next stage (selection for an interview, proposal to apply for a position, etc.).

CV matching and criteria objectivity, an anti-discrimination technology?

The impartiality of artificial intelligence can be questioned in ways that humans cannot. It's very complicated (if not impossible) to be totally impartial when you're human. This is perhaps even more the case in the pre-selection phase.

Matching technology can really avoid human bias, as the compatibility of a CV and a vacancy is based on defined criteria: skills, job titles, soft skills, etc.

On the other hand, since CV matching is a tool and the recruiter has control over some of its parameters, it may reflect the choices made by the human setting. If the gender criterion is requested by the recruiter... obviously there will be discrimination. But it won't be because of the algorithm, only the way it's used. 

The other “anti-discrimination” benefit you get by using this technology as the first step in your selection process: you open up the door to profiles that might not have been considered for different reasons. E.g.: some recruiters may decide to stop when they see a candidate's age (unfortunately) or the fact that he or she didn't go to a certain school. If matching is based on skills, artificial intelligence won't see any of this, and will propose the most suitable profiles for the job. This is a good way of finding really relevant candidates who might otherwise have been taken out of the stack. 

It's also a way for candidates to open their minds. E.g.: a talent is used to work in customer relations, but his/her skills could be very useful as a trainer/coach. CV matching can show him/her a few jobs that are different from what he/she would have looked for on his own. Sometimes for the better.

Which stages of the recruitment process can be supported by matching?

Matching can be used at different levels.

First and foremost, for recruiters: thanks to this matching technology, they can more easily find the right profile (or profiles) for the position they have to fill. They can do this either from their ATS applications, or directly from their internal talent pool.

This is all the easier when companies use a CRM (Candidate Relationship Management) to structure their talent data.

But it's also the case for candidates, who can find the right job offer for their profile:

• Job boards or CV libraries, where they can post their CV directly.
• On career sites, with features such as 1-click application

Matching & candidate relationship management: the winning duo

You might think that matching is only useful in volumetric recruitment, as it has the ability to find a few key profiles in a vast ocean of applications. And it's true that these days, candidates are in short supply in some sectors. Following this logic, matching would be less useful. But this is a rather short-termist view of recruitment. Matching also has its full place outside volumetric recruitment. Its main objective remains to save time and increase relevance. This applies to all recruitment types.

Matching can also be applied to a CRM tool, which has the advantage of multiple talent pools:

1. candidates,
2. former candidates (successful or not)
3. unsolicited applications
4. referred candidates,
5. former employees,
6. talents met at trade fairs, etc.

When a database becomes this exhaustive, it can be more difficult to find the ideal candidate. This is where matching offers real added value. While CRM already enables you to structure your database using tags or filters, matching allows you to find, at a glance, the most relevant profiles in relation to a specific vacancy, without having to do any searching. Multiple criteria can be used in a single click.

Also, unlike a filter or tag, the matching function offers scoring, which ranks the most suitable profiles in order of relevance. The recruiter therefore benefits not only from sorting, but also from prioritizing further action.

Good to know: the CleverConnect platform integrates parsing and matching into all its modules (referral, CRM, career site (CMS)) to make life easier for its customers. 

The CleverConnect platform is a living recruitment ecosystem that is very sustainable for us: A lead that is not hired immediately can be hired in the future. We needed a system that adapts to our very special needs: a combination of automations, referrals as well as nurturing functionalities and a central CRM to store all our profiles from various sourcing channels as well as matching to quickly find the best candidates in our growing talent pools.

Florian Schrodt - Head of Employer Branding & Recruitment @medbase