Email this article 
(Universitas Prima Indonesia, Indonesia)(2) Jumri Habbeyb DS
(Universitas Prima Indonesia, Indonesia)(3) Samuelta Barus
(Universitas Prima Indonesia, Indonesia)(4) Beriman Pasaribu
(Universitas Prima Indonesia, Indonesia)(5) Loredana Ioana Sirbu
(The University of Florence, Italy)(6) * Abdi Dharma
(Universitas Prima Indonesia, Indonesia)*corresponding author
AbstractEmployee turnover is the loss of talent in the workforce that can be costly for a company. Uplift modeling is one of the prescriptive methods in machine learning models that not only predict an outcome but also prescribe a solution. Recent studies are focusing on the conventional predictive models to predict employee turnover rather than uplift modeling. In this research, we analyze whether the uplifting model has better performance than the conventional predictive model in solving employee turnover. Performance comparison between the two methods was carried out by experimentation using two synthetic datasets and one real dataset. The results show that despite the conventional predictive model yields an average prediction accuracy of 84%; it only yields a success rate of 50% to target the right employee with a retention program on the three datasets. By contrast, the uplift model only yields an average accuracy of 67% but yields a consistent success rate of 100% in targeting the right employee with a retention program. KeywordsMachine Learning; Data Mining; Uplift Modeling; Employee Turnover Prediction; Business intelligence
|
DOIhttps://doi.org/10.29099/ijair.v4i2.169 |
Article metrics10.29099/ijair.v4i2.169 Abstract views : 2373 | PDF views : 609 |
Cite |
Full Text Download
|
References
P. W. Hom, T. W. Lee, J. D. Shaw, and J. P. Hausknecht, “One hundred years of employee turnover theory and research,†J. Appl. Psychol., vol. 102, no. 3, pp. 530–545, 2017, doi: 10.1037/apl0000103.
A. A. Kumar and K. B. Mathimaran, “Employee Retention Strategies –An Empirical Research,†Glob. J. Manag. Bus. Res., 2017.
K. Simbeck, “HR analytics and ethics,†IBM J. Res. Dev., vol. 63, no. 4/5, pp. 1–9, 2019.
K. Lepenioti, A. Bousdekis, D. Apostolou, and G. Mentzas, “Prescriptive analytics: Literature review and research challenges,†Int. J. Inf. Manage., vol. 50, pp. 57–70, 2020.
L. Berk, D. Bertsimas, A. M. Weinstein, and J. Yan, “Prescriptive analytics for human resource planning in the professional services industry,†Eur. J. Oper. Res., vol. 272, no. 2, pp. 636–641, 2019.
J. Kaur and K. S. Mann, “AI Based HealthCare Platform for Real Time, Predictive and Prescriptive Analytics,†in International Conference on Computing, Analytics and Networks, 2017, pp. 138–149.
A. Diez-Olivan, J. Del Ser, D. Galar, and B. Sierra, “Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0,†Inf. Fusion, vol. 50, pp. 92–111, 2019.
D. R. S. Kamath, D. S. S. Jamsandekar, and D. P. G. Naik, “Machine Learning Approach for Employee Attrition Analysis,†Int. J. Trend Sci. Res. Dev., vol. Special Is, no. Special Issue-FIIIIPM2019, pp. 62–67, 2019, doi: 10.31142/ijtsrd23065.
S. Yadav, A. Jain, and D. Singh, “Early Prediction of Employee Attrition using Data Mining Techniques,†Proc. 8th Int. Adv. Comput. Conf. IACC 2018, pp. 349–354, 2018, doi: 10.1109/IADCC.2018.8692137.
S. Saranya and J. S. Devi, “Predicting Employee Attrition Using Machine Learning Algorithms and Analyzing Reasons for Attrition,†Int. J. Adv. Eng. Res. Technol., vol. 6, no. 9, pp. 475–478, 2018.
I. W. Ariawan, “Predictive analysis of employee turnorver: a comparative study using logistic regression and artificial neural network,†J. Indones. Math. Soc., vol. 25, no. 3, pp. 325–335, 2019.
K. G. King, “Data analytics in human resources: A case study and critical review,†Hum. Resour. Dev. Rev., vol. 15, no. 4, pp. 487–495, 2016.
