How to predict when an employee is about to leave your company? Many people say that experience, personal knowledge of the particular person, and “gut feeling” can predict when someone might leave. But what if, rather than relying on subjective measurements, you could use predictive data and analytics to answer this question? Welcome to the world of People Analytics, a digital age solution that could help your organization prevent trade secret theft.
“Now we live on the Internet”
We live in a world of data. It’s the modern gold rush. The last 10 years (or so) have seen a boom in the growth of data analytics – also called Big Data and predictive analytics. Outside of the legal industry, businesses and governments are using data to do incredible things – all fueled by the phenomenal growth of the internet and smartphones, the expansion of internet bandwidth, the growth of social media and the significant decrease in the cost of computer memory combined with a simultaneous and exponential growth in improved computer processing speeds. As one character in the movie “The Social Network” put it, “We lived on farms, then we lived in cities, and now we live on the Internet.” And when you live on the Internet, all the data relating to all our activities are collected and processed.
This exponential growth in data has attracted academia and fueled the growth of the field of data analytics. This field – data analysis – is booming. In its essence, data analysis is the combination of mathematics and social sciences. It is the study of human behavior by numbers. He is looking
not knowing why something happened, but what will happen next.
These data analysis tools are very powerful. Las Vegas casinos make big money, often based on odds that favor the house a few percentage points above 50%. In contrast, an effective data analytics tool can predict things with over 80% probability.
Over the past decade, data analytics has been used to to predict everything from knowing if a customer is pregnant based on her grocery purchases; the likely location of IEDs in Iraq and Afghanistan; which majors are best for students; the location of influenza epidemics based on Internet search queries; an individual’s creditworthiness (based in part on their Facebook friends); when delivery trucks and cars break down; the future performance of a professional athlete; and even financial fraud – before it happens. These are just a few examples, but the list could go on for many (numerical) pages.
Welcome to People Analytics
Some believe that data analytics will have an even greater impact on our lives than the Internet. So, it should come as no surprise that data analytics is making substantial inroads in HR departments. It is a natural evolution. Data analytics is used – according to some estimates – by more than 80% of HR departments. Indeed, the new buzzword in the world of HR is “People Analytics”.
Employee retention is one area where People Analytics can help HR departments. Is it possible to create an analytics tool that helps HR departments predict when an employee may leave? The short answer is yes. These data tools are used to study the factors that impact retention and predict when – from a macro and micro perspective – employees might leave the company.
In the past, employers relied on exit interviews and employee surveys to try to find the key factors that predicted a retention problem. In 2019, Professors Brooks Holtom (Georgetown University) and David Allen (Texas Christian University) decided to take a data-driven approach. In an article for the harvard business review, Holtom and Allen explained how they used predictive analytics and machine learning algorithms to find the key indicators that indicate when an employee is about to quit. They found two key factors.
- The first was “turnover shocks”. These are major events that cause an employee to think about leaving an organization – a merger, major litigation, changes in management, negative public relations events. Rotation shocks can also be personal – birth of a child, death of a spouse, illness, job offer from outside.
- The second factor was “work integration”. This measures how deeply connected employees are to their work. This examines things like their social connections at work, their involvement in the community, and their personal values, interests, and skills.
Based on these factors, Holtom and Allen created a Rollover Propensity Index (TPI) that scored employees based on these factors. Holtom and Allen then looked at various data sources for over 500,000 people and found that their TPI score was very good at predicting which individual employees would leave the company.
The best of worlds
As a trade secret lawyer, this is a fascinating concept. Typically, trade secret litigation begins with a frantic Friday afternoon phone call, with a manager calling his attorney to say that one or more employees have suddenly quit and gone to a competitor (or to start their own Rival company). If the departing employee(s) does not have a covenant, the rush becomes whether the employee took confidential information or trade secrets before quitting. In these cases, carrying out even a preliminary forensic analysis of the relevant sources can take several days or even weeks. Your trade secrets attorney may feel like they’re always catching up, trying to piece together what the employee did to potentially steal confidential information and even trade secrets.
What if you had a head start? What if you have been given advance notice that an employee is leaving? This would be extremely valuable to both companies and their trade secret attorneys. As noted above, these tools are now available, and as researchers iterate and learn, these tools will undoubtedly become more efficient, more commonplace, and more powerful.
4 key considerations before turning to People Analytics
But how could one practically use a tool like this? And what should you do if you think an employee is about to walk away with your trade secrets?
The first step is to respect employee privacy. As People Analytics has grown, employees have become more aware of what their employers are doing for workplace monitoring. Workplace surveillance is subject to a wide variety of federal and state laws. In Europe, there are strict laws against certain types of workplace surveillance. These laws will, in some respects, cross the ocean into the United States. It’s just a matter of time. Employers should always keep in mind the restrictions against workplace surveillance that apply to them.
Second, as employers have become targets of cybercrime, many companies have purchased and used data loss prevention (DLP) software. DLP applications can be very good at identifying when employees are doing things like downloading documents to an external drive or sending documents to a personal email address. If you think an employee is likely to leave, check to see if your company has a DLP program and if it can be set up to identify when employees violate certain rules (like improperly uploading documents).
Third, if you find that an employee is getting hold of your trade secrets, immediately shut down their computer and network access. That doesn’t mean you have to fire them right away. But cut off their access and talk to them. See what happens. There could always be an innocent explanation for their conduct. But until you are comfortable with the explanation, cut off their access to your digital assets, especially all confidential information and trade secrets.
Fourth, if the employee confirms that he plans to leave, draw up a separation agreement when the employee promises (or reaffirms) their obligation to return all company property before leaving, including physical devices and all electronic information. The latter is especially important in this era of COVID-19 where many employees are working from home. Much of the “stealing” of trade secrets today is actually improper “keeping” of trade secrets and confidential information that exists in home offices and personal devices, email accounts and other cloud-based storage accounts. It is therefore imperative that you specifically ask the departing employee to return all devices and information before leaving. And it is equally important that you do this in writing and that you receive written confirmation from the employee that he has complied with these obligations.
Many of these practical measures are neither new nor revolutionary. But what is new is the impact People Analytics will have on HR departments. It is prudent for outside and inside attorneys to understand these developments and learn the risks and benefits of using these data-driven tools in the near future.