# Employee Turnover: a Risk Segmenting Investigation

This was different from my initial train of thought that the employees were potentially overworked.

Given this finding, I come to one particular conclusion: employees with highest risk of leaving are those that are on contract, seasonal employees, or are part-time employees.

By considering other variables such as the number of projects worked on by an employee, it is possible to further support this conclusion.

satisfaction_projects <- rate_matrix(df = hr_data, y = "left", c1 = "satisfaction_level", c2 = "number_project", cut = 20, avg = TRUE)View(satisfaction_projects)Here, it is evident to see that the below average satisfaction levels of 0.

363 to 0.

408 and 0.

408 to 0.

454 may in fact correspond to contract or part-time employees as the probability of turnover sharply decreases after 2 projects completed.

Are contract, part-time or seasonal employees more likely to be unhappy if the job is accident-prone?Now that we identified the high risk groups of employee turnover within this data set, this question comes to mind because we would like to address the fact that an employee’s enjoyment in their role should be tied to their satisfaction levels.

It could be that these part-time employees are experiencing hardships during their time at work, thereby contributing to their risk of leaving.

To answer this question, I take a look at the satisfaction level and number of projects completed given that an employee experienced a workplace accident.

# I use the package dplyr in order to filter the hr_data dataframe to only include observations that experienced a workplace accidentrequire(dplyr)accident_obs <- filter(hr_data, Work_accident == 1)satisfaction_accident <- rate_matrix(df = accident_obs, y = "left", c1 = "satisfaction_level", c2 = "number_project", cut = 20, avg = TRUE)View(satisfaction_accident)Here, given the below average satisfaction levels of 0.

363 to 0.

408 and 0.

408 to 0.

454 for a number of projects equal to 2 and given that employees experienced a workplace accident, there is evidence to suggest that there is a higher chance of turnover.

Further WorkThe purpose of this analysis was to apply a risk segmenting method on human resources analytics data to identify potential reasons for employee turnover.

I used probabilities or turnover rates to help identify some groups of employees that were at risk of leaving the company.

I found that there were higher chances of turnover given the employee had an extremely low satisfaction level but also discovered that the type of employee (contract, part-time, seasonal) could be identified as groups of a high risk of turnover.

I addressed a possible fact that the likelihood of unhappiness for part-time employees was attributed to them working on jobs that were accident-prone.

With the example presented in this post, Human Resources can use this information to put more efforts into ensuring contract, part-time, or seasonal employees experience lower turnover rates.

This analysis allowed us to identify which groups of employees are at risk and allowed us to identify potential causes.

This risk analysis approach can be applied to any other field of practice other than Human Resources, including Health and Finance.

It is useful to be able to come up with quick generic risk segments within your population so that further risk management solutions can be implemented for specific problems at hand.

Lastly, this post only provides a simple way to segment and analyze risk groups but it is not the only way!.More advanced methods such as clustering and decision trees can help identify risk groups more thoroughly and informatively to provide an even bigger picture.

For quick checks to domain expertise in any particular field of practice, the rate function I present here can be sufficient enough in identifying risk groups.

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