How Does Mentor–Mentee Matching Really Work? Behind the Scenes of Matching in Mentoring

In most articles about mentoring you can read that proper matching of mentors and mentees is key to a program’s success. That’s true. However, far less is said about what such matching looks like from a technical perspective and why, in larger programs, manually pairing participants ceases to be effective.

It is often the quality of the match that determines whether participants will meet regularly, achieve their development goals and be willing to recommend mentoring to others.

So what does matching look like behind the scenes? Which data is taken into account? And why is creating a few good pairs a completely different challenge than designing an entire mentoring program? Jacek Tkaczuk, Co-Founder Mentiway, talks about this.

In brief

• Good matching takes many criteria into account — experience, development goals, competencies, expectations and preferred style of collaboration.
• In larger programs manual pairing loses effectiveness, because the number of possible combinations grows rapidly.
• What counts is the quality of matching across the whole program, not creating one ideal pair at the cost of the other participants.

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• The Mentiway platform analyzes participant data and recommends pairs using different matching models, which makes the matching more scalable, objective and less time-consuming — get in touch with us.

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• Also read:
  • Pairing mentees with mentors – effective matching in mentoring
  • How to measure mentoring effectiveness? KPIs, metrics and the data that really matter

Good matching is the foundation of the entire mentoring program

At first glance this seems obvious. A mentor should have experience that matches the mentee’s needs. Participants should communicate well and have room to collaborate.

In practice, however, matching is much more complex. As Jacek Tkaczuk says:

“Saying that pair matching in a mentoring program is important sounds like a truism — but in practice it is precisely the foundation of the whole mentoring process. Well-matched pairs translate into the quality of collaboration, participant engagement, and overall satisfaction levels for both mentors and mentees.”

It’s therefore not just about finding a mentor with the right experience. Matching should also take into account development goals, competencies, expectations for the collaboration, or even preferred working style. That is why organizations are increasingly moving away from intuitive participant pairing and are beginning to treat matching as a strategic element of the whole program. Read more about this, how to match mentors and mentees in our article.

Manual matching only works up to a point

Many organizations launching a mentoring program build the pairs themselves. HR knows the participants, analyzes surveys and tries to find the best connections. With only a few pairs, this is perfectly feasible. However, a problem arises when the program starts to grow. Jacek points to a challenge that is often not visible at first glance:

“The biggest challenge, however, isn’t creating a few well-matched pairs. The real difficulty comes when we want to make sure that as many mentees as possible receive mentors who address their actual needs.”

He adds:

“At larger scale — above 20–30 pairs — the number of possible combinations reaches unimaginable proportions. One person attempting to do this manually will very quickly get bogged down selecting a few best matches, and the remaining pairs will necessarily be chosen less precisely.”

That is why more and more organizations use mentoring platforms that support matching. Not because HR can’t match participants, but because at larger scale it simply becomes an optimization problem. And if you want to find out, how to measure the effectiveness of mentoring, also check out this article.

Matching is not a lottery. It’s the analysis of many criteria at once

A good match shouldn’t be based solely on the job title or department where participants work. In reality, many more factors are taken into account. As Jacek explains:

“That’s why at Mentiway we approach pairing algorithmically, not mechanically.”

The first step is to create what’s known as a scoring matrix.

“Our recommendation algorithm works in two stages. In the first, we create a scoring matrix — an assessment of each person’s potential fit with every other.”

That means the system analyzes all possible mentor–mentee combinations. Factors considered include, among others:

• participants’ development goals,
• professional experience,
• competency areas,
• mentors’ specializations,
• self-reported information,
• additional signals analyzed by AI.

This ensures matching isn’t random and doesn’t rely on a single criterion.

The paradox of the ideal pair

Interestingly, creating a single perfect pair doesn’t necessarily mean the entire program will work well. It’s one of the most interesting aspects of matching that people rarely talk about. Jacek explains:

“The second stage — often less obvious but in practice even more important — involves distributing mentors among mentees so the whole program functions optimally as a system.”

In other words, it’s not about one person getting the best possible mentor at the expense of others. The goal is to find a solution that works best for the entire program. As he emphasizes:

“The aim isn’t for a single pair to be perfectly matched at the cost of the rest, but for as many participants as possible to receive a sufficiently high-quality match.”

This approach closely resembles designing complex logistics systems or transport networks. Here, too, you need to find a solution that is optimal for the whole group. Want to know, how mentoring supports employees in a world of constant change? Read this article.

What does mathematics have to do with mentoring?

It turns out quite a lot. At Mentiway, optimization methods from computer science and mathematics are used in the matching process. As Jacek says:

“We use methods at the intersection of mathematics and computer science, including a min-cost max-flow approach that lets us optimize the entire set of pairs rather than individual decisions.”

Although the name may sound very technical, the idea is relatively simple. The algorithm tries to find an arrangement of connections that will provide the best possible fit for the whole group of participants while respecting certain constraints. As a result:

• mentors are more evenly distributed,
• a greater number of mentees receive well-matched mentors,
• the whole process is more transparent,
• the organization can more easily scale the mentoring program.

Transparency is as important as technology

One frequent challenge in mentoring programs is participants asking, “Why was I paired with this person?” With manual matching the answer can be hard. With system-based matching the situation looks different. As Jacek summarizes:

“The result is a more even and fair distribution of matches. The process becomes scalable, less administratively burdensome and more objective.”

He adds:

“Equally important, this way of working limits accusations of randomness or bias in pair selection — decisions are based on clearly defined criteria and a transparent recommendation logic.”

This is particularly important in large organizations, where transparency of the process affects participants’ trust in the entire program.

Summary: good matching combines technology and an understanding of people

Although matching is increasingly supported by algorithms and artificial intelligence, its aim is not automation for automation’s sake. The most important thing remains understanding participants’ needs and creating conditions so that mentoring relationships can develop naturally.

Technology helps analyze thousands of possible connections, but it’s a well-designed mentoring process that turns those connections into valuable relationships. That’s exactly why matching is today one of the most strategic elements of modern mentoring programs. You can also read more about effective pairing in mentoring in this article.

Would you like to see how matching works in practice?

At Mentiway we help organizations design mentoring programs based on data, transparent principles and advanced mechanisms for matching mentors and mentees.

We’ll show you how our recommendation algorithm works, what criteria it takes into account, and how to conduct matching even in large mentoring programs – quickly, objectively, and with a high-quality match.