Old Job versus New JobThe use of data analytics could help you identify who might be prime for the picking for your recruitment efforts. The next time you cold call a prospect, consider asking them questions that could help them recognize the need to move onto greener pastures.

A recent Wall Street Journal article entitled “The Algorithm That Tells the Boss Who Might Quit” focuses on how employers can spot which employees might be quitting and how to stop that. After all, it’s expensive to train new employees, so it’s always cheaper to keep the ones you have.

And just how expensive is it to lose employees? According to the article, “The median cost of turnover for most jobs is about 21 percent of an employee’s annual salary, according to the Center for American Progress, a liberal-leaning think tank. And it can cost, on average, some $3,341 to hire a new employee, according to the Society for Human Resource Management.”

The article found those involved in big data analytics can work with human resources to identify flight risks. Factors like length of time spent in a job, performance reviews, employee surveys, and even how good a supervisor is at his or her job are all considered when determining an employee’s possible wanderlust.

There article includes an interesting chart, based on Labor Department statistics, that shows the median number of years employees spend in various occupations. Here’s how it breaks down:

  • Management – 6.9 years
  • Education/Library – 6.2 years
  • Legal – 5.4 years
  • Health care provider – 5.2 years
  • Production – 5.2 years
  • Business/Financial – 5.0 years
  • Computer/Math – 5.0 years
  • Construction – 3.7 years
  • Sales – 3.4 years

Armed with that data, the next time you are recruiting a prospect, ask them how long they have been with their current employer. Or, you can always mine information on LinkedIn to see who might be ready to move on after spending a long time in the same role.

Here’s another way to quickly determine if someone is a good candidate for recruitment to a new job. Ask how often they interact with other employees and/or attend work-related events that aren’t mandatory. Gauge how interested they seem. Do they seem discontented? Use that to your advantage.

This information could also prove helpful in job interviews. Sure, it’s been drummed into people’s heads not to be negative, and negative candidates can usually be dismissed from consideration. However, if a candidate was once positive about what their former employer did, but has since soured, that doesn’t necessarily mean they will be a negative influence in your workplace. A bad supervisor could be the source of their ennui.

Another class of candidate to seek out is employees who have relocated. The data doesn’t show why this is the case, but apparently, workers who have moved are more likely to move on to new jobs. An unscientific read of that would suggest these workers are already more mobile. Without roots in a particular area, it’s easier to keep on moving down the road.

Data analytics are still an imperfect science. You’re not going to perfectly buttonhole in advance which candidates are likely to respond to your recruitment offers. But knowing what the statistics show could help you winnow down an unwieldy amount of possible candidates to a more manageable number.



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