Today, HR managers have found innovative ways to incorporate analytics in recruitment.
Predictive analytics is a collection of several statistical techniques like predictive modeling, data mining, and machine learning to predict future or otherwise unknown events by analyzing current and historical facts.
According to a report by Markets and Markets, the HR analytics market is expected to grow at 13.% CAGR, reaching 3.6 billion dollars in 2024. The below graph shows the projected market size of HR analytics.
Source: Markets and Markets
Predictive Analytics in Recruitment: Case Studies
As mentioned earlier, predictive analytics has many use cases for modern business, particularly in the fields of HR and recruitment. Many top-level companies have ingrained the best predictive analytics methods into their hiring policies, giving them superior outcomes.
Here are some intriguing case studies and examples from the real business world that have successfully incorporated predictive analytics into their recruitment practices:
1. EON’s Using Predictive Analytics To Reduce Absenteeism
Absenteeism is a major problem for firms and one study reports it can comprise up to 9% of all payroll costs. Naturally, firms are looking for innovative ways to reduce absenteeism and minimize additional costs. EON successfully used high-level predictive analytics to reduce absenteeism. The organization developed 55 hypotheses using predictive analytics to determine the cause of absence, of which they tested 21 and verified 11.
In order to enhance PTO rules, the company discussed with managers its discovery that a lack of long holidays in a year boosted absenteeism. The model suggested that the best strategy for lowering the likelihood of absence was to take one long vacation and a few shorter ones per year. It helped managers better manage vacation requests and shaped the organization’s leave rules.
2. How Xerox Used Predictive Analytics to Reduce Attrition
Xerox Corporation leveraged predictive analytics to reduce attrition by collecting data from personality assessment tests of different employees; based on this, it developed personality trait indicators of good employees. It used these indicators/scores for hiring candidates. This helped Xerox reduce the attrition rate by 20% within six months.
San Francisco analytics startup Evolv helped Xerox reduce call center turnover by gathering and studying data on front-line employees’ characteristics and job performance, then applying what it learned to the hiring process. Evolv found that employees without call center experience were just as successful as those who had it, allowing Xerox to broaden its candidate pool.
The $5,000 per employee training expenses incurred by Xerox were rarely recouped. Data from personality tests showed that work experience had no influence on turnover. However, curiosity and inquisitiveness considerably increased the likelihood of early job departure.
3. Streamlined Operations of LinkedIn Recruiter
LinkedIn’s machine learning research has made significant strides as the firm has access to one of the largest and fastest-growing databases in the world.
LinkedIn Recruiter assists hiring managers and recruiters in streamlining the talent sourcing process and enhancing overall hiring results. A predictive analytics system ranks applicants that meet a particular request when a recruiter requests a candidate search based on the candidates’ work experience, talents, the time and place of the job ad, etc.
Gradient Boosted Decision Trees (GBDTs), among many other sophisticated machine learning techniques, are used by LinkedIn Recruiter to determine specific criteria that might connect a prospective hire with an employer’s priorities.
4. Google’s Mastery of Predictive Analytics
Google is one of the leading tech firms in the world and is known for its progressive company policies. The corporation has adopted high-end predictive analytics methodologies in recruitment and other HR-related functions.
For instance, Google was finding ways to shorten the hiring time and overall recruiting costs. Before selection, Google used to put candidates through 15 to 25 rounds of interviews and tests. It took 125 full-time recruiters to hire 1000 people because this process took so long.
Four interviews were sufficient to predict, with 86% confidence, whether a candidate was deserving of an offer, according to an analysis of the hiring process. Just 1% more predictive power was obtained by conducting additional interview rounds. A study reported that Google reduced the number of interviews per candidate and cut the median hiring time in half, from 180 days to only 47 days, using this analytical insight.
In addition, Google uses HR predictive research to calculate the likelihood that employees will leave the organization. One of Google’s findings is that new sales associates are far more likely to leave the company if they do not receive a promotion within four years.
5. Credit Suisse Reduces Employee Turnover by Using Predictive Analytics
The leader in investment banking, Credit Suisse, used predictive analytics to track employee attrition and pinpoint the causes of individuals’ reluctance to stay. Line managers anonymously received this information to assist them in lowering turnover risk factors and improving talent retention.
Based on these observations, Credit Suisse also trained specific managers on how to keep high-performing workers who were likely to quit. As a result of this program, the bank saved an estimated $70,000,000 annually on hiring and onboarding expenses.
The prediction model developed by Credit Suisse effectively projected the likelihood that an employee will leave the company within a year based on factors such as team size, manager performance ratings, promotions, life events, and demographic data.
6. Experian Defeats Attrition With Predictive Analytics
Experian plc, situated in Dublin, has struggled with a high attrition rate since 2016. Every 1% increase in turnover costs the business close to $3 million. The organization was unable to pinpoint the main reason for the attrition due to manual personnel management techniques.
Experian’s HR professionals decided to use data and technology to inform their HR strategy to gain a deeper understanding of the workforce. Being a data-driven organization, Experian’s HR specialist strategically analyzed employee data using internal predictive modeling technology and developed a novel solution known as predictive workforce analytics.
They estimated flight risk using a predictive model with 200 attributes, such as team size and structure, supervisor performance, and commuting time.
Teams of more than 10 to 12 persons are one example of a risk factor. The analytics team also discovered triggers that increased imminent flight risk, such as when someone moved farther from the office. The company’s global attrition rate dropped by 4% as of 2019, saving millions over the course of two years.
Contact Benchpoint, a healthtech recruitment agency with years of experience in the HR industry, if you’re seeking creative methods to apply predictive analytics in recruitment.
We have a strong track record of collaborating with companies of all sizes and helping them with their HR-related issues. Our aim is to aid our clients with any queries and make sure they have the greatest recruiting experience possible.