Harvard Business School Explains Virtues of Metrics in Hiring
Harvard Business School recently revealed findings on the use of machine learning as an “important decision aid for managers looking to make hiring and promotion decisions.”
According to the research, “hiring is essentially a prediction problem.” In other words, “When a manager reads through resumes of job applicants, she is implicitly trying to predict which applicants will perform well in the job and which won’t.”
Statistical algorithms are designed to solve these types of problems. According to data from a recent HBS study, it’s suggested that “police departments can predict, at the time of hire, which officers are most likely to be involved in a shooting or accused of abuse.”
HBS offers “five principles for using statistical algorithms to aid the personnel selection process” for organizations interested in putting them to use:
- Pick the right performance metric: HBS says the right metric is often “a combination of characteristics,” like a potential salesperson’s balance between “likelihood of turnover, projected close rate, and impact on relationships with clients.”
- Collect the right variables: HBS says, “Effective algorithms require human intuition, experimentation, and iteration to decide which characteristics to measure about an applicant to help predict the performance metric you care about.”
- Gather many data points: The company recommends tracking each new hire’s performance and application data. “Algorithms will use these data points to help guide future hiring.”
- Compare apples to apples: “If the best salesperson took on the hardest clients, they might have the lowest closing rates,” HBS says. “The right performance metric will need to adjust for the underlying difficulty of the task.”
- Anticipate incentives: “Salespeople incentivized to close deals at any costs may score well on a performance metric, but provide little of value to the company,” the company warns. Employers who anticipate this will “create sufficiently broad metrics to take strategic behavior into account.”
The article concedes the limited purview of machine learning on its own. But when coupled with human intuition, the sky’s the limit!
About the Author
Jonathan Pfeffer
Jonathan Pfeffer joined the Clear Admit and MetroMBA teams in 2015 after spending several years as an arts/culture writer, editor, and radio producer. In addition to his role as contributing writer at MetroMBA and contributing editor at Clear Admit, he is co-founder and lead producer of the Clear Admit MBA Admissions Podcast. He holds a BA in Film/Video, Ethnomusicology, and Media Studies from Oberlin College.
