How Can Predictive HR Analytics Help Organisations Increase Employee Retention?
Using Attrition and Retention Analytics to Create Proactive Retention Strategies
Businesses are using predictive HR analytics to identify the specific reasons why their employees might be considering leaving, which helps them to create more successful retention strategies.
Every staff member is different, but there are some common factors that might make them want to quit. Attrition and retention analytics can help pinpoint these factors and take a proactive approach to develop solutions to fix damaging employee turnover.
How can HR predictive analytics help companies increase employee retention
Predictive HR analytics helps HR professionals to identify at-risk employees, measure their flight risk, and predict their potential turnover. It also helps HR professionals to create more personalised retention strategies and improve the employee experience. Specifically, attrition and retention analytics helps to improve employee retention by:
- Determining the causes of attrition before retention becomes a problem
- Identifying what employees may be at risk of leaving
- Improving employee experience and engagement
- Helping create better compensation and incentive programs
- Using Machine Learning to spot patterns that you might miss
One of the most common applications of HR predictive analytics is in determining the causes of employee turnover, which can be done by looking at distinct factors like compensation, work/life balance, or job satisfaction. This can help companies determine what they need to do to retain their employees before attrition becomes a problem.
HR predictive analytics uses different methods to identify employees who are at risk of leaving, including analysing their engagement levels with the company and their performance. These methods help HR managers understand what factors have led an employee to become disengaged or dissatisfied with work, and then find ways to solve them.
By using predictive analytics, HR managers can improve engagement by determining what it is that people like and dislike about their jobs, and then using this knowledge to create specific interventions that will deliver more meaningful and engaging employee experiences, thus improving employee engagement.
Focusing on predicting the future of an employee, predictive analytics in HR can help organisations understand if factors such as compensation and incentive programs, hours worked, and location are determinants of employees deciding to quit their jobs. This helps organisations create better incentives for their employees, thus reducing the likelihood that they will leave the company for other opportunities.
HR predictive analytics helps to improve employee retention by using machine learning to spot patterns that you might miss. It does this by applying machine learning algorithms to collected data and developing hypotheses on what may happen in the future based on identified patterns. This can help an organisation understand their workforce, identify any issues, and make better decisions when it comes to recruitment, hiring, and retention.
How to use predictive HR analytics to reduce employee turnover
To achieve the most effective outcomes possible from HR analytics by predicting the probability of employees leaving their jobs in the future, an organisation must take a strategic, step-by-step approach, collecting data to develop a predictive model that will help the organisation to take pre-emptive actions and reduce employee turnover.
The five steps to using predictive HR analytics are as follows:
Step #1: Calculate the base metrics
HR must calculate current and previous base metrics across employee turnover and retention rates. The higher attrition rates rise, the fewer skilled workers an organisation will have to do the work needed. Productivity and quality fall, and it is necessary to figure out how business outcomes are affected by resignation rates.
By collecting and analysing data, an organisation can tweak its retention strategies according to data and not intuition.
Step #2: Collect data
Data must be collected that enables the organisation to accurately calculate metrics that affect employee retention. This data will help you to identify correlations and determine personalised retention interventions for employees who are most at risk of leaving. Key factors to consider include:
- Employee engagement rates
- Retention rate per manager
- Employee lifecycles
- Voluntary and involuntary turnover
For example, an organisation may track and log employee interactions, conduct pulse surveys, compile performance review statistics, identify compensation schedules, and conduct exit interviews.
Step #3: Identify attrition trends (Who is leaving, when, and why)
When an organisation has identified that it has an employee retention issue, it can utilise HR analytics to identify which employees are leaving, and why employees are leaving.
This may be achieved by performing an analysis of resignation data to determine which factors are increasing or decreasing resignations. Is it departmentally biased (perhaps there is an issue with a manager)? Are people leaving because of compensation (you may have failed to keep pace with market salaries)? Is there a pattern of employees leaving after a specific tenure in their jobs (perhaps you don’t provide sufficient learning, development, and career advancement opportunities)?
Step #4: Flag employees who are at risk of leaving
Not all employee resignations are bad, but it is better to reduce high employee turnover rates. Many resignations could be avoided by applying the knowledge you have gained from data collection and analysis to the development of retention strategies. You can focus on those groups of employees who are most at risk, as predicted by this analysis. Every resignation that can be avoided is money saved on hiring and training.
Step #5: Perform a focused intervention
Interventions must be focused on developing strategies that tackle the root causes of employee turnover, and on retaining key employees. By understanding the reasons why people are quitting ─ such as burnout, work relationships, the need for flexibility, compensation, lack of career advancement, etc. ─ an organisation can personalise its retention policies and strategies to department, teams, and individual employees as warranted.
The bottom line
Your organisation can use HR analytics to help identify employee retention issues and develop strategies to reduce employee turnover. The data you collect and analyse will help you focus on the underlying employee retention issues, and to find solutions before they cause real problems.
This data-driven approach removes bias and saves time and money in the HR function, enabling more precise retention strategies to be created and personalised ─ there is no one-size-fits-all solution to retention issues. It will also help to embed the C-suite support that is critical to the effectiveness of employee retention strategy.
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