Preserving talent: Employee churn prediction in higher education

Retaining employees in a knowledge-based organisation, such as a university, is a significant challenge, especially as the need to keep knowledgeable workers is key to sustaining their competitive advantage. Knowledge is the organisations’ and employees\u27 most valuable and productive asset, but this intrinsic character leads to a high employee turnover. Often, universities learn about employees\u27 imminent departure too late. To prevent the loss of high-performing employees and to detect the warning signs early, business firms have been using advanced data mining techniques to predict “customer churn”. Recently these techniques have been used with “employee churn” in various industries, but not in higher education. This research bridges this gap by applying data mining techniques to predict employee churns in a university. The contributions of this research will be: 1) to identify critical factors that lead to talent losses; 2) to help universities devise appropriate strategies to retain their employees’ talents

Employee Churn Prediction using Logistic Regression and Support Vector Machine

It is a challenge for Human Resource (HR) team to retain their existing employees than to hire a new one. For any company, losing their valuable employees is a loss in terms of time, money, productivity, and trust, etc. This loss could be possibly minimized if HR could beforehand find out their potential employees who are planning to quit their job hence, we investigated solving the employee churn problem through the machine learning perspective. We have designed machine learning models using supervised and classification-based algorithms like Logistic Regression and Support Vector Machine (SVM). The models are trained with the IBM HR employee dataset retrieved from https://kaggle.com and later fine-tuned to boost the performance of the models. Metrics such as precision, recall, confusion matrix, AUC, ROC curve were used to compare the performance of the models. The Logistic Regression model recorded an accuracy of 0.67, Sensitivity of 0.65, Specificity of 0.70, Type I Error of 0.30, Type II Error of 0.35, and AUC score of 0.73 where as SVM achieved an accuracy of 0.93 with Sensitivity of 0.98, Specificity of 0.88, Type I Error of 0.12, Type II Error of 0.01 and AUC score of 0.96

Comparison of Classification Algorithms and Undersampling Methods on Employee Churn Prediction: A Case Study of a Tech Company

Churn prediction is a common data mining problem that many companies face across industries. More commonly, customer churn has been studied extensively within the telecommunications industry where there is low customer retention due to high market competition. Similar to customer churn, employee churn is very costly to a company and by not deploying proper risk mitigation strategies, profits cannot be maximized, and valuable employees may leave the company. The cost to replace an employee is exponentially higher than finding a replacement, so it is in any company’s best interest to prioritize employee retention. This research combines machine learning techniques with undersampling in hopes of identifying employees at risk of churn so retention strategies can be implemented before it is too late. Four different classification algorithms are tested on a variety of undersampled datasets in order to find the most effective undersampling and classification method for predicting employee churn. Statistical analysis is conducted on the appropriate evaluation metrics to find the most significant methods. The results of this study can be used by the company to target individuals at risk of churn so that risk mitigation strategies can be effective in retaining the valuable employees. Methods and results can be tested and applied across different industries and companies

Predicting HR Churn with Python and Machine Learning

Employee turnover imposes a substantial financial burden, necessitating proactive retention strategies. The aim is to leverage HR analytics, specifically employing a systematic machine learning approach, to predict the likelihood of active employees leaving the company. Using a systematic approach for supervised classification, the study leverages data on former employees to predict the probability of current employees leaving. Factors such as recruitment costs, sign-on bonuses, and onboarding productivity loss are analysed to explain when and why employees are prone to leave. The project aims to empower companies to take pre-emptive measures for retention. Contributing to HR Analytics, it provides a methodological framework applicable to various machine learning problems, optimizing human resource management, and enhancing overall workforce stability. This research contributes not only to predicting turnover but also proposes policies and strategies derived from the model's results. By understanding the root causes and timing of employee departures, companies can proactively implement measures to mitigate turnover, thereby minimizing the associated financial and operational burdens

Prediksi Employee Churn Dengan Uplift Modeling Menggunakan Algoritma Logistic Regression

Pada sebuah perusahaan, karyawan merupakan aset yang berharga dan dapat menunjang kesuksesan perusahaan tersebut. Namun, hilangnya tenaga kerja dapat merugikan perusahaan. Kondisi ini disebut dengan Employee Churn. Salah satu solusi untuk mengatasi Employee Churn adalah dengan menerapkan model Uplift Modeling. Dalam penelitian ini, penulis menganalisa penerapan Logistic Regression terhadap Uplift Modeling dalam permasalahan Employee Churn. Data yang diteliti adalah data karyawan dari IBM HR Analytics. Hasil prediksi pada penelitian ini mendapat akurasi sebesar 64,40%, sedangkan hasil preskripsi menghasilkan hasil yang cukup baik apabila menerapkan waktu kerja tambahan pada karyawan. Berdasarkan hasil yang didapat, diketahui bahwa para karyawan justru cenderung bertahan di perusahaan apabila diberikan waktu kerja tambahan

The Theoretical-Conceptual Model of Churning in Human Resources: The Importance of Its Operationalization

Given the current socio-economic context in which the labor market is set in, if we were to consider both employment opportunities in specific economic periods and the individual expectations workers have regarding one’s working conditions, it should be a matter of one’s individual right of choice to decide whether or not to stay or leave and change companies. The paper we present before you took into account the phenomenon of churning as a cyclical process. Our main goal was to understand the main causes leading to it in the context of human resources and, ultimately, what were the consequences emerging from it. In order to carry out this analysis, we put forward a conceptual-theoretical model of the phenomenon of churning, made possible through the analysis of both the currently available literature and the empirical studies and conclusions stemming from the latter. The aforementioned analysis allowed us to trace all main causes responsible for the origin of churning and also, to understand how it could be implemented further on, in order to minimize the costs associated with hiring new personnel and retaining qualified employees by resorting beforehand to the implementation of strategic measures of human resources retention. By applying the methodology based on grounded theory, this study allowed us to further contribute to the already available, yet limited, literature and definition of this multifaceted and greatly complex subject that is the phenomenon of churning.info:eu-repo/semantics/publishedVersio

Churn Prediction of Employees Using Machine Learning Techniques

Employees are considered as the most valuable assets of any organization. Various policies have been introduced by the HR professionals to create a good working environment for them, but still, the rate of employees quitting the Technology Industry is quite high. Often the reason behind their early attrition could be due to company-related or personal issues, such as No satisfaction at the workplace, Fewer opportunities for learning, Undue Workload, Less Encouragement, and many others. This paper aims in discussing a structured way for predicting the churn rate of the employees by implementing various Classification techniques like SVM, Random Forest classifier, and Naives Bayes classifier. The performance of the classifiers was compared using metrics like Confusion Matrix, Recall, False Positive Rate, and Accuracy to determine the best model for the churn prediction. We found that among the models, the Random Forest classifier proved to be the best model for IT employee churn prediction. A Correlation Matrix was generated in the form of a heatmap to identify the important features that might impact the attrition rate

GR-267 Churn Prediction

Employee churn is a situation where people leave the organization voluntarily or involuntarily. This has become a serious problem in recent times. We have also seen that attrition rates in several industries are going high. So, it is very much required to understand and analyze the reason behind attrition and why this is happening. We must conduct an analysis to know what the factors affecting employee churn are. It will create a huge impact on the organization if the attrition rate goes high. In order to resolve this issue, we are trying to take up this issue and find the best solution for this