HR analytics, particularly predictive HR analytics, is a powerful tool that leverages data and machine learning algorithms to understand employee productivity, work patterns, and behaviors. Your organization’s greatest asset is its workforce. Productive employees are foundational to efficient operations. A 2020 Gallup report found that engaged employees lead to a 21% increase in business profits.
However, more recent studies show that just 23% of employees report being engaged at work. This is where predictive HR analytics comes in, offering a solution to boost employee productivity.
Boosting employee productivity should be a priority for all organizations, and this challenge often falls to HR departments. Fortunately, technology can help.
Predictive HR analytics leverages data and machine learning algorithms to understand employee work patterns and behaviors. Prediction models can use this data to deliver actionable insights, empowering HR professionals and business leaders to optimize productivity and performance across the employee lifecycle. Here, we break down the stages of the employee lifecycle and show how advanced analytics may be used to optimize productivity in each, putting you in control.
Recruitment and Onboarding
New job positions take an average of 42 days to fill, and 47% of organizations report struggling with onboarding due to company infrastructure. That’s a lot of wasted time trying to ramp up a new job role. Predictive analysis can be a great recruitment tool to streamline the process.
Predicting candidate success and job performance
Finding the right candidate is the top priority for any job posting. Of course, this is easier said than done, but predictive analytics can help. Using predictive models, HR teams can identify the key attributes and skill sets that have historically correlated with high performance in a given role. Then, they can filter applications to prioritize individuals with those attributes and skills, as well as the preferred job history.
Using internal historical data for similar roles, external data collected from other research sources, and pre-hire assessments from hiring managers and team leads, you can apply a prediction model to any candidate to predict the quality of their future job performance.
Optimizing job postings and candidate sourcing
Just as predictive analytics can help find candidates with the right attributes, it can also use those data and inputs to inform a recruitment strategy. Based on the attributes and skills you’re looking for, statistical models can help HR leaders find the best job boards, marketing channels, and candidate-targeting strategies for the recruitment process. It can even help HR professionals hone the job description language to be as attractive as possible to the best talent.
Identifying effective onboarding practices
The average cost of onboarding and training an employee is over $1,600. That doesn’t even account for lost productivity when it takes a new employee to do their job. Predictive analytics models can assess your current onboarding practices to flag inefficient processes and help you eliminate time-wasting steps or redundant tasks, reassuring business leaders of the potential cost savings.
Predictive analytics models can assess your current onboarding practices to flag inefficient processes such as redundant paperwork, lack of clear role expectations, or delayed access to necessary tools and help you eliminate time-wasting steps or redundant tasks. If aspects of your onboarding increase the time-to-productivity for new hires, prediction models can help HR teams streamline them or eliminate them entirely.
Learning and Development
Hiring is expensive. Efficient organizations recognize the potential within their ranks and invest in learning and development to upskill existing employees rather than hire new ones. Investing in organizational talent can save money, increase employee engagement, and reduce employee turnover.
Predicting skill gaps and training needs
Your organization will require talent with different skills and expertise as it grows and evolves. A predictive analytics framework can help identify emerging skill gaps and training needs based on planned changes to your organization.
For instance, if your organization plans to build an app in the coming year, you’ll need programmers and engineers with mobile development expertise, product designers, and more specific talent. Using performance data and business objectives, predictive analytics can identify the skills and knowledge areas that will be critical for achieving this goal and identify suitable candidates within your organization who could develop these skills.
These deeper insights will help your organization make smart learning and development investments in your best talent, showing a belief in them that should pay off in increased engagement, productivity, and employee retention.
Human resources teams that can both search for the best external talent and cultivate potential in long-time employees can, as they say, have their cake and eat it, too.
Personalizing learning recommendations
Professional development programs are a great way to garner loyalty and improve employee performance. A predictive analytics program doesn’t have to be purely for organizational use. Individual employees can use predictive models to create personalized learning recommendations based on their roles, skills, interests, and career goals.
Likewise, business leaders can assess employee performance and goals to provide their own learning recommendations during performance review cycles.
Humans want to feel seen by their employers. Investing in an employee’s learning shows a belief in their potential and is a powerful tool for increasing engagement and productivity while keeping that developed knowledge within the organization.
Identifying optimal training modalities and formats
Different people learn in different ways. Employee training and professional development programs are most effective when they can serve several employees at the same time. By leveraging predictive analytics, HR teams can determine the most effective training delivery methods for different types of content and employee segments, then make informed decisions on training.
