People analytics is all about the discovery, interpretation, and communication of workforce-related insights – and right now, one area of concern for which companies need actionable, data-driven insights is the “Great Resignation.”
Recently, Adam Kabins, senior director of talent management for McLane Company, joined Perceptyx’ global head of research Brett Wells and people analytics consultant David Weisser in a webinar focused on the use of people analytics to encourage retention by predicting and reducing attrition.
When modeling turnover, the primary challenge involves building a foundation for the model – a foundation that has to rest on the right tracking metrics. Not surprisingly, many organizations struggle when determining what should be tracked.
“It’s really important to standardize which metrics should be tracked, using different streams of data from HR along with operational and financial data to uncover the true costs of someone leaving the organization,” explained Brett Wells. “It’s somewhat easier to measure the direct costs here, because someone from HR is telling you how much talent marketing and training cost. The indirect costs are oftentimes more expensive than the direct costs, and proportionally harder to measure, but reaching out to organizational stakeholders outside HR for more data can establish confidence and credibility in the modeling while improving the cost modeling and overall strength of the analytics.”
The actual cost of replacing an employee, Wells noted, can prove to be far higher than estimated solely by direct costs such as separation processing, payout of accrued benefits, replacement costs during the employee’s vacancy, administration of the recruitment process, and selection and onboarding of replacement employees. This number is increased by the indirect costs of employee attrition, including lost productivity by the incumbent employee prior to departure, lost productivity while the position is open and supported by other employees, and lost productivity during the replacement employee’s onboarding process.
“These costs are too complex to compress to a single slide or bar chart,” said Wells. “We need to use inferential methods like machine learning models that are designed to accommodate large amounts of data from disparate sources that can give us lots of insight, because this allows us to control for factors like job attributes.”
The resulting model, Wells said, has to account for the fact that, in situations where a company has only a 10% turnover rate, someone could simply guess that employees would stay in their roles and be right most of the time. “We have to manufacture a balanced playing field so that the analyst can’t cheat and so that we can really derive the risk factors associated with attrition. We can assign these risk factors to individuals throughout an organization, then we can determine if the factors are more prevalent in particular areas of the organization.” Tools such as Tableau or Power BI can then visualize these risk factors via dashboards, empowering organizational decision makers with the actionable insights needed to quickly understand what levers they can pull to influence attrition.
Like other companies in the logistics and supply chain sector, in which revenues are high but profit margins quite low, McLane faced challenges as the voluntary turnover rate in the U.S. climbed steadily from April 2020 until reaching an all-time high of 3% in September 2021 – a period referred to as the “Great Resignation” that saw 2021 come to a close with roughly 11 million jobs open across the country.
“Right now, there’s about 50,000 openings over and above the number of [CDL-licensed] truck drivers that are available, and over the next few years that will expand to about 80,000, so in terms of our industry, this is probably the most challenging environment that has ever existed,” said McLane’s Adam Kabins.
Reflecting on some of his prior professional roles, Kabins recognized that retaining hourly talent of the sort that comprises McLane’s trucking fleet was vastly different than retaining more knowledge-based workers. “With a lot of organizations I’ve worked for in the past, there was a mismatch in terms of the expectations of the workforce, where many organizations were looking for lifers,” Kabins said. “They were looking for people who were going to come in and work for 10 to 20 years. If your target is looking for a long-term employee like that, you’re never going to hit it.”
Kabins also realized the hazards of focusing too much on compensation rather than the rest of the employment experience. “People will say we’re not as competitive as we thought we were in terms of pay or benefits, and while that’s important, it isn’t the be-all, end-all to driving retention.”
When McLane sought to determine the keys to success on the attrition front, they turned to Perceptyx. Perceptyx’ ability to gather engagement survey data and exit survey data with the highest degree of trust and confidentiality proved essential to this strategy.
“We needed a third-party partner to help us match different data points together and give us guidance regarding what those analytics meant,” Kabins said. “And we had to maintain confidentiality and employee trust throughout.”
This partnership resulted in the creation of modernized reporting for more accessible turnover dashboards, consistency in the calculation of turnover, and a better understanding of the cost of turnover for use when evaluating investments and interventions. That, in turn, led to the identification and assessment of risk factors to predict and reduce turnover, which could be leveraged for McLane’s strategic initiatives.
More fine-grained insights enabled McLane to craft a precise strategy for dealing with attrition:
The strategy yielded an understanding of attrition that went far beyond mere guesswork rooted in concerns about inadequate compensation. McLane learned that warehouse and driver employees had different turnover rates, as well as where geographic hotspots for attrition were concentrated. The company also discovered that optimism about the company’s future as well as opportunities for career growth were key predictors of turnover, as was supervisor feedback indicating to employees that the company cared about their well-being, performance, and professional development.
With this in mind – and with the data clearly visualized on dashboards easily accessible to leaders and key stakeholders – McLane could make interventions and investments that best addressed the true cost of the turnover they were experiencing. And with new awareness of the value of feedback and optimism, the company chose to reemphasize its employee recognition program to drive improvements in those areas after noting that supervisors who most frequently leveraged the recognition program boasted far greater retention rates than their peers.
“After we found that optimism was an important predictor of retention and reduction of turnover, we started to focus on some of our leadership initiatives and training initiatives, and even some of our communications explaining elements of our business,” Kabins said. “We just didn’t always do the best job of communicating that out. We’re a very humble kind of culture, and so those things can sometimes be not as strongly vocalized at times, but now we’re encouraging our leadership and our supervisors to be more vocal about the strengths of our business.”
To listen to the entire webinar, click here to watch it on-demand. And for more information on how your organization can utilize Perceptyx’ expertise to address retention and attrition issues in your organization, schedule a meeting with a member of our team.