Predictive Analytics In HR: 5 Excellent EXAMPLES You Can Copy From!

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Predictive Analytics In Hr

Analytics is a crucial component of Human Resources (HR) digital transformation. 

Mckinsey research says 70% of company executives consider people analytics a top priority. However, the maturity and capabilities of HR analytics vary widely based on organisations’ size, composition, HR processes, and strategic priorities.

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Read on to know real-life case studies where HR predictive analytics have delivered staggering ROIs for businesses.

Predictive Analytics in HR

Per the 2021 research report titled ‘Evolution of People Analytics‘ by PeopleMatters, 66% of the 126 organisations surveyed used HR data only for reporting and visualisations. These constitute descriptive HR analytics. They offer a clear picture of the current business operations and are handy for monitoring and management.

Another 21% used diagnostic HR analytics. These organisations could perform root cause analysis (RCA) and troubleshoot issues by unearthing patterns in their HR data.

A mere 7% of organisations reported using predictive HR analytics. These organisations could leverage historical data to predict future outcomes and drive data-backed HR strategy decisions.

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Examples of Predictive Analytics in HR

Gartner defines predictive analytics as a data mining approach emphasising prediction rather than classification or clustering, which are descriptive and diagnostic techniques. It also highlights that predictive analytics provides rapid insights relevant to strategic outcomes and is used for decision-making by business users. 

Hr Predictive Analytics
Predictive Analytics In HR: 5 Excellent EXAMPLES You Can Copy From! 1

Read on for some real-life examples of how HR predictive analytics works.


Xerox Corp. Reduces Employee Attrition

According to the Wall Street Journal, Xerox Corp. used predictive analytics to enhance the hiring process for 48,700+ jobs in its call centre. It was struggling with high attrition. Many call centre workers quit soon after being trained. 

Xerox could not recoup its training costs of $5000 per employee often. Personality test data revealed that work experience did not have much of an impact on churn. However, personality traits such as curiosity and inquisitiveness significantly increased the chances of employees quitting early. 

Xerox Corp. modified its hiring process to ignore work experience and focus on personality tests for choosing among candidates. They implemented this process permanently after a half-year trial that reduced attrition by 20%.


E.ON Minimises Employee Absenteeism

The Strategic Workforce Analytics report by the Corporate Research Forum describes how energy company E.ON tackled absenteeism with predictive HR analytics. They tested multiple hypotheses and concluded that the length and timing of vacations directly impacted the likelihood of employees taking unscheduled time off due to sickness. 

The model predicted that taking one long holiday a year along with a few short breaks was the ideal combination for reducing the probability of absenteeism. It guided the organisation’s leave policies and enabled managers to handle holiday requests better.


Wikipedia Retains Editors Better

Wikipedia is one of the largest user-contributed systems in the world, boasting more than 100,000 active editors contributing at astonishing speeds of more than two edits every second. Wikipedia collects multiple editor data points like age, education, motivations, editing activities, writing styles, and reasons for leaving. 

Researchers have proposed a predictive model that studies patterns in editing behaviour and learns from existing data. This model can accurately predict which users are likely to become inactive and stop contributing to the encyclopedia. Wikipedia uses this model to appreciate and re-engage these Wikipedians preemptively.


Google Streamlines Hiring Process

Google used to put candidates through 15 to 25 rounds of interviews and assessments before selection. This process was so time-consuming that it took 125 full-time recruiters to hire 1000 employees. 

Analysis of the recruitment process revealed that four interviews were enough to predict with 86% confidence whether a candidate was worthy of an offer. Further interview rounds contributed to just a 1% higher predictive power. With this analytical insight, Google limited the number of interviews per candidate and slashed the median hiring time by 75%, from 180 days to just 47 days.


Credit Suisse Lowers Employee Turnover Rates

Credit Suisse, one of the largest financial services companies in the world, has implemented predictive analytics for identifying employees at flight risk. The HR analytics team analysed 40+ variables, comparing employees who quit and those who stayed. 

