Future proof
The model was built to enable flexibility between mentoring programmes to allow for future adjustments to both mentor and mentee requirements and programme adaptations.
Ease of use
Careful consideration was given to ensure ease of use for the client whilst still allowing the model user to be able to adapt inputs and change set criteria. The focus of the model was to create a process for the client whereby minimal involvement in the initial filtering procedure was needed, as well as prioritising suitability criteria between mentee and mentor for the most effective outcomes.
Separation / Simplicity
Clear separation of inputs, calculations and outputs was necessary to ensure simplicity and to help navigate the model. Whilst some of the formulas were complex, separation ensured the logic was easier to follow.
Challenge
Level 20 required a model that would support the process of matching mentees to mentors in the private equity space. While a mentoring committee hand checks all the final pairings to ensure the highest quality outcomes, the
high volume of applications meant the initial manual assessment process consumed a great deal of time and resource.
The solution required an accurate automation process that could filter mentees and mentors based on eligibility criteria, and then rank these candidates based on a prioritisation guide. A semi-automated process would then allow for the user to pair sets of eligible mentees and mentors.
Solution
Criteria
Logic to ensure that each mentee and mentor met the base level requirement was established at a programme level. Calculations to filter out those not eligible were used and highlighted to show where they failed to meet the set
criteria.
Prioritisation
Each eligible mentee and mentor were then ranked based on a prioritisation guide. This scoring system was built dynamically so prioritisations could be adjusted.
Pairing
Based on these prioritisations scores, a pairing dashboard was created. The client was able to match a mentee to a mentor based on how suitable they were to each other (e.g Location, area of speciality, experience). This mentee was then filtered from the list and only the unpaired mentees remained for subsequent pairing.
Results
The client will now be able to use the model going forward to provide a clear view of mentee to mentor pairing for a high volume of data with minimal manual processes necessary.
Other Success Stories
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