K. Sehgal, H. Bindra, A. Batra, and R. Jain, “Prediction of Employee Attrition Using GWO and PSO Optimised Models of C5. 0 Used with Association Rules and Analysis of Optimisers,†in Innovations in Computer Science and Engineering, Springer, 2019, pp. 1–8.
S. S. Alduayj and K. Rajpoot, “Predicting Employee Attrition using Machine Learning,†in 2018 International Conference on Innovations in Information Technology (IIT), 2018, pp. 93–98.
Y. Zhao, M. K. Hryniewicki, F. Cheng, B. Fu, and X. Zhu, “Employee turnover prediction with machine learning: A reliable approach,†in Proceedings of SAI intelligent systems conference, 2018, pp. 737–758.
J. Frierson and D. Si, “Who’s Next: Evaluating Attrition with Machine Learning Algorithms and Survival Analysis,†in International Conference on Big Data, 2018, pp. 251–259.
D. Bertsimas and N. Kallus, “From predictive to prescriptive analytics,†Manage. Sci., vol. 66, no. 3, pp. 1025–1044, 2020, doi: 10.1287/mnsc.2018.3253.
M. Ramannavar and N. S. Sidnal, “A proposed contextual model for big data analysis using advanced analytics,†Adv. Intell. Syst. Comput., vol. 654, pp. 329–339, 2018, doi: 10.1007/978-981-10-6620-7_32.
L. Berk, D. Bertsimas, A. M. Weinstein, and J. Yan, “Prescriptive analytics for human resource planning in the professional services industry,†Eur. J. Oper. Res., vol. 272, no. 2, pp. 636–641, 2019, doi: 10.1016/j.ejor.2018.06.035.
D. Pessach, G. Singer, D. Avrahami, H. C. Ben-Gal, E. Shmueli, and I. Ben-Gal, “Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming,†Decis. Support Syst., p. 113290, 2020.
E. Rombaut and M.-A. Guerry, “The effectiveness of employee retention through an uplift modeling approach,†Int. J. Manpow., Dec. 2019.
D. Olaya, K. Coussement, and W. Verbeke, “A survey and benchmarking study of multitreatment uplift modeling,†Data Min. Knowl. Discov., vol. 34, no. 2, pp. 273–308, 2020, doi: 10.1007/s10618-019-00670-y.
F. Devriendt, D. Moldovan, and W. Verbeke, “A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics,†Big Data, vol. 6, no. 1, pp. 13–41, 2018, doi: 10.1089/big.2017.0104.
F. Devriendt, J. Berrevoets, and W. Verbeke, “Why you should stop predicting customer churn and start using uplift models,†Inf. Sci. (Ny)., 2020, doi: 10.1016/j.ins.2019.12.075.
E. Ascarza, “Retention futility: Targeting high-risk customers might be ineffective,†J. Mark. Res., vol. 55, no. 1, pp. 80–98, 2018, doi: 10.1509/jmr.16.0163.
S. N. Mishra, D. R. Lama, and Y. Pal, “Human Resource Predictive Analytics (HRPA) For HR Management In Organizations,†Int. J. Sci. Technol. Res., vol. 5, no. 5, pp. 33–35, 2016.
T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,†in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794.
D. Nielsen, “Tree boosting with xgboost-why does xgboost win" every" machine learning competition?†NTNU, 2016.
N. J. Radcliffe, “Using control groups to target on predicted lift: Building and assessing uplift model,†Direct Mark. Anal. J., no. 3, pp. 14–21, 2007.
R. Gubela, A. Bequé, S. Lessmann, and F. Gebert, “Conversion uplift in e-commerce: A systematic benchmark of modeling strategies,†Int. J. Inf. Technol. Decis. Mak., vol. 18, no. 03, pp. 747–791, 2019.
K. Kane, V. S. Y. Lo, and J. Zheng, “Mining for the truly responsive customers and prospects using true-lift modeling: Comparison of new and existing methods,†J. Mark. Anal., vol. 2, no. 4, pp. 218–238, 2014.
D. J. Benjamin et al., “Redefine statistical significance,†Nat. Hum. Behav., vol. 2, no. 1, p. 6, 2018.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
________________________________________________________
The International Journal of Artificial Intelligence Research
Organized by: Departemen Teknik Informatika
Published by: STMIK Dharma Wacana
Jl. Kenanga No.03 Mulyojati 16C Metro Barat Kota Metro Lampung
Email: jurnal.ijair@gmail.com
This work is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.