For instance, basic compliance training for new employees could be done online, in an employee’s own time. On the other hand, specific career development opportunities like an SQL training seminar offered by the data team lead may be better offered in person, at lunch, or first thing in the morning.
Statistical models gathered from training programs and other onboarding and educational content to determine the best ways to serve different types of training.
Performance Management
Being able to see the future is a superpower. While predictive analytics doesn’t go that far, they can provide powerful insights into predicting your best and most at-risk talent.
Predicting high performers and underperformance risks
Utilizing data points like projects completed, time worked, idle time, and more, prediction models can accurately predict future employee performance. Predictive models quickly flag key characteristics and behaviors of high performers within your organization and early warning signs of underperformance.
This data informs business leaders who is working hard and worth investing in and who may need an extra push. More importantly, analytics models can suss out quiet quitters and potential insider threats who already have one foot out the door to their next job. Predictive analytics can identify employees who may be disengaged or considering leaving the company by analyzing factors such as decreased productivity, increased idle time, or disgruntled employees.
Optimizing performance feedback and check-ins
Feedback and check-ins can be extremely valuable in motivating and keeping employees engaged. Predictive analytics solutions help determine the optimal performance feedback frequency and format for different teams. While some teams and individuals may need project-specific feedback, others may benefit from a little less oversight and only quarterly check-ins.
Predictive analytics can help HR teams and team leaders organize feedback cycles based on employee performance levels, departments, roles, and other segments.
Identifying key drivers of engagement and productivity
Naturally, you want all employees to be high performers. While it may be a bit of a pipedream, predictive analytics can at least help uncover specific factors that have the greatest impact on engagement and productivity for different roles, teams, and departments within your organization.
Using insights from annual engagement surveys, training programs, and more sources, team leaders can develop specific strategies for incentivizing and motivating individuals and identify flight risks. Managers, directors, and VPs can glean actionable insights to develop targeted interventions and management strategies to motivate their teams more effectively.
Career Pathing and Internal Mobility
Nearly 90% of companies agree that employee retention is essential to their business strategy. Long-time employees have deeper company knowledge and skill sets uniquely suited to supporting organizational performance. Predictive analytics can help HR teams retain employees and ensure their knowledge remains an asset.
Predicting success likelihood in new roles or departments
As we mentioned, hiring from within or upskilling existing employees is usually more cost-effective than hiring for a new role. Whether you’re in the early stages of building a new department or creating a new role, predictive analytics can assess current employees’ skills, experience, and performance data to identify internal candidates likely to succeed in this new role or department.
Transitioning or promoting a high performer is less expensive than hiring someone new to address a skills gap and has the added benefit of increasing that employee’s engagement.
Identifying optimal career paths and development opportunities
Retaining the best talent is always a priority for HR teams. Predictive models can help HR and business leaders plan how to keep talent for the long term by creating personalized career paths and development recommendations.
Using data about individuals’ skills, interests, and growth potential, prediction models can lay out an optimal path to support an employee’s growth into an invaluable asset for the organization.
Analyzing the Impact of Internal Mobility
Internal mobility is a natural course of any organizational lifecycle. Employees leave, grow, and teams and priorities shift—the only constant in a modern organization is change. Predictive analytics can help manage and measure the impact of that change by predicting key potential outcomes relating to productivity, retention, employee dissatisfaction, and more metrics.
By gathering data and producing internal mobility insights, prediction models can help inform your organization’s policies and best practices for a wide range of mobility future outcomes.
Retention and Succession Planning
A Northern Illinois study has found that companies with a good retention rate have quadrupled the profits of companies with poor retention. Retention indicates happier, more productive, more engaged employees, and it’s crucial to have a strong retention and succession program in place.
Predicting employee turnover risk
Predictive analysis identifies key factors that contribute to employee attrition that can ultimately lead to employees leaving. Data on indicators like engagement levels, performance ratings, and demographic factors may all be used to predict employee turnover rates, attrition rates, and potential risks. That way, when you notice certain employee behaviors, your team can proactively work to replace disengaged or departing employees.
Developing targeted retention strategies
Every organization wants to keep its high-performing and high-potential employees. Predictive insights can analyze an employee’s interests, skills, and goals to create personalized retention strategies. Whether it’s tailored development opportunities like paying for an individual’s MBA program, compensation incentives for hitting performance benchmarks, or recognition programs like an employee of the month program, prediction models can help business leaders devise smart solutions to retain top talent.