Their predictive model zeroed in on 10 to 11 parameters like team size, manager’s performance rating, promotions, life events, and demographic variables that accurately predicted the probability of an employee leaving the company in one year. 

Based on these insights, they trained their managers to engage and retain high performers who were likely to leave. This program saved around $70 million every year for the bank.


How is Predictive Analytics Used in HR?

Predictive people analytics can find applications in several HR use cases, such as talent acquisition, employee engagement, performance management, and workforce planning. The foremost requirement for HR analytics is accurate and relevant data. 

An automated HR Management System (HRMS) helps capture employee data in a centralised database that you can leverage for predictive analytics. Chief data sources include:

  • Survey data – includes employee satisfaction surveys, feedback surveys about HR initiatives, etc.
  • 360-degree appraisal data – includes manager ratings, team feedback, customer feedback, performance ratings, etc.
  • Hiring assessment data – includes job application data, recruiter feedback, test scores, interview comments, etc.
  • Lifecycle data – includes promotion history, loans, past increments, family events, training, certifications, etc.

All these data points analysed against the desired past and present business outcomes help build predictive models.

Predictive Analytics for Recruitment

The recruitment process can leverage predictive data analytics by analysing applicant profiles and personality test data against historical data about existing employees who excel at the desired skills or business requirements. It can also leverage past recruitment process data and results. 

Predictive models can help recruiters:

  • Hire employees likely to stay longer with the organisation
  • Hire employees likely to be most aligned with the job descriptions
  • Avoid hiring toxic employees based on personality tests
  • Predict drop-off candidates and rates in the recruitment process
  • Predict the skills and competencies that the organisation will require in the future
  • Predict optimum compensation and benefit plans that increase the probability of offer acceptance


Predictive Analytics for Controlling Employee Attrition

HR departments have been collecting reasons for quitting in exit interviews for quite a long time. However, predictive analytics can help course correction before the employee takes the exit decision. It helps in:

  • Predicting attrition due to lack of manager effectiveness by analysing manager feedback, tenure, ratings, etc.
  • Increasing engagement by analysing employee skills, interests, and personality traits to predict assignments most suited for them
  • Predicting attrition due to unmet learning and development needs 
  • Avoiding regretted attrition by enabling managers to identify likely exits of their most valuable employees
  • Understanding competitor hiring trends and their impact on attrition

Benefits of Using Predictive Analytics in HR

Why Predictive Analytics In Hr
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Cost-Saving

Predictive analytics can boost your enterprise’s bottom line by saving hiring and onboarding costs by enabling managers to retain top-performing talent that is likely to churn.

  • Avoiding loss of billable hours due to absenteeism or unscheduled offs
  • Cutting recruitment costs by streamlining hiring for the best job-employee fit and high offer acceptance rate


Data-driven HR Decision Making

Predictive analytics helps minimise the influence of biases and subjective judgments on crucial HR functions such as recruitment and performance appraisals. The higher management can predict the impact of various variables on the business outcomes of interest and take a balanced view. It fosters an outcome-based strategic outlook among HR professionals. 


Accurate Workforce Planning

The ‘Analytics-driven talent strategy’ report by Gartner reveals that 60% of heads of recruiting have difficulty acquiring talent to support a change in strategy. 

Predictive analytics helps recruit and train the right resources by predicting the future skill demands of your organisation. It also helps in succession planning by predicting the most suitable replacements for management roles.


Future of HR Analytics

Predictive analytics uses big data and statistical techniques to predict outcomes based on past data. However, these predictions may go awry in disruptive scenarios such as the COVID-19 pandemic. 

Organisations will continue to mature toward prescriptive HR analytics in the future. Just as predictive analytics helps predict what can happen, prescriptive analytics goes one step further and helps decide the future course of action in each scenario.

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Tarulika Jain

Hi.. I am Tarulika Jain. I work as a content writer in HROne. I am an Electrical Engineer by degree, and a pragmatic writer by profession. I bring you insights about Human Resource Management and how digitalisation can make your workplaces happier. When I’m not writing, researching and reading, I walk around the woods, sip my coffee and listen to Indie Music.

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