Predictive analytics tools can help HR identify risks, measure employee turnover, and determine when people need assistance. While they were originally available only to large-scale organizations and enterprises, these tools have become more accessible to small and mid-sized businesses. That’s important as organizations shift HR processes and data to cloud tooling, giving you data sources and streams to feed those tools.
Robust predictive modeling relies on establishing data validation processes. You have to know your data is reliable to ensure you feed your models the right information and obtain accurate and valuable results. Once you do, predictive analytics can be applied to various business aspects, from selecting benefits to reducing turnover or offering upskilling and training to boost performance and job satisfaction.
We’ll give you a brief overview of predictive analytics and how it can help your business’s HR department.
What is predictive analytics?
Predictive analytics uses past and present data streams to predict future outcomes. Depending on the algorithm, it can identify patterns, look for similarities in data, and forecast trends based on historical occurrences.
For example, you could input two years of data on employee retention, including performance records and interviews after employees quit, into a program. Then, the algorithm could search for similarities between those profiles and existing employees to reveal what factors are most common in employee attrition and which individuals in your organization are at high risk of churn. This requires a healthy data stream, which entails setting up data collection processes like performance and behavioral analyses that all feed data into the same platform.
These models are also limited without historical data sources. If you rely solely on cloud data collection or human resources management, you might lack the information to build an algorithm tailored to your organization. You may still be able to use industry benchmarks and update the algorithm as you obtain more data, but less information will yield lower quality results.
5 Ways predictive analytics adds value to HR
With rich data streams in place, predictive analytics offers clear insight into your personnel, including their development and skills.
As mentioned in the example above, understanding why people leave can help you highlight high-risk employees. You can also deliver remediation across your organization to prevent those risk factors from developing in the first place.
For instance, a whitepaper from ADP Research used predictive analytics to pinpoint the 40 most common reasons people quit. Researchers then checked that data against different organizations and found their top 10 reasons varied slightly. ADP was able to flag high-risk individuals in each organization’s historical data and found 50% of people labeled as “high risk” by the program had quit. Meanwhile, only 10% of those categorized as “low risk” quit.
Understanding the attrition contributors in your organization lets you take proactive steps to prevent turnover. That can take the form of offering better benefits and pay, more interesting challenges, or expanded opportunities for personal development, depending on the challenges you face.
Predict candidate success in a role
Predictive recruiting uses analytics to compare a candidate’s assessments, qualifications, and skill sets to existing profiles to forecast their success in a new role. Although success takes on different forms depending on the position, predictive hiring can improve decision-making in the interview process. For example, Afni relied on predictive hiring to streamline its automated hiring process by highlighting top talent. This resulted in new hires increasing their achievements for 90-day goals, as well as a 36% improvement in annual employee retention.
Identify candidates for succession pipelines
Skill and competency matrices show you what to look for in leaders and can also map the behaviors, skill sets, and personality traits of existing ones. Predictive analytics can then look for those factors in current employees and new hires — essentially creating a shortlist of people to move into developmental training and succession pipelines.
Once you identify strong candidates, you can invest in professional development through broadening experiences and training. This serves both to assess their desire to move into a leadership role and ensure they have the tools and experience to be successful in those roles if they do.
Assess and fill skills gaps
Talent intelligence, where you use predictive analytics to assess the skills you have, need currently, and will require in the future, is one of the fastest growing branches of predictive analytics. This approach aligns skills and role assessments with skills gap and role analyses to determine what you require now while also forecasting future needs.
This subcategory of predictive analysis requires a strong skill matrix or management platform to map necessary skills to technologies and responsibilities across the organization. Updating that information as new technologies and responsibilities are introduced allows predictive analytics to flag both current and potential skills gaps automatically.
That assessment allows HR to take proactive steps via delivering training and coaching to teach skills that remediate those gaps before they crop up. If training isn’t feasible, it still gives you insight into what new talent you’ll need (and when).
Organizations are increasingly adopting predictive analytics to assess and forecast employee performance. For example, you can measure engagement and participation levels, check past performance data, and combine current data with feedback from team leads and members for a comprehensive overview of each individual. This identifies potential “underperformers” and enables you to take steps to help them improve like offering training and development, understanding why those people are underperforming, and providing coaching or management.
While it’s necessary to have data on past performance metrics, as long as you have enough personnel, you can map employees to profiles and then group them according to their risk factors. This will help you understand more clearly why and when someone falls short of their role’s expectations.
Moreover, if you can predict low performance, you’ll know what solution to offer. In turn, by improving performance before someone receives a bad end-of-year performance review or experiences some other friction, you’ll show that employee the company invests in its people, which can increase employee retention.
Predictive analytics helps identify problems within your organization, raises early warning signs, and gives HR data to make more informed decisions. Yet, it remains an underutilized tool: One study on data analytics implementation in Fortune 1000 companies revealed only 23.9% of organizations consider themselves “data driven” and think they have to improve their use of data analytics when making decisions.
When expanding your reliance on predictive analytics, it’s important to ensure you have processes in place to validate and employ your data well — rather than simply creating more dashboards to manage. By following the recommendations we’ve outlined, your HR department can learn more about your organization’s workforce and maximize it for optimal results.