Identifying future leadership potential
When leaders move on, your organization needs people ready to step up to fill a leadership gap. Predictive analytics tools are useful in identifying the employees most ready to step up in these instances. Assessing performance and engagement data, prediction models can help create a proactive succession plan to prevent productivity disruption when key roles are vacated.
Employee Well-being and Productivity
Employees who are not thriving at work are 61% more likely to experience burnout, which leads to productivity losses and potential employee turnover. Using predictive analytics, HR teams can develop wellness programs and perks that will help improve employee mood and increase productivity.
Predicting the impact of well-being programs on productivity
Well-being programs can help your organization be more active in your employees’ health and work-life balance. Healthier employees are more productive, and predictive models can use data to analyze employee participation in programs, health metrics, and productivity outcomes from your various wellness programs. Using these data-driven insights, HR teams can determine the ROI of different initiatives and decide where to invest budget and time.
Identifying the optimal mix of benefits and perks
Today, many organizations offer total rewards packages that incorporate benefits, perks, and monetary compensation. Workforce analytics can assess the approval rates and productivity of different segments of the workforce based on what benefits and perks they receive. As such, models make it easier to identify the benefits and perks that impact employee engagement and how adjusting benefits and perks packages may yield greater productivity.
Analyzing the relationship between well-being and productivity outcomes
Employees experiencing burnout or other challenges tend to be less productive. However, trends aren’t facts, and your organization will have its own relationship between wellness and productivity. Predictive models can analyze absenteeism, happiness scores, and performance metrics to uncover correlations between well-being and productivity outcomes.
With these valuable insights, HR teams and business leaders may create targeted well-being interventions and support programs, especially for employee segments that are struggling with heavy workloads or long hours. From including therapy in health plan coverage to funding weekly yoga sessions, there are many ways to support employee well-being, but predictive analytics can help determine what works best for your employees.
Leverage HR Analytics with Teramind
Teramind can be used for predictive HR analytics by leveraging its capabilities to monitor and analyze employee behavior. Here’s how you can leverage Teramind:
- Behavioral Monitoring: Teramind can track employee activity such as time spent on tasks, websites visited, applications used, and even keystrokes. This data can provide insights into employee productivity, engagement levels, and work patterns.
- Data Collection and Analysis: Teramind collects extensive data on employee activities, which can be analyzed to identify patterns and trends. For example, it can analyze which employees are consistently productive, which departments may be facing bottlenecks, or which individuals might be at risk of burnout based on their work habits.
- Identifying Trends: By analyzing historical data collected by Teramind, HR departments can identify trends in employee behavior. For instance, they can predict peak productivity times, seasonal variations in workload, or common causes of distractions.
- Risk Management: Teramind can flag behaviors that may indicate potential HR risks, such as data breaches, policy violations, or signs of employee dissatisfaction. By identifying these risks early, HR can intervene proactively.
- Resource Allocation: Predictive analytics with Teramind can help HR departments optimize resource allocation. Based on workload patterns, HR can adjust staffing levels or training programs to align with anticipated needs.
- Compliance Monitoring: Teramind can help ensure compliance with company policies and regulations. Predictive analytics can help identify areas where compliance might be at risk and preemptively address issues.
- Employee Engagement: Teramind can analyze behavioral data to provide insights into employee engagement levels. Predictive analytics can suggest interventions to boost engagement, such as training programs or adjustments to work schedules.
FAQs
What is predictive HR analytics?
Predictive HR analytics uses historical data and statistical algorithms to forecast future workforce trends, employee behaviors, and HR outcomes. It helps organizations make data-driven decisions about hiring, retention, and talent management.
What are the 4 types of HR analytics?
The four types of HR analytics are:
- Descriptive analytics
- Diagnostic analytics
- Predictive analytics
- Prescriptive analytics
What are the different phases of HR analytics or HR predictive modeling?
The phases of HR analytics typically include:
- Data collection
- Data cleaning and preparation
- Data analysis
- Model development
- Interpretation and visualization
- Implementation and action
What is a predictive model for advanced people analytics?
A predictive model for advanced people analytics is a statistical tool that uses historical data to forecast future HR outcomes. Examples include models for predicting employee turnover, identifying high-potential employees, or optimizing recruitment strategies.
Conclusion
A predictive analytics program can help HR teams better serve employees and the organization as a whole. After all, engaged employees are more productive, more likely to stay with the organization longer, and more likely to contribute to positive business outcomes. From talent acquisition to reducing burnout and incentivizing high-performing employees, predictive workforce analytics assist in data-driven decision-making to improve your company culture and employee performance.
