HireVue Announces Data-driven Candidate Recommendation Engine
HireVue Insights uses big data and personalized digital interactions to recommend candidates based on 15,000 interaction, behavioral and performance attributes, and how they correlate to the organizations current top performers.
HireVue Iris, the deep learning analytics engine that powers HireVue Insights, analyzes a unique data set of interactions, feedback and outcomes that never before existed. Iris was built based on over 3 million interview responses. Each candidate interview contains 100,000 times more bytes of jobs data than the resume or profile traditionally used for identifying candidates. The platform examines attributes in three major categories: interview attributes, behavioral attributes, and performance attributes. Iris’s proprietary algorithms discover patterns and learn which attributes predict performance, then scores each candidate on how they compare to existing top performers. Iris also scores interviewers based on how their historic ratings and feedback correlated with hiring and performance outcomes.
“Recruiters and hiring managers rely heavily on instincts, hunches and memory to choose the right candidates, but there isn’t a lot of data to help them predict who will become a top performer, or decide who should be interviewing candidates,” said Mark Newman, CEO of HireVue. “This could be the most important innovation in recruiting in the past 25 years. HireVue Insights analyzes over 100,000 times more data than a resume, all within the context of your organization, your positions and your feedback. It gets smarter over time to become your own personal data-driven hiring model.”