To begin with, HireIQ’s ML-based ‘Audiolytics’—a patent-pending, predictive performance innovation— simplifies the painstaking process of interviewing, evaluating and selecting the right talent. When hiring for the best, it turns out that voice matters. This is precisely why Audiolytics was born from the extraction of thousands and thousands of voice features in candidate voice response made available through emotion and affect classification tools. “Based on voice analysis of these features extracted from candidate voice responses, Audiolytics determines a clear recommendation of candidate’s fit for specific roles within an organization,” explains Paul Noone, CEO of HireIQ. Models using ML techniques take the voice features as input and provide insights into how a given candidate will perform in each role. This data collection during the interview process and subsequent processing allows HireIQ to zero in on the signals that indicate success within candidates.
Based on voice analysis of thousands of features extracted from candidate voice responses, HireIQ determines a clear understanding of candidate’s fit for critical roles
HireIQ has worked with Fortune 500 companies and the world’s largest Business Process Outsourcers (BPOs) to quantify both efficiency and performance results associated with their solution. One such client needed to fill thousands of positions in less than a month and had sourced over 20,000 applicants for their open positions. Using HireIQ, the customer could sort the candidates meeting minimum thresholds in skills and predicted performance. This enabled the customer to reduce their entire interview process time (application to candidate disposition) from 9 days to 3 days (a 67 percent improvement), convert 75 percent of their applicants from application to completed interview (30 percent more than their standard process) and increase the daily productivity of their recruiters to 200 candidates per day. The average recruiter review time for the candidates in this effort was less than 4 minutes because of the clarity of the data presented through HireIQ. Having 45 percent of the interviews completed after normal business hours, meant that they were reaching employed candidates through extending the interview through non-business hours. The outcome data from the hired agents demonstrates their ability to exceed expectations in first call resolutions, transfer rates and even training graduation rates.
Riding this wave of success, HireIQ will continue to leverage AI and ML to improve the recruitment process and ensure quality hires in the workplace. “In the future, we will gather even more data to refine our current ML approach and drive the innovation of new services for our customers,” adds Noone. “We are actively working with large organizations that will help us with the distribution of our solution in meaningful ways around the world.