‘Employee Churn’ – HR from a Business Intelligence Point of View

15 Jul 2015

You must have heard the term “customer churn” a dozen times by now, it actually means customers cancelling their contracts or in other terms ‘leaving’. However, this term is mostly used in customer analytics, implying algorithms that is designed to ‘spotting the exact customers who are about to leave soon’. Although algorithms differ, it mainly uses following customer data:

  • Remaining contract time
  • Usage data (declining product usage may imply something wrong is going on)
  • Customer Relations data (Recurring Complaints etc.)
  • Regional Trends (your competitor may be running a campaign in that neighborhood or region etc.)

The list goes on. We follow our customers closely to make sure that they stay with us. We even call them regularly if everything is alright and invest in huge integrated systems called CRM. The question is, how about our employees?

As companies caring so much about our customers, do we really care about our employees as much? Do we track their behaviors/feelings or listen to them on a regular basis so that they feel that they are valued and it is good to be working for us?  I will not be focusing on HRIS systems that let us to keep all employee-related data, instead, I will be asking the question, “how about employee churn?” I mean why not we use business intelligence tools and try to guess “who is about to leave”.

It would be much easier to focus on our disengaged talents if we had a shortlist of employees that are most likely to leave in a specified timespan. We have and may easily use following data:

  • Compensation Package
    • Comparison Ratios using job grades
    • Comparison with similar profiles, two employees may have different grades but may have similar backgrounds, college, degree, graduation year etc.
    • Negative Irregularities, e.g. if you get minimum bonus this year and got maximum bonus last year, your resign possibility is just about to increase.
  • Company Culture
    • If your many teammates are leaving, then you’ve already started to think about leaving, it is just a matter of time.
    • Poor cultural fit, employees whose stress management skill is poor would stay long if the stress level is increasing. You may see this kind of resigns more if you have incompetent recruiters who could not assess the candidate correctly.
  • Chance of Being Head Hunted
    • Employees who are more likely to be head hunted must be spotted. Strong academic background, foreign language skills, strong personal brands are our essentials in recruiting and also others’.

This list of parameters may just go on, but it would not be useful if we spend so much time analyzing data while actual talents are in need of our personal interest.

I currently look for colleagues who are interested in creating ‘employee churn algorithms’ that would give us possible ‘employee churn’ shortlists to focus on. If you had already created such tools or are interested in creating one, please leave your comments or contact me.

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