Predictive Modeling Hiring: The Key to Talent Acquisition Success

by Pranav Ramesh
May 26, 2021
Predictive Modeling Hiring: The Key to Talent Acquisition Success

John Carter is a talent acquisition manager in a software company. In the short-skilled market, he has been finding it increasingly difficult to recruit and retain candidates as most of them tend to leave within the first few months. The hiring, training, and retaining the talents is costing Carter and his firm a fortune. Despite having numerous profiles from prospective candidates, it seems increasingly impossible for him to hire the right resources. However, Carter is not alone. Despite using technology, website, and data analytics, organizations fail to identify the right resources. According to a Gartner report, “Only 21% of HR leaders believe that their organizations are harnessing talent data.”

According to the U.S. Department of Labor, the cost of a bad hire is around 30% of the employee’s first-year earnings. To avoid such mishaps and save costs, organizations are moving toward predictive analytics in Human Resources.

What is Predictive Modeling?

Predictive modeling or predictive analytics is the use of historical data to observe patterns and predict future behavior. It is widely popular across numerous industries which use predictive analytics to offer better customer services. For instance, the music station suggestions you receive on Spotify or your shopping suggestions on Amazon come from listening or purchasing history. Autocorrects, text suggestions, email filers, and streaming suggestions on Netflix are a few examples of predictive analytics in our everyday life.

The success of predictive analytics in every industry and vertical is now finding popularity in the hiring space. More and more organizations are switching to the use of predictive modeling for their sourcing, hiring, and employee engagement while aiming to increase retention potentially.

Role Of Predictive Modeling in Hiring

An average job posting receives over 500 applications, and the recruiters do not have the time to go through all of them to find the potential candidates. This is where predictive analytics can come into play. Data-driven recruiting has been around for over two decades now. However, the HR departments’ recruitment metrics and the SLA-based reporting do not hold good in the skilled labor shortage market. This is because most organizations have been using big data for reporting rather than predicting future behavior.

The evolving technology, changing nature of the job market, and sophisticated assessment tools are now being ingrained into the algorithms to use the data to predict the outcome of the recruitment process.

Data-driven recruitment involves tapping into the large volumes of data lying within disparate systems, developing a 360° view of the candidate by combining the different data points, enabling analysis/interpretation of the data, and providing actionable recommendations to the talent acquisition teams.

Predictive modeling in recruitment helps the recruiter develop a 360-degree view of the prospective hire by tapping into the massive volumes of data. The data interpretations provide actionable insights to the recruiting team, including the candidate’s availability, interest, and qualifications. Besides that, it could also predict if the candidate falls under the category of high-risk individuals who could potentially quit within the first 12 months of being hired. The costs involved in sourcing, recruiting, developing, and recruiting a candidate could cost anything from 16% to 213% of the candidate’s salary or an hourly wage.

How To Make Predictive Modeling Strategies Work to Your Advantage?

Hire Candidates with High Caliber

According to the Human Capital Benchmarking Report by the Society for Human Resource Management, the average cost per hire is $4,129, and it takes around 42 days to fill a new position. And if the organization is trying to fill the position of an existing candidate, the numbers soar higher. On average, a recruiter has to go through 250-300 resumes and cover letters per job opening.

With most employees having similar skills and expertise, it becomes difficult to weed out the best for the interview and selection process. However, predictive hiring will help recruiters identify applicants who will be the best fit for the organization and its goals. As the algorithms do most of the work, the recruiters will allocate more time to each of the short-listed candidates and know them before during the recruitment process. In other words, it helps them identify and focus their energies on the top talent in the pool.

Understand What Works and What Doesn’t

With numerous job boards, recruitment firms, and recruiters trying to attract the top talent, the options could be overwhelming. However, when organizations use predictive modeling, they can pinpoint the exact source from where they are finding their best talent and reduce their job posting efforts considerably. Besides saving money, it would also eliminate redundancy and help the hiring team streamline their recruitment efforts.

Reduce Employee Turnover

A research report by the Work Institute found that around 75% of the causes of turnover are preventable. Organizations can use employee predictive modeling to understand which employees are a “flight risk” basing on an analysis of employee attributes. It would help organizations factor in numerous variables that could lead to employee resignation and the potential financial impact this would have on the organization. Having this data makes it possible for the human resource teams to build a targeted retention or hiring plan.

Hewlett-Packard (HP) is an enterprise information technology company that has over 300,000 employees. As a pioneer in technology, HP has been using HR predictive analytics for several years now and found that employees generally quit after their performance appraisal.

However, the organization had to delve into further details to understand which employee was at a higher flight risk because the cost of replacing the mid-level employees was around millions of dollars for the organization. Using the findings from numerous surveys and past data, the organization created a dashboard with key metrics on employee retention and satisfaction. According to Siegel, HP saved around $300 million with the help of predictive modeling to calculate this flight risk and reduce turnover.

Avoid Bad Hires

Seventy-five percent of the recruiters have recruited the wrong person for a job, a bad hire could cost around US$15,000, and when you multiply the number of bad hires, it could make your budget go for a toss. A bad hire disrupts the teams’ performance metrics and impacts the confidence of the team members. A bad hire is not just an employee who underperforms, but also someone who is ill-tempered lowers the employee engagement or leaves the organization within a short span.

While the resume can help the recruiter determine an employee’s skill set, it is hard to identify the other elements. A highly qualified employee could be a micromanager and lead to an unproductive workplace. Without workforce analytics, it’s difficult to identify what makes a strong hire. Using the right kind of intel will help you stay wary of any surprises. You will be prepared to adjust your recruitment process to identify and accommodate the right candidates. In Conclusion

Predictive modeling in recruitment will help organizations streamline their recruitment process. However, it will not be a self-sufficient mechanism and definitely not a replacement for the human recruiter. Organizations will need to empower and equip the recruitment teams to build a well-thought-out hiring system, which allows them to attract, engage, and retain top candidates.

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