Matching mentors and mentees
Organizations begin mentoring programs with an aim to connect the right people and promote a learning environment with career progression benefits. A key component of any mentorship program is the matching of mentors and mentees. Finding the right fit can be complex and program coordinators often struggle with this process.
Listed below are some ways to match participants in a mentoring program.
Traditional manual mapping
A traditional way of matching mentors and mentees has been mapping seniors to juniors within the same team. The juniors learn from the personal and professional experiences of the seniors through planned interactions. Reverse mentoring programs can also be designed in the same way by interchanging the mentors and mentees. The mentorship program coordinator manually assigns mentors to mentees. The drawbacks of this method are the time and effort it entails to match participants manually. This task also gets more tedious as the team size increases or when the ratio of seniors and juniors in a team is skewed. Manual mapping also does not accommodate for various custom attributes of the participants such as diversity parameters for a more nuanced mapping.
Peer mentoring is when employees with similar years of work experience are mapped to each other. One employee might have worked longer in the organization while one could be a relatively new employee. Peer mentoring is taken up when the number of senior leaders in the company is relatively smaller compared to the number of other employees. It works well during the onboarding process and helps new joiners to understand the internal processes and how the company works. It also helps in developing collaborative projects. Peer mentoring also eases networking and can help remote employees feel connected to the organization.
The most sophisticated method of mentor-mentee mapping is through algorithms available in mentoring software solutions. A pairing algorithm takes into account key attributes to match mentors to mentees. These typically include attributes available in the HRIS system such as date of joining, department, gender, office location and others. More advanced mentoring platforms, such as Teleskope Talent Peak, allow for custom attributes to be added to the algorithm. These custom attributes like ethnicity and interests could be derived from ERG management software or be collected from the participants through instant surveys. Moreover, the capacity of how many mentees a mentor can manage can also be set in the platform for the mentoring program. The personalities of mentors and mentees could also be matched by integrating the results of personality tests undertaken by participants into the algorithm. The algorithm then automatically matches mentors to mentees based logic and the input parameters.
In case of large global organizations with long-term, company-wide mentoring programs, manual mapping is next to impossible. Algorithms can be used however, they may have capacity limitations. In such situations, where mentoring programs are planned at scale (for example for 5000 to 10000 employees), a higher-level solution such as Teleskope Team Builder is needed. This module creates pairs among the participants using the attributes defined for matching and aims to leave no employee unpaired. The percentage of matching attributes varies to ensure maximum participants find a match.
With the advancements of technology, matching mentors to mentees is now a multidimensional task which takes into consideration the purpose of mentoring while aiming for an accuracy in matching mentors to mentees even at scale.