Early Prediction of Employee Attrition using Data Mining Techniques

Aman Jain · Deepti Singh

82 citations0/0 files storedIEEE International Advance Computing Conference

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AI and HR Technologies Online Learning and Analytics Customer churn and segmentation

Abstract

Bill Gates was once quoted as saying, "You take away our top 20 employees and we [Microsoft] become a mediocre company". This statement by Bill Gates took our attention to one of the major problems of employee attrition at workplaces. Employee attrition (turnover) causes a significant cost to any organization which may later on effect its overall efficiency. As per CompData Surveys, over the past five years, total turnover has increased from 15.1 percent to 18.5 percent. For any organization, finding a well trained and experienced employee is a complex task, but it's even more complex to replace such employees. This not only increases the significant Human Resource (HR) cost, but also impacts the market value of an organization. Despite these facts and ground reality, there is little attention to the literature, which has been seeded to many misconceptions between HR and Employees. Therefore, the aim of this paper is to provide a framework for predicting the employee churn by analyzing the employee's precise behaviors and attributes using classification techniques.

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References

2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach 2008Customer churn prediction using improved balanced random forests 2017Designing of customer and employee churn prediction model based on data mining method and neural predictor 2012Applications of Data Mining Techniques in Telecom Churn Prediction

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