Matching Technology

talent peak matching technology can be used to make recommendations which can help program leads form a team of matching mentors and mentees since the matching criteria can vary from program to program, talent peak matching technology provides a lot of flexibility to configure the matching algorithm that matches the requirements of each program setting up the matching algorithm the team matching algorithm can be configured from choosing ‘configuration’ under the ‘teams’ tab and choosing the ‘matching algorithm sub tab the algorithm allows use of any existing employee attribute that can be provided by hris system (e g workday, sap, adp) or custom attributes that can be collected from the user as part of the program join request the above screen shot shows an example program configured with primary attributes that were received from hris and custom attributes that the user will provide as part of their program join request survey each of the attributes can be configured to match or not match or be ignored the custom attributes can be configured by clicking on “ + ” sign next to the select custom attributes you want to match section which will open a survey creation form as shown below the custom attribute survey form allows data to be requested in the form of multiple choice question which is depicted with a check box (see fav color in the example below), single choice question which is depicted with a radio button (see fav sport in the example below) or rating (see negotiation skill level below) application of the matching algorithm once configured the matching algorithm is put to use on the talent peak application either when the program lead is trying to pair mentors and mentees for admin led programs or when a user of the platform is trying to discover matches in peer to peer programs the matching algorithm compares the attributes of the subject mentee with a pool of available mentors to make recommendations of the best match similarly the mentors can also be matched against the available mentees the matching recommendation is provided as a numerical score which is calculated as the average of %age matched of each attribute the percentage match for each attribute depends upon the attribute type for single choice questions or numerical/rating attributes, percentage match is either 0% (not a match) or 100% (a match) e g for fav sport above, if mentor and mentee both select soccer then it is a 100% match but if mentor selects soccer and mentee selects basketball then it is a 0% match for multiple choice questions, the percentage match is calculated as number of matched options divided by the number of options that would give a 100% match for example if mentee selects red and yellow as their favorite color and mentor selects red as favorite color then it is considered a 50% match as one out of the two selected options matched for every recommendation provided by the matching algorithm, the details can be seen by clicking on the “i” button next to the overall match (as shown below) please note, the matching algorithm is just a recommendation tool and it is up to the discretion of the program lead to use the recommendation or not sometimes program leads download the survey report to look at the raw data and form teams based on their own criteria