Improve Employee Retention with Predictive AnalyticsReading time: 5 minutes
Hiring and onboarding new employees is incredibly costly for employers, especially in today’s competitive environment. On average, businesses spend over $4,000 to hire and train each new employee. Those costs add up! Retaining workers by improving employee retention strategies is one of the most significant cost-saving measures a business can make.
But how can you tell where your efforts to address employee turnover will have the largest impact?
That’s where predictive analytics comes in!
Predict Employee Turnover Risk
Predictive analytics allows you to develop a stronger employee retention strategy by identifying criteria that indicate which of your employees are likely to leave their positions.
While every employee is unique, there are common risk factors that indicate that an employee is likely to quit or perform poorly. Predictive analytics can help you identify those factors, flag employees for intervention, and implement the right solution to keep the employee on board.
These four steps will help you improve employee retention using predictive analytics.
4 Steps to Improve Employee Retention Using Predictive Analytics
Step 1: Compile data
Step 2: Identify criteria that lead to turnover
Step 3: Flag employees who are at risk of quitting
Step 4: Implement the appropriate interventions
Before you can analyze data, you must collect data. But what type of data do you need? And where can you find it?
Begin with larger trends and hone in from there.
For example, the Work Institute’s 2020 Retention Report states that 40% of employees who leave a business do so within their first year. This information can guide your approach to working with a new employee. Investing in a quality onboarding process and offering additional attention during a new hire’s first year is especially important.
On-the-ground data collection might look like:
- Asking managers to track and log interactions with employees
- Conducting surveys on how employees commute to the office each morning
- Compiling performance review scores
- Gathering demographic and career-span metrics
- Identifying (or formalizing) promotion and compensation schedules
It’s also easier than ever to collect digital data on employee behaviors and attitudes. If you offer employee support services through a website, for example, track how often individual employees access those resources.
You likely already have a plethora of employee behavior and attitude data available in your system. You can mine data from employee support services, server log-ins, certifications or continuing education efforts, and more. This information lends to your understanding of how employees work through their days and seek to maintain a healthy work-life balance.
Keep in mind: it’s critical to work with clean data. Without first cleansing your data, your conclusions could end up off the mark. For example, a duplicate data point (such as a double-recording of a meeting with a manager) might lead you to lend a variable twice its actual weight. At Rapid Insight, we strive to make data cleansing an intuitive, easy process for you. Our expert support team ensures that analytics is within reach of anyone with a working knowledge of your organization’s data.
Identify criteria that lead to turnover
With clean and complete data in hand, it’s time for analysis, which you should see as an ongoing process.
Predictive modeling allows you to parse through and identify the most significant contributors to employee turnover. You can build models in Excel, Python, and R (among others), but it’s worth noting that Rapid Insight’s tools make building models incredibly easy. With a single click, Rapid Insight’s Predict generates a model that automatically mines your data, identifies risk factors, and produces an individualized risk score for each employee.
The model will likely identify several familiar and expected contributors. These might include pay, promotion schedule, performance review scores, commute time, and relationships with coworkers and managers. While these factors may seem obvious, data plays a role in confirming whether or not gut instincts are on target.
Additionally, a model can tell you the weight of each of these factors. In other words, it can identify how much attention and importance each risk factor merits. Perhaps a long commute isn’t a significant risk factor on its own, but when combined with a lack of managerial attention, the risk is higher.
Predictive modeling also tends to uncover factors that might surprise you. It could be that a request to meet privately with a manager means an employee is more likely to retain rather than to turn over. A model can help uncover these surprises.
Still other factors may be unique to your company or industry. That’s another benefit of predictive analytics. It helps you identify what makes your employees stay with your company or leave it, rather than giving a broad, generalized understanding.
You can apply these insights to employees who currently exhibit high risk, increasing your chance of retaining them. Just as importantly, you can apply this understanding to future hires. Improving onboarding procedures and intervening when employees start to exhibit risk allows you to address needs before they become urgent.
Consider this step an ongoing, ever-improving process.
Flag employees who are at risk of quitting
Next, you’ll want to create a flagging system to identify employees who demonstrate a high likelihood of leaving the company.
Typically, an employee will have an elevated risk score if they exhibit multiple highly weighted risk factors. It’s often a confluence of factors that lead to turnover rather than a single stressor.
When an employee’s risk score rises above a specified level, your model should flag that employee for monitoring and attention.
Flagging means ensuring that your HR and support staff have access to the information they need in an actionable format.
The best option is a visualization or a dashboard. Many businesses create an entirely new dashboard for risk score, while others incorporate turnover risk as a metric in their existing dashboards. A simple, cloud-based dashboard platform allows for easy access to answers.
Another route is an automated email system, where alerts automatically trigger an email to relevant staff. This system allows for rapid response and is well-suited to businesses that prefer immediate, direct interventions.
Implement the appropriate interventions
Now that you and relevant stakeholders can identify which employees pose a turnover risk, it’s time to plan and implement interventions to retain those employees.
Perhaps the intervention is nothing more than outreach from a manager. A simple “how are things going” conversation can go a long way.
Maybe it’s shortening the time before salary or promotion conversations take place at a given seniority level.
Or perhaps your analysis calls for a more significant shift. Another benefit of the predictive model is that it will jumpstart conversations about the structure and culture of your company. These conversations can motivate change and support suggested initiatives with objective data.
Whatever the necessary intervention may be, your data will help you find it by identifying the employees who need help.
This data-informed approach to improving employee retention also equips you to track the success of your interventions. Over time, you’ll build an ever-increasing understanding of how to retain employees.
Improve Employee Retention with Analytics
Ultimately, it takes executive discretion and a human touch to resolve many of the issues that cause employee turnover.
But predictive analytics takes the grunt work out identifying the roots of the issues causing turnover at your organization. Analytics equips your HR department and managers to take efficient proactive action to retain employees.
If you’re interested in learning more about how Rapid Insight’s analytics tools and second-to-none support can help you improve employee retention, click the button below to schedule a demo!