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HR's Guide To Predictive Workforce Analytics
The document discusses the importance of predictive workforce analytics in HR, emphasizing its role in identifying retention and recruitment risks by analyzing employee data. It outlines how predictive analytics can help organizations understand employee sentiments and behaviors, thereby improving engagement strategies. Additionally, it highlights the significance of ongoing measurement and adjustments to enhance organizational performance and employee experience.
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HR's Guide To Predictive Workforce Analytics
- 1. HR’S GUIDETO PREDICTIVE WORKFORCEANALYTICS
- 2. Big data, fastdata, business analytics, machine learning, the list goes on and on. Data are in, and they’re here to stay.
- 3. The most importantaspect of data is how they’re used. Using data well can make the difference between good and great organizations, and even the difference between barely surviving and absolutely thriving. That’s why HR analytics is such a hot trend right now.
- 4. If you searchonline for images of “analytic evolution,” you’ll see a variety of charts, graphs, and tables that basically look like the following: Prescriptive Predictive Descriptive
- 5. Descriptive analytics isthe first step in an organization’s analytics journey. How well is this team performing? How engaged is that department? The answers to these questions describe certain qualities. Prescriptive Predictive Descriptive
- 6. Descriptive analytics becomestale after awhile, only taking you so far in understanding your organization... Prescriptive Predictive Descriptive
- 7. To become moreanalytically proactive, we need predictive analytics. Predictive analytics is the use of current or past data to offer insights about unknown events. In other words, we can use known data to offer a roadmap into the unknown. Prescriptive Predictive Descriptive
- 8. Predictive analytics canbe complicated and often requires some background in statistics to set up and calculate. But what is more important is how you approach and think about predictive analytics, even if you’re not a statistician. Prescriptive Predictive Descriptive
- 9. LEVERAGE PREDICTIVE ANALYTICS 2W A Y S T O
- 10. LEVERAGE PREDICTIVE ANALYTICS 2W A Y S T O Identify Retention Risks
- 11. LEVERAGE PREDICTIVE ANALYTICS 2W A Y S T O Identify Retention Risks + Identify Recruitment Risks
- 12. IDENTIFY RETENTION RISKS Thefirst example can help you identify at-risk employees. On this heat map, you can see the perceptual differences between individuals who took the survey and are either still employees or have since termed.
- 13. IDENTIFY RETENTION RISKS Termedemployees had much lower favorability than existing employees across a variety of themes. This indicates that we can be more confident that employees who have lower ratings to engagement surveys are more likely to term within the next 6-12 months.
- 14. IDENTIFY RETENTION RISKS Let’sdig deeper…
- 15. IDENTIFY RETENTION RISKS Thecategory with lowest favorability for termed employees was Feeling Valued. This is where a “predictive analytics mindset” comes into play: we can hypothesize that employees who rate Feeling Valued items lower are more likely to term. Feeling Valued is predictive of employee turnover.
- 16. IDENTIFY RETENTION RISKS Inother words… Employees who don’t feel valued are more likely to leave.
- 17. IDENTIFY RETENTION RISKS TESTTHE HYPOTHESIS Focus on teams or departments with higher-than-average turnover, and potentially implement strategies to boost recognition and help them feel valued. After implementing those strategies, track turnover in those same teams or departments – does turnover decrease, increase, or remain about the same? This constant measurement, tracking, evaluation, and refinement is at the heart of predictive analytics.
- 18. IDENTIFY RETENTION RISKS TAKEIT A STEP FURTHER A lot more questions can emerge from the “existing vs. termed” data. Which survey items have the largest differences between existing and termed employees? Which teams have the most termed employees? How can we use these data to determine at-risk employees? That last question is the most crucial because it emphasizes the predictive analytics mindset, of trying to shine light on the unknown with what is known.
- 19. IDENTIFY RECRUITMENT RISKS Thesecond can help you identify potential recruitment risks. We can see perceptual differences across employee generations here:
- 20. IDENTIFY RECRUITMENT RISKS Let’sdig deeper…
- 21. IDENTIFY RECRUITMENT RISKS Millennialshad much lower favorability than Gen Xers and Baby Boomers across the board. As Millennials become a larger and larger part of the workforce — especially for newer and entry-level positions — recruitment efforts need to take at least some generational differences into account. With Millennials having the lowest favorability in that organization, it may encounter recruitment risks with Millennials, ultimately hurting organizational growth and competitive advantage.
- 22. IDENTIFY RECRUITMENT RISKS TESTTHE HYPOTHESIS Analyze your data to uncover the following questions: Which survey items have the largest differences between Millennials and Baby Boomers? Which teams have the highest proportions of Millennials? How can we use these data to enhance Millennial employees’ perceptions? How can heightened perceptions among Millennial employees be leveraged to make our organization more attractive to Millennial job seekers?
- 23. IDENTIFY RECRUITMENT RISKS Focuson teams or departments with higher millennial population, and potentially implement strategies that cater to their unique needs and wants you identified. After implementing those strategies, track engagement in those same teams or departments – does favorability on items decrease, increase, or remain about the same? TAKE IT A STEP FURTHER
- 25. Having a predictiveanalytics mindset is crucial to determine which HR strategies and initiatives to pursue, and being more informed with engagement survey data can refine those efforts even further. With all that said, it never hurts to have a partner who has a strong background in behavioral statistics, whether inside or outside your organization.
- 26. We didn’t forgetabout prescriptive analytics… Prescriptive Predictive Descriptive
- 27. Prescriptive Predictive Descriptive Prescriptive analytics answerquestions that include would, could, or should, such as “What should we do?” or “What should be done?” These analyses are quite advanced and sophisticated.
- 28. Prescriptive Predictive Descriptive But even so,it’s good to know that after predictive analytics become fully embraced within your organization, there’s yet another step toward better understanding the employee experience and making work better every day.
- 29. Click below tolearn more about our Employee Engagement Survey – and how you can leverage its predictive analytics to make work better every day. Smart Predictive Analytics Start Here. LEARN MORE