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Workforce Transformation: Here is why people analytics has to be about more than just predicting whe

It can safely be said that once your significant other, whether you are married or just dating, changes their relationship status on their Facebook page from “in a relationship” to “single” it is too late. For the time being you may still hold out hope of re-igniting the relationship but most dispassionate observers would assume the relationship is pretty much over.

Similarly it is also too late if one of your employee’s effectively and metaphorically changes his/her status from “happily employed” to “actively seeking new employment”. But except for concerns around security and productivity I would posit that knowing that someone wants to leave your company isn’t that important. Sure it might be nice to know that an employee that is still collecting a paycheck from you is looking for another job so that you can maybe help them part ways sooner. But once someone takes that step, that mental and emotional leap from being “happily employed” to “actively looking”, it is safe to assume that no matter what you do they have already mentally checked out and it is too late to save the “relationship”.

The problem is that every time I read articles or speak with people about the exciting new world of “people ops” one of the top “features” that always rises to the top is around this seemingly awesome ability to predict when your best talent is about to walk out of the door.

I would argue that talent analytics, people ops, or whatever term everyone will settle on needs to be about much more than this to have lasting value. It needs to go much deeper. It needs to provide insights and provide predictions around what actions or events trigger employee’s to leave so that those actions and events can either be eliminated or their impact reduced. And knowing these trigger points will provide a company with an opportunity to intervene with key employee’s long before they take the emotional and mental leap from being “happily employed” to “actively looking”.

Many of the reasons employee’s leave are obvious and have been obvious for years, long before the advent of big data and analytics, but big data should be able to provide us with deeper insights that are unique. Just a few of the reasons that top employees leave are:

  • Changes in senior management: Do people have faith in the new CEO or head of sales?

  • Changes in pay structure: Analytics aside it is common sense that if your top talent does not get their bonus they will leave.

  • Loss of belief in the company’s future: This could be due to declining revenue, falling market share, decreasing product quality, etc

  • Corporate reorganizations: It is unavoidable that a large reorganization will cause turmoil.

  • Significant process change: Processes and procedures can be burdensome and if they become too overwhelming top employee’s will leave.

Analytics and big data can help provide potentially profound insights into each and every one of these while hopefully uncovering much more. For example, we all know that large reorganizations in a global company lead to some employee’s becoming disengaged and eventually drives them to leave for what they perceive to be greener pastures. Big data can give us greater insight into the “why”. For instance:

  • What corporate communication channels and forms worked and what didn’t?

  • Who are the true influencers at every level of the organization and was their effect positive or negative during the event?

  • Did the reorganization split up teams that had tremendous synergy and strong relationships?

  • What is the overall sentiment across the organization, or for specific groups, or for certain individuals? At what point(s) did sentiment change and what were the specific drivers?

  • And much, much more.

Gaining deep and meaningful insight into questions like these would be game changing for corporate managers. But if the only benefit that predictive analytics ends up providing is a notification that an employee has changed their status from “happily married” to “single” then the promise and hope around the sector will quickly turn to disillusionment.

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