Are you conveying a more subdued emotional tone in your emails to your boss? Have you increased the distance between yourself and your colleagues when chatting around the coffee machine? Today, changing your routines and habits (even in small ways) may peg you as a flight risk.

Don’t worry: If your boss suspects you’re about to quit, they haven’t become a soothsayer. Chances are good they’re being tipped off by HR and a burgeoning practice that uses data analytics and machine-learning algorithms to predict in real time which employees are likely to leave. What’s more, the highly accurate “quit algorithms” can reveal your intentions even before you start accepting calls from recruiters or post your résumé.

Here are the new ways that your boss can identify you as someone who is likely to quit.

Creating an Index

How do pioneering companies predict who’s going to quit? They develop a turnover propensity index or risk profile by collecting and analyzing data from public sources, such as professional profiles, research and surveys to infer an employee’s openness to outside job opportunities and likelihood of leaving.

“For instance, one dataset is tied to biography—including someone’s education, location, industry and position,” explained Ryan Hammond, global director of people analytics for a fast-growing data solutions and storage firm, as well as a former member of the founding team at hiQ Labs.

For example, professionals in the computer gaming, internet and software industries pose the greatest risk of leaving, since they have the highest turnover in tech, according to a recent survey by LinkedIn.

Dr. David Allen, professor of management at Neeley School of Business, TCU and editor of the Journal of Management, identified other factors to develop, test and validate a turnover propensity index score (TPI) for individuals.

Allen also considered numbers of past jobs, employment anniversary and tenure, as well as potential organizational and personal “turnover shocks”—which are events or changes that may cause an employee to rethink their commitment. Examples include management departures, M&A, lawsuits against the company, or the birth of a child. 

Employers also consider the “opportunity structure” around the employee to predict intent, Hammond added. As you might expect, tech pros with sough-after skillsets and educational pedigrees in fast-growing tech hubs tend to receive the most inquiries from recruiters, and therefore are more likely to get poached. (See a list of emerging tech hubs in the Dice 2021 Tech Salary Report).

According to LinkedIn, while user experience designers and data analysts had high demand and equally high turnover, embedded software engineers receive the most InMails per person of any occupation in North America.

Finally (and here’s where it gets a bit creepy), employers may analyze changes in activities, behaviors and communication patterns to identify employees at greater risk of leaving. For example, someone who suddenly reduces their coding activity levels—such as not recommitting or checking in code every day—might represent a higher flight risk. Someone who spends less time on communication and messaging apps may have become disengaged.

A company that utilizes a smart ID badge or employee-tracking system could consider how often you enter and leave the building or whether you’re isolating yourself from your colleagues in order to spot changes in your engagement levels, Allen admitted. Indeed, a 2018 survey by Gartner found that 22 percent of organizations worldwide are monitoring employee-movement data. Your boss might receive updates on a weekly basis, in fact.

Alternatively, they may monitor employees’ email patterns or external social media accounts to see who has one foot out the door. In a Genpact analysis, people who left the company were significantly less engaged in their communications up to six months in advance of their departure.

Exploring the Pros and Cons

Although companies are primarily using “quit algorithms” to proactively eliminate the reasons for job dissatisfaction and spur retention efforts, there’s always a chance that job seekers could be outed or confronted by their boss before they are ready to leave.

Moreover, unless your boss violates employee privacy laws or an internal data protection policy, you may not know what data they are tracking or how it is being used. Just be mindful of your actions, because we all leave digital signals or footprints… whether we like it or not.

“The best practice is to be transparent when it comes to the collection of data because employers don’t want to violate the trust of employees,” Hammond said. “The goal is to raise discussions and questions, not to punish or restrict.”

Nevertheless, a recent study showed that 91 percent of organizations were regularly using basic data-analysis tools in their HR or talent systems; therefore, employees should just assume that they are being tracked or monitored for retention purposes, Allen added.    

“If you are concerned about what data your company is monitoring, the best way to find out is to ask, Allen advised. “Have the conversation.”