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A case study in Predictive Analytics to understand why employees leave a company and predict the next possible leaver

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Predicting Employee Turnover

1586528839207

Understanding and Predicting Employee Turnover

A case study in Prescriptive Analytics

"Alone we can do so little, together we can do so much." - Helen Keller

employee-turnover

"I quit..." This is the last thing anybody wants to hear from their employees. In a sense, it’s the employees who make the company. It’s the employees who do the work. It’s the employees who shape the company’s culture.

High rate of employee turnover can lead the company to huge monetary losses. Recognizing and understanding what factors are associated with employee turnover will allow companies and individuals to limit this from happening and may even increase employee productivity and growth.

These predictive insights give managers the opportunity to take corrective steps to build and preserve their successful business.

The Problem:

One of the most common problems at work is employee turnover which can be a nightmare for the companies. Replacing a worker earning about 50,000 dollars cost the company about 10,000 dollars or 20% of that worker’s yearly income according to the Center of American Progress.

Costs include:

  • Cost of off-boarding
  • Cost of hiring (advertising, interviewing, hiring)
  • Cost of onboarding a new person (training, management time)
  • Lost productivity (a new person may take 1-2 years to reach the productivity of an existing person)

Proposed Solution:

Since this model is used for humans, hence we should not solely rely on the models results. A better approach would be to use it's predicted probability to design a customized risk zone for all employees and treat each employee respectively.

  1. Safe Zone (Green) – Employees within this zone are considered safe. [Score < 0.2]

  2. Low Risk Zone (Yellow) – Employees within this zone are too be taken into consideration of potential turnover. This is more of a long-term track.

  3. Medium Risk Zone (Orange) – Employees within this zone are at risk of turnover. Action should be taken and monitored accordingly.

  4. High Risk Zone (Red) – Employees within this zone are considered to have the highest chance of turnover. Action should be taken immediately. [Score > 0.9]

employee-retention

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A case study in Predictive Analytics to understand why employees leave a company and predict the next possible leaver

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