4 Ways Predictive Analytics Increases Employee Retention

An employee leaves: You hire and onboard their replacements, spend months on training only for them to leave again in a year (40% do), and start the process all over again. Employee turnover is incredibly costly for employers. Retaining your top talent by improving employee retention has been proven as a cost-saving measure.

Unfortunately, it isn’t always clear where to start. How can you identify where your efforts will have the most impact? How can you anticipate when someone will leave and, more importantly, what makes them want to stay? How do you keep employees from leaving?

Predictive Analytics can address all of the above.

PREDICTIVEHR’s Predictive Analytics using true AI allows you to identify pain points within your team and point out indications that an employee is likely to leave their position. While every employee is different and their reasons for leaving can vary, Predictive Analytics can help reveal those factors, flag employees for intervention, and implement effective solutions to develop stronger employee connections.

Our industry experts can help you answer the question everyone is asking:

“How do I keep employees from leaving?”

[bctt tweet=”Employee turnover is expensive. Retain your top talent and save those hiring dollars by improving employee retention through Predictive Analytics. PREDICTIVEHR (@TalentData) shows you how: ” via=”no”]

Collect the Data

A good starting point is to address ‘big picture’ trends. The Work Institute’s 2020 Retention Report states 40% of employees who leave a business do so within their first year. That’s a really good piece of information that should motivate you to create a quality onboarding process for your new hires. But we’ll also dive much deeper.

Keep in mind, you already have a plethora of information on your own employees. You’d be amazed at how much data you already have on their likelihood to stay or go. This doesn’t usually require an interrogation. Predictive Analytics begins dissecting information like:

• Used sick time and vacation days
• The current length of employment
• Changes to their home life
• Their average commute time
• Previous promotion and compensation timelines
• Server log-in times
• Continuing education efforts
• Evaluation of work/life balance

PREDICTIVEHR shares more insight on what your data can do for you: Unraveling Your Data.

[bctt tweet=”Predictive Analytics helps aid in employee retention by: Collecting the data Rating the criteria that contribute to turnover Flagging high-risk employees Implementing interventions To learn more about #PredictiveAnalytics and #EmployeeRetention, click here: Via @TalentData)” via=”no”]

Rate Criteria Contributing to Turnover

This is the analysis point which isn’t a one-time process. Predictive Analytics uses AI to identify the most significant contributors to employee turnover. By simply inputting the data you’ve already obtained on your employees, the model mines the data, identifies your specific risk factors, and produces a risk score for each employee.

Some of the expected contributors could be unsurprising. Such identifiers like pay, performance review scores, and work relationships are usually the most obvious. But predictive analytics can also weigh these factors. On their own, these considerations may not be a significant risk. However, when combining factors of a certain weight, the risk can become glaringly obvious.

Predictive modeling also uncovers factors contributing to an employee’s longevity within a company. We’ll help you identify exactly what makes your employees stay with your company or leave it, rather than providing broad generalizations of an entire industry or market.

These insights can be applied to every employee to increase your success in retention, as well as to new hires as they’re onboarded. This step is ongoing and can help in constantly improving your organization.

Flag High-Risk Employees

Now that each employee has their risk score, we’re going to flag those considered to be a high risk for resignation. These are usually employees exhibiting multiple highly weighted risk factors, as there’s usually not a single stressor contributing to turnover.

Going forward, while you adjust and input employee information, once a risk score rises above a designated threshold, your model will also flag those employees for monitoring.

What flagging means in this situation is a visualization of that employee. This ensures your team has access to the information necessary to move forward, in an actionable format. Our dashboard view is customizable depending on your needs and easily shared with relevant members of your organization.

Read More: 3 Key Considerations for Successful Use of Data & People Analytics

Implementation of Interventions

Once you’re able to identify those employees who pose a risk of resignation, you’re able to move forward with intentional retention efforts.

Depending on the employee’s highest risk factors, this could be simple. Sometimes a conversation with a manager or a discussion of growth opportunities in the company is all it takes to lower that high-risk score. Other times, the answer might be to shorten the timeline on the employees’ next promotion or salary increase. Perhaps the data points to a larger conversation around the structure or culture in your company.

These conversations can work to uncover true opportunities to motivate change. The bottom line is that you have the objective data to uncover where to start and what to address.

What’s more, our data-driven approach also allows you to track the success of your interventions over time, which will continue to improve the way you address such opportunities.

Ultimately, it takes action on your part to resolve many of the root causes of employee turnover. But predictive analytics works to identify the causes and equip your team with the insights necessary to make profound changes.

To protect your top talent from turnover, contact PREDICTIVEHR today.