I count myself blessed to have dropped out of “corporate” before the rage for all sorts of employee analytics began.
Oh, sure, back in the day we had HR departments, but there never seemed to be much of a scientific overlay to what they were doing.
Sometimes they’d come around and tell all the managers that they had to do annual reviews; sometimes the managers ignored them. (I went years during my career when there was no such thing as a review.)
Sometimes reviews were tied to salary increases. Other times, they weren’t. Sometimes managers rated employees the way they were told to, sometimes they did what they damn well pleased.
In one classic example, the managers in my company were forced – after years of doing nothing – to give everyone a performance review. Managers were told that they should be very parsimonious about giving high ratings. The vast majority of employees, they were instructed, should receive a “3” on a 5-point scale. If there was someone really terrible in a group, they could be given a “4” or a “5”, but “2’s” should be scarce. And no employee in the history of mankind could ever be given a “1” for uber excellence.
It almost goes without saying that my manager followed orders from headquarters, and gave us all a “3” rating. Other managers Lake Wobegone’d their folks and gave out “1’s” and “2’s” like M&M’s. Which mattered because, come raise time, “1’s” and “2’s” got better raises.
Overall, in most places I worked, the evaluation, promotion, and pay increase processes were haphazard, at best.
But although much of my career was spent in companies trying to figure out who and how many to lay off, there were also occasional schemes to try to retain key employees.
It was all completely arbitrary, and generally came down to whether your manager liked you or not.
Sometimes the retention initiatives meant getting to go to training, sometimes it meant a bonus, sometimes it meant a raise.
My favorite reward was a week long trip to Hawaii.
What happened was that my company had booked an expensive resort for its sales winners’ reward trip, but ended up with not enough sales guys making quota. So they picked a couple of dozen non-sales folks to fill in the already paid for slots.
Great trip, but did it help retain me?
Nope. A year later, I was begging to be put on the layoff list, which actually took some maneuvering, as I’d just been one of the anointed who’d been sent off to a weeklong mini-MBA program at Babson College. There, we watched films of Jack Welch in action and worked on a company strategy that no one was ever going to implement. But being part of this mini-MBA initiative meant you were exempt from layoffs.
Fortunately, the need to fill the layoff lists was so great that, with the support of three VP friends, I was able to weasel my way on to it.
Of course, after that layoff round, the halo around the Babson Fifty had lost its shine, and they were booting my fellow strategists out left and right. I’m sure I would have eventually been given the heave-ho.
So I really have near-zero experience in companies that tried to be at all scientific about how employees were rated and retained.
But I suspect that if I’d stuck with corporate a while longer, the analytics would have caught up with me and I would have been contributed data that would enable analysts to scientifically rate members of my team, and, in turn, been scientifically measured by my manager. (Weighed-and-found-wantings, all around, I’m sure.)
Anyway, there are now analytics packages that predict which of your employees is likely to leave. Given how costly it is to train an employee, let alone go through the trouble of hiring and training their replacements, this has become a big expensive deal.
Corporate data crunchers play with dozens of factors, which may include job tenure, geography, performance reviews, employee surveys, communication patterns and even personality tests to identify flight risks, a term human-resources departments sometimes use for people likely to leave.
The data often reveal a complex picture of what motivates workers to stay—and what causes them to look elsewhere.
At Box, for example, a worker’s pay or relationship with his boss matters far less than how connected the worker feels to his team, according to an analysis from human-resources analytics firm Culture Amp. At Credit Suisse, managers’ performance and team size turn out to be surprisingly powerful influences, with a spike in attrition among employees working on large teams with low-rated managers. (Source: Wall Street Journal)
Interesting that, at Box, pay doesn’t matter. (Hah!) But does it take an analytics expert to determine that a crappy manager is a “surprisingly strong influence.”
And what might companies do with all this data?
Sure, they want to retain the folks they want to retain.
But do not organizations also want some people to go without their having to lay them off, give them severance, and incur the costs of them collecting unemployment benefits.
I can just see the HR-ers: let’s put these three guys on a large team with a lousy manager. They’ll be gone in no time, and we won’t even have to confront them. (Surely, there are few managerial satisfactions the equal of having an employee who’s on some type of performance plan hand in their resignation? Oh, we’re not suppose to say this, but sometimes there is addition by subtraction.)
VoloMetrix Inc., which examines HR data as well as anonymized employee email and calendar data, found that it could predict flight risk up to a year in advance for employees who were spending less time interacting with certain colleagues or attending events beyond required meetings.
Man, forget concerns about employees with flight risks, it gives me the willies to think that someone’s out there collecting data on what colleague interactions people are having, and whether they showed up for the company holiday party.
Glad I’m no longer in this particular game…