Predictive Analytics and Employee Retention: A Winning Combination
Employee surveys are no substitute for predictive analytics for anticipating workforce turnover.
- By Todd Goldman
- April 5, 2019
Modern business relies on positive employee retention. When companies experience high employee turnover, the costs of hiring and training create financial stress. When vital positions remain unfilled for extended periods, current staff become taxed and productivity suffers.
Escalating employee turnover is why it is essential that companies integrate intelligent, intuitive human resource protocols that target the problem. When a company can predict turnover, it can take steps to prevent it.
Many companies leverage internal employee survey data as a way to compile data-based insights into employee welfare. Are employees happy? Just ask them in a survey. Unfortunately, valid employee survey data relies heavily on people to be truthful. Case in point, a Harvard Business Review survey discovered that 58 percent of workers trust strangers more than their bosses. Such distrust of management can cause employee survey information to be unreliable.
The situation worsens when we consider that many companies don’t grasp the reasons why employees flee from positions. According to an OfficeVibe.com survey, 89 percent of employers believe that turnover stems from an employee’s desire to earn more money. However, as the study reveals, only 12 percent of employees leave their jobs over salary. That’s an enormous (and costly) misperception about employee turnover.
Predictive Analytics: Defeating Inadequate Perception-Based Drivers
When our information is compromised, we tend to make poor business decisions. Your company can improve its retention strategies by relying on data analytics that removes human emotion.
Don’t ask the employees how happy they are. Instead, consider the variables that factor into their happiness. The good news is that you already possess important employee data.
To predict employee turnover, consider hard values not associated with a person’s emotions. Instead, look for consistent factors across your enterprise, such as
- Benefits
- Promotions
- Past reviews
- Historical pay
- Sick time used
Your enterprise already has this information. Other factors, such as estimated commute times, can be calculated from address data already in your payroll records.
Once you’ve compiled data, you want to have an analyst relate it to trends. For example, does more or less sick time used correlate to employee turnover? Can an analyst confirm a lack of employee promotions instigates increased turnover?
It is important to allow the data to speak for itself. For example, it may feel intuitive to surmise that employees with more historical promotions are less apt to quit. However, if a history of promotions seems to reveal a trend of employee turnover, promotions (or increased responsibilities) is the issue that deserves further analysis.
Such data shouldn’t signal immediate and drastic companywide change but rather deeper scrutiny into employee promotion logistics.
Leveraging the Data You Already Have
One of the compelling reasons to rely on data analytics rather than employee surveys is that companies already have the information on hand. Employee surveys must be disseminated. Survey completion monitored by management because few employees tend to return a company survey quickly.
If a company wants to use data sets to tell a story, the data sets must be factual. An address and commute time meet this criteria. An employee’s address is not an opinion; it is a fact.
Predictive Analytics and Company Culture
A company can optimize predictive analytics by instilling a culture of data reliance in its management team. When managers understand the predictive analytics that influence their department’s successes and failures, they can react optimally based on that data. A company that trains management to rely on data might eliminate emotional or knee-jerk reactions to unfavorable department trends.
Of course, a company can’t just drop a data-driven agenda on a management team. Instead, it needs to create custom predictive analytics plans for each department. Following that, managers should be trained to interpret the data, and for that you’ll need easily digestible analytics. That’s easier said than done, but the payoff for getting it right is big.
Predicting Employee Turnover Offers Warnings, Solutions
When your company predicts high-risk employee turnover demographics, you increase your enterprise’s ability to address the problem. In other words, you may be able to lessen the turnover percentage by focusing on the specific types of workers likely to resign.
Additionally, your enterprise may invoke strategic changes that treat the issue from a global perspective. If your company can make a widespread change in policy that increases employees’ odds for staying, it automatically reduces turnover costs.
Current data suggests that employees leave their jobs for salary reasons less often than we assume. This means your company may be able to lessen employee turnover without increasing salaries. In such cases, data used in predictive analytics can save your company money on multiple fronts: hiring expenses, training costs, and overall payroll expense.
A company leveraging survey data can use the results to complement predictive analytics. One should not interpret this article as condemnation for employee surveys, rather, a deep dive into the aptitude of in-house analytics.
Predictive Analytics Can Improve Culture, Productivity, and Achievements
Companies rightfully focus on employee retention numbers, but data analytics might also help improve a company’s culture and productivity. For example, if a company concentrates on salary’s influence on employee happiness, it might miss other employee-happiness variables, causing it to miss out on changes which might actually create a better work environment.
When employee predictive analytics becomes company culture, it cuts down on reactionary decisions. It increases the odds of company success by exposing the least costly obstacles to that success.
About the Author
Todd Goldman is the vice president of marketing for Palo Alto, California-based Infoworks.io, a software company that automates the data engineering for BI and machine learning data analytics for companies worldwide. You can contact the company via email.