Dr. Charles Handler is a thought leader, analyst, and practitioner in the talent assessment and human capital space. Throughout his career Dr. Handler has specialized in developing effective, legally defensible employee selection systems. Since 2001 Dr. Handler has served as the president and founder of Rocket-Hire, a vendor neutral consultancy dedicated to creating and driving innovation in talent assessment. Dr. Handler has helped companies such as Intuit, Wells Fargo, KPMG, Scotia Bank, Hilton Worldwide, and Humana to design, implement, and measure impactful employee selection processes. Through his prolific writing for media outlets such as ERE.net, his work as a pre-hire assessment analyst for Bersin by Deloitte, and worldwide public speaking, Dr. Handler is a highly visible futurist and evangelist for the talent assessment space.
Can Pre-hire Talent Assessments Be a Part of a Predictive Talent Acquisition Strategy? Over the past 30 years, businesses have spent billions on talent assessments. Many of these are now being used to understand job candidates. Increasingly, businesses are asking how (or if) a predictive talent acquisition strategy can include the use of pre-hire assessments? As costs of failed new hires continue to rise, recruiters and hiring managers are looking for any kind of pre-hire information to increase the probability of making a great hire.
HireVue Assessments are created by industry-leading I-O Psychologists and delivered via video interview. Our validated approach predicts the top performers you need to meet your business objectives. Responses to video interviews are full of data. The content of the verbal response, intonation, and nonverbal communication are just a few of the 25,000 data points we collect. These data points are analyzed with our proprietary machine learning algorithms to accurately predict future job performance.
A system reads "input" data - perhaps assessment scores or CV information. The system does some math to apply a "predictive model" to the input data. The results of the model are shown as "output" data of the model - perhaps the likelihood of the candidate achieving a certain level of Sales Performance or another KPI. At heart, it takes "inputs" and turns them into "outputs" or predicted business outcomes. But to build and validate a model, you need a healthy, logical set of both input and output data for that role in your company.