Here you will find answers to commonly asked questions surrounding how artificial intelligence is used in Beamery's Talent Match tool. For more information on using Suggested Contacts and Talent Match Scores in Beamery, click here.
Beamery's AI Talent Match is in Early Access. If you don't currently have this feature, or are interested in participating in Early Access Programs in the future, please contact your CSM.
Q: What are the Match Scores available?
Match scores are given from 0-5 where 5 is an excellent fit and 0 is a poor fit. Hover over the match score for each candidate to learn more about how the match score was calculated.
Q: What parts of a profile are used to create the Match Score?
- Job Title - how close does the candidate's current job title match the Vacancy title.
- Skills - how relevant are the candidate's skills to the skills required for the Vacancy title.
- Seniority - how closely does the candidate's seniority match the seniority in the Vacancy title.
- Company Industry - how similar are the industries the candidate has worked when compared to the customer's industry
- Company Size - how similar are the company sizes the candidate has worked when compared to the customer's industry
Q: How are each of these weighted to determine the final score?
30% - Job Title
30% - Skills
12.5% - Seniority
22.5% - Company Industry
5% - Company Size
Q: How is seniority defined?
Seniority is defined by matching the vacancy title and the current job title of the candidate.
Vacancy Title: Senior Product Manager
Candidate Job Title
Sr. Product Manager
Jr. Product Manager
Q: How are skills defined?
Skills are inferred by matching the Vacancy's skills to the candidate's identified skills as well as adjacent skills using the Skills API. From there, the model identifies the intersectionality of those skills in order to calculate an appropriate match. Vacancy skills can also be calibrated using the Vacancy Calibration feature for quick iteration.
Q: What could cause poor matches?
Low data quality is usually the root cause of poor matches. The AI that powers Talent Match runs on relevant data in your CRM. Missing skills, companies or roles in the candidates or vacancies can create poor matches. Also skills, companies or role labels that are misspelled or missing letters (e.g. Snr IT Anlst)
Q: What languages does Talent Match support?
The current version of Beamery's AI Talent Match only supports Vacancy and Candidate data in English. Later version updates will support additional languages.
Q: What happens to the feedback I provide for poor matches?
Beamery captures Talent Match feedback and reviews it offline on a regular basis. We then use this data to help us understand how we should continue to improve the model's accuracy.
Q: Are there any limits on the number of records Beamery’s AI Talent Match can process?
There are no hard limits on the number of inbound applicants that can be scored against the vacancy. The current limit on suggested contacts from the CRM is 1000 per vacancy.