Algorithms can help with employee retention
Working in tandem with their fellow executives, CFOs have every reason to find the most effective employee retention measures. Advances in data analytics can provide large employers with more sophisticated insights into workers at risk of quitting. And here’s the good news: many companies already have the data they need for this analysis.
Machine learning that taps into the huge data set kept by an HR team can create a model that will accurately predict and identify employees who are about to leave. It then becomes possible to anticipate their behavior and make management better understand why they might leave or what might encourage them to stay.
Such efforts are necessary. Nearly a third (29%) of workers are actively looking for a new job at another company, according to a study by Grant Thornton on the state of work in America conducted in January and February 2022.
Machine learning opportunity
HR datasets typically contain salary history, performance ratings, and disciplinary ratings for each employee up to their date of hire. Machine learning algorithms can also work with information such as whether an employee has applied to an internal job posting, whether they manage people, whether they are critical talent, and whether they have been flagged as a high potential employee.
The model can be adapted to fit the data of a specific company. But the breadth of data that most organizations can gather is at the heart of how this method works.
Overall, the machine learning required is not particularly difficult compared to other AI applications. Marketers have been doing something similar for years. Subscription businesses such as streaming services or mobile phone providers are good examples. These companies are embracing the idea that it’s possible to take customer data and deploy machine learning to: identify customers they might lose, find out what actions will keep them from leaving, and persuade other customers to buy more.
Joint analysisthat marketers use to understand the relative value that customers place on product or service features, can help measure an individual’s sensitivity to changes in benefits and rewards.
Working with Grant Thornton clients, we’ve discovered that a machine learning model can effectively predict which individual employees plan to leave within six months or a year. As part of an engagement for a large federal government agency, machine learning was used to accurately identify those at risk of quitting or retiring early. This was accomplished by reviewing several years of HR Information System (HRIS) data. From there, we proposed proactive measures to extend the seniority of these employees.
Optimization of employee preferences
Once a company has identified a high performer who may be about to leave and decides it wants to keep that person, the next question is what to do – the effort shifts to deploying tools relevant retention.
The goal is to understand how sensitive workers are to specific changes in the value proposition represented by compensation, benefits, and any other set of rewards. A well-designed survey won’t just ask employees what they want directly. Employees usually say more of everything, which is not actionable.
Instead, ask employees to compare the value they perceive in different packages. In one case, we found that if a company increased the monthly car allowance for top employees, they would perceive the value of this benefit to be almost double its cost.
More broadly, listening to employees through surveys makes it possible to analyze a panoply of benefits ranging from health coverage to retirement savings and vacation policy. The analysis is based on the perceived value of the benefit as a fraction or multiple of the actual expense. Joint analysisthat marketers use to understand the relative value that customers place on product or service features, can help measure an individual’s sensitivity to changes in benefits and rewards.
Employee surveys make it easy to conduct effective analyzes of the best benefits options for employers and employees. We can often identify a total rewards set that 70% or 80% of employees believe is better than their previous set, but which costs the employer thousands of dollars less per person per year. By using such tools, an employer who plans to increase spending on rewards and benefits – intensifying the war for talent – can be sure that they are doing so effectively.
Tim Glowa is Director and Leader of Employee Listening and Human Capital Services Offerings at Grant Thornton LLP. Eric Gonzaga is the National Managing Director of Human Capital Services at Grant Thornton LLP.