How to Identify Your Company's Flight Risks

​Human resources can reduce employee attrition and improve engagement when equipped with predictive data analytics that point out where turnover risk is highest.

Flight risk models help identify what types of employee profiles are the most likely to leave the company, enabling HR to make strategic decisions about whom to target for retention and where to invest in development opportunities.

"A flight risk model determines the people characteristics, demographics and attitudes that are most strongly related to whether or not employees voluntarily exit the organization," said Scott Mondore, a data analytics expert and co-founder and managing partner of Strategic Management Decisions, based in Atlanta. "It provides a profile across every job function or seniority level of attributes that exhibit impact on turnover. For example, it compares all the people who have left the organization in the past 12 months against all the people who have stayed over that same time and surfaces the reasons why the people who left did so and why the people who stayed, stayed."

IBM CEO Ginni Rometty recently made a splash when she said that the technology pioneer can now predict with 95 percent accuracy which employees are likely to leave their jobs within six months.

She said that IBM's predictive attrition tool, developed with its Watson artificial intelligence (AI) technology, analyzes thousands of pieces of data to predict employee flight risk and prescribe actions for managers to take to address the underlying issues. The new tech is one of the more high-profile examples of the way traditionally low-tech HR has been investing in data science to assist its decision-making.

Diane Gherson, IBM's chief human resources officer, said that the company had previously tested hypotheses about who might leave but that "the value you get from AI is it doesn't rely on hypotheses being developed in advance—it actually finds the patterns."

"Predictive analytics can be sensitive to things that management may not easily apprehend just by talking with employees or looking at their employee records," said Jason McPherson, chief scientist at employee feedback platform Culture Amp in Melbourne, Australia. "We know that waiting for people to resign and chasing them out the door with a better offer doesn't work, at least not for long. Imagine the money you could save, and the goodwill, productivity and engagement you could retain, if you could see into the future and understand which employees are going to leave and why. Knowing what's driving turnover arms HR professionals and people leaders with the keys to turning turnover around," he said.

[SHRM members-only toolkit: Managing for Employee Retention]

How to Build a Flight Risk Model

A flight risk model takes about a month to develop, according to Mondore, and should result in HR understanding:

• The attitudes that drive turnover.
• What leaders do that can lead to high turnover, and what they do that can lead to low turnover.
• Specific investments needed to drive retention.

The quality of the predictions HR can make ultimately depends on the quality of the data it feeds into the algorithms and machine learning models, explained Toby Roger, lead product manager at Culture Amp.

"Since our founding in 2011, we've collected insights from over 2.5 million employees globally," he said. The data includes behavioral indicators gleaned from employee engagement surveys, onboarding and new-hire feedback, and soon performance management reviews.

Mondore recommended including demographic data like age, gender, marital status, education and tenure; performance metrics and quarterly or annual reviews; engagement survey data; workload; paid-time-off usage; absenteeism; and salary and career growth. Compensation data should be worked into the model to be able to explain to leadership the amount of money at risk if turnover isn't prevented. He added that the problem with collecting information from exit surveys is that it's too far downstream—coming from people too late to save.

Some companies are alerting managers about individuals at risk for attrition, Mondore said, but that's a bad idea, experts agreed. He suggested reporting aggregated data up to the manager or department level.

"We aggregate the data, typically from groups of 15 people or more," Roger said. "Confidentiality is the primary reason for that. It's important for trust and ensuring that we are collecting quality data. When someone feels the information they provide will be disclosed to management, they tend to not respond or give pat responses. And as soon as a manager sees a little red flag on someone's record, they can't get it out of their mind."   

Mondore said unwanted manager reactions could be showing positive or negative bias toward the person, being resentful toward him or her, trying to fire the person, reducing professional development, or going out of the way to induce the person to stay with increased compensation and perks.

Experts agreed that it's also not helpful to analyze or monitor employees' e-mail or external social media accounts.

"Predictive attrition technology works better when the aim is to improve the workplace rather than target individuals," Roger said.

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Flight risk models are a wasted exercise when nothing is done to address the findings, Mondore said. "The data is interesting at a high level, but you must get it in the hands of your frontline leaders and make it actionable. Go to the areas with turnover risk and start setting up stay interviews, talking to your managers and training those managers on retention practices."

Roger added that Culture Amp provides a list of drivers for each group at risk for turnover. "We also provide actions that the organization can take to address or improve those areas. Just telling someone they have a problem is not particularly helpful."  

Gherson said that IBM's program urges managers to intervene with high-potential workers or those with hard-to-find skills but not necessarily with workers across the organization.

"The ones who are in high demand today and high demand tomorrow are going to be the ones we treat with a very high-touch" response, she said.

Quiz: How Can Implementing ATS Software Help Your Business? 

Companies around the world spend billions of dollars annually on applicant tracking tools and technology, and HR is becoming more data-driven than ever. Take this quiz to test your knowledge on how applicant tracking system (ATS) software can help your business: https://www.shrm.org/ResourcesAndTools/tools-and-samples/quiz/Pages/Quiz-How-can-implementing-ATS-software-help-your-business.aspx