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Robo-recruiter: Better results, less effort, lower costs

by Joseph Murphy

Stock pickers, individuals who collect data from various sources to make complex decisions, are going away in favor of data-enabled processes that produce better outcomes. Are recruiters next?

Traditional investment selection decisions in actively managed portfolios are based upon listening to investor earnings calls, reading the lines (and between the lines) in annual reports, and evaluating market and trend reports and press releases. Assimilating and aggregating all this data leads to conclusions and decisions about what to buy, hold, or sell. But even by this method, nearly 70 percent of actively managed mutual funds underperform their peer index. 

BlackRock, the world’s largest investment management company, has documented that intentionally collected, systematically structured and analyzed information leads to really good investment decisions. And this combination of insights from analysis and human judgment creates more favorable outcomes at a fraction of the cost of using expert humans.

Armed with this evidence, BlackRock has scaled back its team of stock pickers and estimates it will reduce investor fees by $30 million using a combination of human judgment and algorithms from big data analytics. The availability of data and analytic methods has transformed the process of making investment selection decisions. The results are better performance and lower costs.

Traditional candidate selection decisions in actively managed recruiting process are based upon listening to or watching interviews, reading the lines (and between the lines) of resumes and job applications, evaluating the relevancy of market experience, and scrutinizing career trends and other details available by reference checks. Assimilating and aggregating all this data leads to a conclusion about whether or not to hire a candidate.

According to the 2016 Candidate Experience Awards and Research program, 75 percent of companies are using some form of pre-employment assessment. Furthermore, 53 percent of companies have conducted a form of big data analytics called validation analysis to create a candidate job-fit scoring algorithm. Algorithms are used to place weight on various factors under consideration.

Insight into on-the-job performance is the primary business objective for the use of these decision-support tools. The difference in above-average and below-average performance can be over 100 percent. Recruiters who use assessments make more objective and fairer decisions, are more efficient, improve new-hire retention, and collect the data to prove it.

In jobs with high applicant-to-hire ratios, assessment data can help reduce the number of phone screens and face-to-face interviews by 20-30 percent. Assessment data helps recruiters identify where to invest their time.

In jobs with traditionally high levels of early attrition, assessment can improve 90-120 day retention by 40-50 percent. Bank of America's awarding-winning assessment strategy delivered $6.8 million in savings through improved employee retention. Assessment data helps recruiters identify candidates more interested in a career and with a more reliable approach to work.

By reducing turnover and streamlining recruiter effort, assessment lowers costs and improves selection decisions.

Are robo-recruiters the way of the future? A selection process that is half data, half human may not be as threatening or far-flung as it sounds. The decision to hire someone may always be an act of personal judgment, but the right data can transform the way your selection decisions are made. 

Learn more about augmenting your recruiter’s selection decisions and what Shaker’s HireScience™ can do for your organization.