Title:Integrating Employee Value Model with Churn Prediction
VOLUME: 10 ISSUE: 4
Author(s):Nguyen Thi Ngoc Anh, Nguyen Danh Tu*, Vijender Kumar Solanki, Nguyen Linh Giang, Vu Hoai Thu, Luong Ngoc Son, Nguyen Duc Loc and Vu Thanh Nam
Affiliation:Big Data-AI Department, CMC Institute of Science and Technology (CIST), Hanoi, Mathematics for Informatics Department, School of Applied Mathematics and Informatics, Hanoi University of Science and Technology, Hanoi, Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, TS, Big Data-AI Department, CMC Institute of Science and Technology (CIST), Hanoi, Mathematics for Informatics Department, School of Applied Mathematics and Informatics, Hanoi University of Science and Technology, Hanoi, Big Data-AI Department, CMC Institute of Science and Technology (CIST), Hanoi, Big Data-AI Department, CMC Institute of Science and Technology (CIST), Hanoi, Big Data-AI Department, CMC Institute of Science and Technology (CIST), Hanoi
Keywords:Churn prediction, employee value, modeling, machine learning, artificial intelligence, logistic regression.
Abstract:
Background: In recent years, human resource management is a crucial role in every companies
or organization’s operation. Loyalty employee or Churn employee influence the operation of
the organization. The impact of Churn employees is difference because of their role in organization.
Objective: Thus, we define two Employee Value Models (EVMs) of organizations or companies
based on employee features that are popular of almost companies.
Methods: Meanwhile, with the development of Artificial intelligent, machine learning is possible to
give predict data-based models having high accuracy.Thus, integrating Churn prediction, EVM and
machine learning such as support vector machine, logistic regression, random forest is proposed in
this paper. The strong points of each model are used and weak points are reduced to help the companies
or organizations avoid high value employee leaving in the future. The process of prediction integrating
Churn, value of employee and machine learning are described detail in 6 steps. The pros of
integrating model gives the more necessary results for company than Churn prediction model but the
cons is complexity of model and algorithms and speed of computing.
Results: A case study of an organization with 1470 employee positions is carried out to demonstrate the
whole integrating churn predict, EVM and machine learning process. The accuracy of the integrating model
is high from 82% to 85%. Moreover, the some results of Churn and value employee are analyzed.
Conclusion: This paper is proposing upgrade models for predicting an employee who may leave an organization
and integration of two models including employee value model and Churn prediction is feasible.
