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January 5, 2022

Employee attrition or employee churn is one of the most serious threats that an organization faces against its growth and long term strategies, owing to its impact on work force and productivity, and subsequently the costs. High attrition influences detrimentally to an organization, as replacing key employees or business domain experts with niche competencies and skillsets is very difficult, also leading to increased hiring and training costs and besides the loss in revenue due to loss in productivity and morale. However, if there is a way to detect any probable instance of attrition, then the organization can take right measures at the right time to tackle the problem.  Let’s look at a machine learning (ML)-based model that can help determine the attrition probability of an employee and aid the organization to improve its work force productivity.

Model building

The Employee Attrition Prediction model provides foresight to human resources management (HRM) and organization leaders to facilitate talent retention, by predicting the employee attrition risk using machine learning techniques. The scope of the solution is to predict possible voluntary separation in an organization. Several HR management researches point out several causes for attrition, that can be factored and obtained from employee-level data.

The identification of possible cases of attrition can be treated as a supervised learning problem where AI will learn from historical attrition dataset to understand the attrition pattern within the organization.Employee attribute classes such as employee demographics, experience, compensation and hike, leave behaviour, promotion and performance,employee feedback, organizational details like job grade, level, department etc can be considered to build this machine learning model. Any tree based algorithms can be used to train this machine learning model.

Interpreting the model utility

Unlike a general prediction model, the developed attrtion prediction model also captures and provides the most contributive attributes for attrition risk for every individual employees, beside predicting their attrition probability score. The display of the factors in decreasing order of the impact along with their specific values enhances the interpretability of the model‘s output, making it more useful and impactful towards the causal analysis of the attrition in an organization. Further, output analysis can help the management to find out the segments viz. departments or job levels within a sub department, in the organization with, greater risk of attrition and take necessary actions to solve the issue.

Integration and benefits

The Employee Attrition Prediction model can be deployed in a compatible module of any HCM application. To enhance the utility of this solution, the attrition probability can be displayed against an employee’s profile using an appropriate interface and be made available to the HR management. The most contributive factors and their respective values can be displayed to the HR managers in the form of an attrition prediction report. Thus, the organization can be able to tackle the problem of attrition, once they are made aware of the probable employees that are going to quit, and their specific responsible factors, and then take a specific pre-emptive action to abate the issue.

This ML-driven model for identifying attrition risk can generate rapid outcomes post ingestion of appropriate training and prediction data. Due to the ability of continuous learning, the model can automatically train itself with incremental changes in data across time periods, without much manual intervention, once deployed. Being domain-independent, the solution can be used for different organizations, irrespective of the industry they belong, giving better decision-making capabilities to the management using the data-driven insights with interpretation of predicted results for attrition risk.

Implications
Employee attrition prediction with ML techniques have shown an evident impact in the overall improvement in the retention rate of an organization, wherein the organization benefits by early identifiation of the potential resigning employees, along with an understanding of the contributing factors. Such ML techniques make it possible for the HR management to address the employee-specific issues, ultimately boosting employee engagement and satisfaction, and leading to talent retention.

Vimal Mangeshkar is a Business Analyst and a team member with the Innovation and Product Engineering - Analytics (IPE - Analytics) group within TCS Platform Solutions. He possesses proficiency in business analytics, business intelligence and functional knowledge in HRM and operations and supply chain management. As a business analyst, he is responsible for the implementation of several predictive and embedded analytics in the home grown HRMS platform of TCS Platform Solutions. He holds a PGDM degree in Marketing and Operations Management from SDM Institute of Management Development, Mysore, and a bachelor’s degree in Mechanical Engineering from Goa University.

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