Originally served on March 17, 2020 @ 2:46 pm
Last Updated on July 17, 2021 by Pratik
Objective
HR analytics, especially to evaluate attrition risk has been gaining its much deserved traction, enabling organizations to make data driven decisions (more than a passing fad, data driven decision making is also one of the key ingredients of building a high performance organization), and we, at The Brew, were approached by the client organization to help them de-clutter their attrition – to analyze attrition risk and factors which influences the turnover in an organization (the turnover costs at the organization had a definite area of improvement – also see The Brew’s Turnover Costs Estimator) using multiple data mining techniques (See all of works & applications of HR Analytics here).
In this case, the objective is to identify key factors (with a definite level of confidence) influencing attrition. For this case, different predictive models are also used to arrive at a minimum threshold of confidence for factors attributed to attrition. The result is a robust analysis, enhancing the efficiency and effectiveness of human resource planning processes used to focus on the employee turnover problem.
This study is conducted on real data provided by a global IT organization, which is an Amsterdam based company. For presenting this report, the data received from client was fully anonymized to fulfill the privacy rules. The necessary information which is used to answer our research question was present in the data.
Attrition Risk Findings
From our analysis, the attrition risk factors which have highest influence on employee turnover are Age, Location, Currency, Base salary, Business level, FTE, In-position and In-service (See how to develop an aligned HR strategy using these key insights). The factors which have less impact in predicting the employee turnover are Hire type, Gender, Contract type and Functional area. (Estimate your organization’s turnover costs here – for people intensive organizations, the turnover costs are in range upwards of 15% of over overall revenues – How much is it for you & see how much can The Brew help you save)
Demographic factors like age and location are strong predictors of employee turnover because the younger employees from age 18-25 are more likely to turnover than older employees. Since younger employees leave in early stages so in-position and in-service also have some effect on turnover. These results are consistent with a study on turnover rates conducted by Hill and Associates which found that young undergraduates, graduates and post graduates in the outsourcing business had changed their jobs at least once in the past three years (Link). The location in our analysis has an impact because most of the employees who leave the company are from UK.
One of the fascinating reveal was of key indicators of Currency and Base Salary; Since the currency is one of the most important predictors of employee turnover, it can be explained by the fact that people who are working in UK and not getting salary in GBP are more likely to leave the company. Moreover, the people who are working as a part-time employee are more likely to leave the company as compared to a full-time employee. The salary of the part-time employee is also lower than the full-time employee.
Conclusions to mitigate attrition risks
This study investigated the attrition risk factors of influence on employee turnover using different data mining techniques. Three important conclusions can be made from this research study. First, the identification of the important factors like age, location, currency, business level, in-position, in service and FTE are significant from a research perspective.
Secondly, it finds the importance of prediction models which can be used to predict the employee turnover. By considering different models, we analysis concludes that the best prediction is possible using random forest. . Lastly, as this analysis is specific to the given dataset so these predictive accuracies can be used by client organization, providing this dataset and they can identify those employees who have turnover intentions even before they had made their final decision to leave.
