All of these questions should be part of your ongoing people strategy. To find out the best way to make sense of offboarding, I sat down with Glenn Donaldson, Chief Customer Officer and President of the Americas for intelliHR. Through his experience with some of the most innovative HR leaders across the world, Glenn has fed this expertise into intelliHR’s employee offboarding tools and analytics. In our chat, Glenn shared some of what he’s learned with me – these are great insights whether you’re an intelli user, or just trying to understand why people are leaving your business.
“Onboarding and retention are always hard to talk about. There are so many sensitive issues at play, so the key is to take this discussion from reactive to proactive. In today’s agile working environments, it’s about getting strategic with offboarding right now, not just when things go wrong.”
1. Understand your culture and performance
The first and most obvious use for offboarding is to gauge the temperature of your business through the lens of the person leaving.
“As people leave, they often feel more comfortable sharing their thoughts on your performance and culture,” Glenn said.
The key to this step is to ask both the departing employee and their supervisor as much as you can: find out about their role satisfaction, their view on your performance processes, their feedback on career development, goal setting, the business culture, and to share their experience with leaders in the business.
“This reveals key variance gaps,” Glenn said. “If the employee scores themselves as a top performer but their supervisor scores them poorly, maybe there’s an opportunity to investigate whether that discrepancy in expectation and understanding contributed to the turnover. One of the key goals of conducting employee exit surveys is to identify the variance between why employees say they are leaving, and why supervisors say employees are leaving.”
When these differences are identified, we can then move from reactive to predictive. The best way to predict future turnover is by looking at past data.
“It’s more than just looking at percentages, it’s about identifying gaps in the data and breaking them down to be meaningful,” Glenn said.
“AI analytics tools have the ability to really explore these variables – which are almost always invisible. More than that, it lets us tell meaningful stories about our data. If a few employees leave in a row and describe goal setting as poor, but your supervisor says it’s great, maybe the issue lies with the supervisor and your process.”
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2. Assess the employee journey
“A key gap in onboarding actually stems from offboarding. We need to start asking questions about an employee’s journey before they leave, not just when it’s occurring or after,” Glenn told me. “We should be actively asking questions throughout the employee lifecycle: how did you get here? What was your experience like when starting? What were the first few months like?”
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Armed with this knowledge, organizations can then validate exit information and incorporate it into employee retention initiatives. If your employee spends their first two years raving about the support and guidance at your company, but cites this as a reason for leaving, we can investigate. Were they just citing it as a reason to leave because they were headhunted? Perhaps- or maybe there was something they felt uncomfortable disclosing during their employment.
Ascertaining these gaps early on not only helps us reduce turnover by catching problems earlier on, it helps to improve the employee journey for new hires right now, not just in the future.
“The best way to do this is to make sure you’re capturing that feedback through their journey in the same format as that exit survey,” Glenn said. “It’s a lot easier to compare two identical scores for the same question, giving you a view of individual and broader trends throughout your organization.
These changes over time become your employee’s journey. As their role satisfaction changes throughout their tenure, we can identify inflection points where things go wrong. This information can then be used to ensure that new hires have a different journey and experience.
3. Retention rates and cost to hire
“This might sound like old hat advice, but a lot of us still aren’t tracking retention in relation to cost to hire. Every recruitment has a cost, just like every departure has a cost. If someone is leaving within the first 18 months then you’re almost always losing money,” Glenn explained.
The best place to start thinking about retention rates and their impact to your business is recruitment cost versus wage cost. If we can understand how much it costs to source a new employee and their cost of seat, we can reveal a few invisible trends. The most obvious: is one recruitment source consistently costing us more money, with higher turnover? Maybe everyone you hire through a particular platform stays for at least two years, whereas hires from another source leave far earlier.
Equally, it’s important to compare these numbers to in-business factors. Does one business unit experience a turnover rate faster than others? Why is that? Does a particular supervisor have a far higher retention rate than others? Why is that?
Collecting and analyzing something as simple as retention and wage cost with AI tools can help us tell stories about turnover, with data to reinforce these findings. This then helps HR make data-driven strategic choices about where to source our new hires, and understand what we can do to keep them.
4. Rehiring, the black book and your business
“Something that many of us forget is the offboarding journey itself. The second someone resigns we usually shift into hiring mode, but we can forget to make sure their departure is positive,” Glenn pointed out. “Some questions for HR are: how do we ensure that the outgoing employee’s departure experience is positive? How do we gain a rich understanding of why they’re leaving and tune our offboarding procedures to match?”
“Shift your framework. Rehiring turned-over employees is not a dirty practice. If we offboard gracefully, ask the right questions and improve on the reason they left, then we can look at ways of pulling them back into the business down the track.
I asked Glenn about the benefits of re-entry of past employees, and he pointed out the obvious: rehiring past employees involves less training, less upskilling, they already know your business and processes, and arguably most importantly, they also know the culture.
“They also have a ton of new information and experience from other industries, so drawing them back in is a huge opportunity,” Glenn said.
“Equally,” he pointed out, “it’s crucial to know who not to rehire. If HR departments want to stay competitive in the human capital game, we need to have a talent pool of departed employees that we’d love to have back. Businesses change and grow all the time. If I know why a pillar employee has left, and that reason is later solved or improved in my business, I’m going to be picking up the phone and finding out if they’d like their old desk back.”
5. The hidden data: cost, attrition, time to offboard
The final tip Glenn shared was about all the hidden aspects of employee offboarding.
“Imagine you take all of your attrition information and feed it into a great excel spreadsheet and discover you have an attrition rate of 10%. That’s pretty impressive, right? Fairly low, and our cost of seat is relatively low, as we’re running a tight ship,” Glenn said.
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The problem with this approach is that it presents a limited view of the employee experience.
“There are so many attrition stories that aren’t coming out of excel,” Glenn went on to explain. “Now imagine that you’ve imported that attrition information into an analytics engine, along with all of the job and person information about those starters and leavers. The analytics engine agrees that there’s a 10% attrition rate overall, but now you can see that out of that 10% attrition rate, it’s actually comprised of 90% of individuals who identify as female.”
Without the proper tools, we might’ve commended ourselves for that 10% attrition rate while unknowingly having a 90% attrition rate for individuals who identify as female.
“That’s the kind of invisible information that excel and old reporting tools just don’t give you. Using AI-driven analytics engines, like the one underpinning intelli’s insights, helps us get to the bottom of the data,” he told me. “You have to dig past the first level of data. Going deeper identifies areas where you can actually make a difference.”
But to get there you need to be collecting the right data at the right time in the right place.
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