The success of a mentoring program depends largely on how well mentors and mentees are matched. A good match fosters engagement, career development, and long-term relationships. However, the matching method—whether Admin-Driven, User-Driven, or Hybrid—can significantly impact efficiency, fairness, and program outcomes.

At Brancher, we use a science-based AI algorithm to generate high-quality match recommendations. The way these recommendations are finalised depends on the matching approach. Here’s a breakdown of each method, along with their pros and cons, to help you choose the right approach for your organisation.

1. Admin-Driven Matching

How It Works:
Administrators oversee the matching process, using AI-generated recommendations to assign mentors and mentees based on skills, values, goals, and more.

With admin-matching, you can choose to prioritise:

• The maximum number of people paired
• The maximum compatibility of pairs

Pros:

✅ Fast – Particularly beneficial for programs where you want everyone matched as soon as possible. Admin matching is done all at once, whereas user-driven matching can stretch out over several weeks (depending on how long people take to send/accept mentoring requests).

✅ Even Mentor Distribution – Prevents mentor overload by balancing capacity across the program, ensuring all mentors are engaged.

✅ Bias-Free Matching – Whilst both user-driven matching and admin-matching use the algorithm (to minimise bias), with user-driven matching, users can still override the algorithm and request a specific mentor. Admin-matching removes this bias and ensures everyone is matched based on personality, values, skills and more.

✅ Strategic Alignment – Administrators can ensure matches align with leadership development, succession planning, and DEI (diversity, equity, and inclusion) goals.

Cons:

❌ Less Perceived Control for Participants – Mentees and mentors don’t actively choose their match, which may reduce initial buy-in.

❌ Data Quality is Crucial – If the matching criteria are incomplete or inaccurate, matches may require adjustments.

❌ Potential for Manual Adjustments – While AI can optimise matches, administrators may need to fine-tune pairings based on participant feedback.

❌ Potential for Bias from Administrators – Administrators who review the admin-matches are also prone to bias and may be tempted to ‘override’ the algorithm based on their own assumptions or knowledge.

Best For:

✔ Programs that require speed where there is a set deadline for the program’s kick off and end date.

✔  Programs that require admin oversight and approval of mentee-mentor matches.

✔ Small, medium or large scale programs.

✔ Programs where you have a lot of introverts or a low trust culture where people are hesitant to ask for help

✔ Pilot programs or new mentoring programs where people are ‘warming up’ to mentoring

2. User-Driven Matching

How It Works:
Participants receive AI-generated mentor recommendations, with their top three matches highlighted. Mentees can browse profiles and select the mentor they prefer, or search for other available mentors in the program.

Pros:

✅ Empowers Participants – Mentees feel a sense of ownership over their mentoring journey, which can lead to stronger engagement.
✅ Encourages Relationship Chemistry – Participants can choose based on personal fit, personality, skills, experience and more.
✅ Higher Initial Buy-In – Mentees are more likely to commit to the relationship when they have chosen their mentor.

Cons:

❌ Risk of Mentor Overload – Some mentors may receive too many requests, while others are overlooked. With Brancher, when a mentor hits their maximum capacity (e.g. 2 mentees), they are fully booked and do not receive any further requests until they have availability again.

❌ Requires Active Mentee Participation – If mentees don’t request a mentor, administrators must follow up, potentially delaying the program. With Brancher, we send reminders to mentees every month to nudge them into a relationship if they are not already in one.

❌ Selection Bias – Mentees may pick mentors who are similar to them rather than those who could challenge or stretch them professionally.

❌ Can Be Confronting for Introverts – If you have a lot of introverts in your organisation or if you have a culture where people are hesitant to ask for help, user-driven matching might be uncomfortable for some people. Admin-matching, where people are assigned into a relationship may be a preferred approach.

Best For:

✔ Programs where mentee engagement is a top priority.

✔ Industries where networking and self-driven career growth are highly valued.

✔Programs where no admin-approval or review of matches is important

✔Medium or large scale programs

3. Hybrid Matching

How It Works:
Hybrid matching is a combination of both user-driven and admin-driven matching. Generally, the order of hybrid matching is (1) Mentees can self-match within a set period, then (2) administrators step in to assign matches for those who haven’t selected one. Hybrid matching can also be vice versa in the opposite order.

Pros:

✅ Offers Flexibility While Ensuring Full Participation – Mentees have the freedom to choose, but admins can step in to ensure no one is left out.
✅ Balanced Mentor Distribution – Prevents mentor overload while still allowing mentees some choice.
✅ Encourages Active Engagement – Mentees feel empowered to select a match, increasing their commitment.

Cons:

❌ Requires More Management – Admins must track progress and intervene when necessary.

❌ Timing is Key – If the self-selection window is too short, mentees may feel rushed; if too long, it could delay the program start.

❌ Communication Must Be Clear – People can get confused about why they have been ‘forced’ into a relationship if they thought they had choice. It must be made very clear to all users about the matching approach and timelines.

Best For:

✔ Programs where participant choice is valued but structure is needed.
✔ Mid-sized programs that want a balance between engagement and efficiency.
✔ Organisations that want mentee-driven matching without the risk of unassigned participants.

Which Matching Approach is Right for You?

Key Takeaways: 

✔ Admin-Driven Matching is best for small, medium or large programs with set deadlines, and organizations with clear strategic goals.

✔ User-Driven Matching works well in medium or large programs where mentor capacity isn't a concern, particularly in industries valuing networking and self-driven growth. 

✔ Hybrid Matching is ideal for mid-sized programs seeking balance between participant choice and structured matching.

Making Your Choice: Next Steps

Selecting the right mentoring matching approach is crucial for your program's success. While each method has its merits, your choice should align with your organization's culture, program goals, and operational capacity.

Key Considerations for Your Decision:

• Your program's size and timeline requirements
• Organizational culture and participant preferences
• Administrative resources available
• Strategic objectives (DEI, leadership development, etc.)
• Level of control needed over the matching process

Ready to Transform Your Mentoring Program?

Don't let the complexity of mentor matching hold your program back. Brancher's AI-powered platform can support any matching approach you choose, ensuring optimal outcomes for your mentoring initiative.

Take the Next Step

Request a demo today to see how Brancher can:

• Implement your preferred matching approach seamlessly
• Leverage AI for evidence-based matching recommendations
• Scale your mentoring program efficiently
• Track and measure program success

Contact us now to schedule your personalized demo and discover how Brancher can elevate your mentoring program to new heights.