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Personnel turnover control is one of the key functions of the companys HR specialists. By managing the process of employee movement, they identify the reasons for layoffs in time and take measures that prevent negative consequences. In essence, employee turnover refers to the percentage of employees who leave the company within a certain period of time. Since employee turnover is very expensive, HR managers have to implement practices to keep work teams (Skelton et al., 2020). The rate of employee turnover often depends on the scope and type of the business. Each company must control the processes of personnel movement and try to predict turnover rates.
The file contains different variances that are proposed to be relied on in predicting a companys turnover. Thus, this models main types of information include an employees gender, race, work experience, education level, personality, and the performance recorded after day, week, and year of work. To answer whether it is possible to reduce the turnover rate based on those indicators, one should be able to predict the trend relying on the provided information and identify the causes of employees leaving. From the file, it is possible to draw a personal portrait of employers and their experience and correlate those factors to the probability of leaving the company. Thus, it becomes easier to identify the type of employees who are more likely to increase the turnover rate.
However, whereas at the basic level, the logic seems right and straightforward, the model lacks some essential considerations. It is worth noting that the rate depends on many factors, but certainly not only on the level of salary. Rather, it is influenced by the overall working atmosphere and conditions that a company offers employees (Stamolampros et al., 2019). Employees feel comfortable when the work environment supports them in achieving their goals. Moreover, the reasons for leaving a company varies from unmet expectations regarding career growth, learning and training opportunities, travel problems, inconvenient schedule, and many others (Ekhsan, 2019). Only by identifying the reasons for people quitting the job or being fired is it possible to address the issue and reduce the turnover effectively.
Therefore, in order to accurately analyze and predict employee turnover, it is important to take into account not only the psychological portrait and impressions of new employees but also build ideas about the climate in the team. Perhaps employees become less motivated after 3, 5, or 10 years of service for one of the reasons mentioned above. Dissatisfaction and a high propensity to leave the company can be expressed in suddenly increased rates of absenteeism, which should also be taken into account (Stamolampros et al., 2019). Therefore, it is important to monitor the work environment in order to predict and prevent the causes of high employee turnover.
It should not be forgotten that predictive models only work where the conditions that were analyzed to build the model remain constant over time. Modern companies operate in a rapidly changing environment, so the built models must be constantly updated. It is important to constantly monitor the accuracy of your forecasts throughout the life of the predictive models. Otherwise, after a few months, the actual and expected results will begin to diverge significantly. In general, building a model and algorithms is only the first step in assessing employee turnover. Having determined which employees are at risk, it is necessary to understand the reasons that encourage people to quit and be able to do something about it and improve the situation. Therefore, the data collected in the file does not fully reflect all the factors that can help predict and reduce turnover.
References
Ekhsan, M. (2019). The influence job satisfaction and organizational commitment on employee turnover intention. Journal of Business, Management, & Accounting, 1(1). Web.
Skelton, A.R., Nattress, D., &Dwyer, R.J. (2020). Predicting manufacturing employee turnover intentions. Journal of Economics, Finance and Administrative Science, 25(49), 101-117. Web.
Stamolampros, P., Korfiatis, N., Chalvatzis, K., & Buhalis, D. (2019). Job satisfaction and employee turnover determinants in high contact services: Insights from Employees online reviews. Tourism Management, 75, 130-147. Web.
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