Predictive People Analytics: Boost HR Outcomes & Retention

What is predictive people analytics?

Predictive people analytics uses your historical workforce data to forecast individual behavior and segment employees by their actual needs, transforming HR from reactive reporting to proactive, employee-centric strategy. It uses existing data to determine individual behavior, which means that predictions are precise. You can segment your workforce by individual needs and shift from one-size-fits-all programs to targeted interventions that address what actually drives engagement and performance for each employee group.

Key Takeaways

  • Data-Driven Decisions: Predictive analytics uses historical HCM data to move HR from reactive reporting to proactive strategic planning.
  • Retention Forecasting: Models can identify “at-risk” employees up to 6 months in advance, allowing for targeted retention interventions.
  • Quality of Hire: By identifying traits of top performers, HR can optimize candidate sourcing and selection for long-term success.
  • Strategic Impact: Predictive tools enable HR to directly influence business outcomes like revenue, market share, and operational costs.

The challenge for HR and the business

HR teams are now expected to contribute directly to strategic business goals like revenue growth and market expansion. You’re already strong at operational and compliance reporting-but connecting workforce decisions to financial outcomes requires a different approach. The question isn’t whether HR can impact revenue; it’s how to make that connection visible and measurable. The answer lies in HR’s core mission: attract, develop, and retain top talent. The challenge has always been connecting these activities to measurable business outcomes. Predictive analytics changes that by turning your HCM data into forecasts you can act on. Predictive analytics gives you exactly which hiring, development, and retention decisions will move the needle on revenue and performance.

Attract, develop, and retain top talent

Easy? No, but you’ve got new technologies that can help. By applying predictive models to your HCM data, you can answer critical workforce questions that directly impact hiring quality, employee performance, and retention costs.

Hiring

  • Which candidate sources produce the highest quality-of-hire?
  • Which candidates are most likely to succeed? According to SHRM’s 2024 research, 54% of organizations now use pre-employment assessments during hiring to answer this question.
  • How can we reduce time-to-hire?

Development

  • How does training spend affect performance and tenure?
  • Who has promotion potential?
  • How can we increase employee performance?

Retention

  • Which top performers are likely to exit and why?
  • What should we do to prevent exits?
  • What are the best retention options?
By observing traits and behaviors from the past, you can predict what people are likely to do in the future. Let’s start with a question that’s frequently answered using predictive analytics: Achieving Business Goals with Predictive People Analytics 4

How likely are employees to quit in the next six months?

This question is increasingly critical as many U.S. employees are now consistently monitoring the market or actively seeking their next career move.

By answering it, we can learn where people are likely to quit, which critical skills will be affected and how much it will cost to replace the employees-up to 200% of salary for leaders and managers according to Gallup research. We can then forecast alternate outcomes and build effective retention and hiring plans.

To calculate exit risk, the model requires two primary data sets:

  • Applicant Data: Previous employer, education level, and industry tenure.
  • Employee Data: Hire/exit dates, performance ratings, training spend, and compensation history.

Deep dive into statistics

With this historical data in place, the predictive model works through these steps:

  1. Train: Use 80% of historical data to identify correlations between employee traits and exit/stay outcomes.
  2. Verify: Test the model by forecasting exits for the remaining 20% of former employees to ensure accuracy.
  3. Apply: Deploy the validated model to current employee data to identify real-time risks and opportunities.

This analysis shows you how likely each employee is to exit, and which factors are driving those risks. With these insights, you can build targeted retention plans and forecast their impact before you invest resources.

Another common HR question: How can we increase employee performance?

You can answer this by using predictive models to identify the traits that distinguish your top performers from the rest of your workforce. This identifies gaps in development that are limiting performance potential.

How can we hire better candidates who are most likely to succeed in each role?

This is answered in a similar way to predicting employee performance. We use the predictive people analytics model to identify traits in candidates that have resulted in high performance of actual employees. This intelligence helps you hire employees who stay longer and perform at higher levels from day one.

There are many more HR questions you can answer using predictive analytics. Your data’s more useful than you might realize. Ready to put your data to work? Let’s talk about how predictive people analytics can help you achieve your business goals-contact us today.

Frequently Asked Questions
How can predictive models identify at-risk employees up to six months before they decide to quit?
SplashBI predictive models offer a 3-6 month advance view of employee exit risk, enabling targeted retention programs. The engine utilizes 7 algorithms, including Random Forest and Gradient Boosting, to analyze 50+ influencing factors. This proactive approach allows HR to intervene before regrettable turnover occurs, protecting organizational productivity.
How does a diverse workforce impact key business outcomes such as revenue growth, innovation, or employee retention?
A diverse workforce increases profitability by 15% compared to non-diverse organizations according to research. Diversity boosts productivity by fostering creative thinking and reduces employee turnover by cultivating loyalty. Using People Analytics to close diversity gaps ensures an environment that maximizes performance and attracts a broader pool of top talent.
What are the primary data sets required to calculate employee exit risk using predictive analytics?
Calculating exit risk requires integrating diverse data sets including compensation details, performance appraisals, engagement levels, and training records. SplashBI Reports also correlate employee data with customer satisfaction metrics. Healthy data from these reliable sources is essential for the prediction engine to identify active employees at the brink of termination.
What are the three essential steps in developing a predictive people analytics model?
Developing a predictive model involves three essential steps: data input from various people tools, data preparation for analysis, and configuration of influential factor settings.
What is the best way to use predictive analytics to prevent talent attrition?
Predictive analytics prevents attrition by identifying high-risk areas and analyzing turnover trends through the SplashBI Exit Risk module. By correlating compensation structures with exit decisions, organizations achieve proactive churn reduction. Our People Analytics platform enables timely interventions, ensuring critical talent retention and strategic workforce planning for long-term business success.
What is the financial cost of replacing leaders and managers according to recent retention research?
Replacing a Manager costs approximately $133K, while a Director replacement reaches $214K according to data. These high turnover costs underscore the financial impact of losing leadership. Utilizing People Analytics helps mitigate these expenses by providing cost estimates and alarming systems to improve retention across key job groups.