Assessing fit and skill is one of the biggest challenges for a hiring team. From application to interviews, employers have a few tactics to help them decide whether a hire will stay or turnover quickly.
Unfortunately, more than 50% of voluntary turnover happens within a year of the new hire’s start date and experts estimate 80% of employee turnover is due to bad hiring decisions. Even with the best efforts, companies are failing to accurately assess talent. Enter technology. Thanks to advancements in HR tech, human resources and recruiting teams have brought better efficiency and automation to their programs. And now, technology is offering even more insight into the performance and fit of a candidate thanks to prescriptive analytics.
What are prescriptive analytics? Sometimes called predictive analytics, prescriptive analytics are a technique that uses data mining, statistics, modeling, machine learning and artificial intelligence to analyze current data to make predictions about future. For a talent acquisition team, this means using their existing employment data as a way to make better hiring decisions. Struggling to find diverse talent that fits your team or can’t seem to nail employee retention? This is where predictive analytics shines.
To understand prescriptive analytics, it is essential to first understand intelligent automation. Intelligent automation is data driven decision algorithms that help talent acquisition teams speed up processes. Though these algorithms are more efficient than humans when it comes to data, the real competitive edge of automation comes from the lack of human bias.
Recruiters and hiring managers are great at forming relationships, but not so great at forming unbiased decisions. Since 1989, researchers have studied hiring discrimination with less than stellar results. Over the years, white candidates get an average of 36% more callbacks than black candidates and 24% more than latino candidates. More importantly, there has been no real level of change observed in the last 25 years.
This information goes to show that even the most experienced professional with the best of intentions can suffer from unconscious bias. Machines, however, examine exponentially more data in a fraction of the time all while forgoing those biases. Additionally, when machines do the repetitive work, those HR professionals are free to spend more time on the high-touch, human side of talent acquisition like nurturing great relationships.
Prescriptive recommendations, similar to predictive analytics, use data, statistical algorithms and machine learning techniques to figure out the likelihood of future events based on historical data. The goal is to go beyond knowing what has happened in the past to provide an accurate assessment of what will happen in the future.
Predictive analytics models use known results to develop a prediction value, or model, for new data. Modeling provides results in the form of predictions that represent a probability of the target variable (for example, a loyal candidate) based on estimated significance from a set of input variables.
While predictions are nice, this is where prescriptive recommendations come into play. Simply predicting that a person is a good fit for your company is only one small step of the equation. What if they won’t accept the offer? What if they are highly likely to leave within the first 90 days? Recruiters focused on high quality candidate outcomes also need to know who is most likely to accept an offer and stay with the team long enough to make a substantial impact.
Prescriptive recommendations use data points essential to helping recruiters make better informed decisions in a fraction of the time. This technology maps qualified candidates against the talent DNA of top performers and offers precise recommendations that help recruiters fast track the right talent and accelerate the time to hire. Using prescriptive recommendations gives you the ability to find the right talent of your company while measuring the likelihood of a candidate’s future success within the company.
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