Analytical framework to discover churn rate insights

• Autor(a) principal: Saúde, Patrícia Raquel Gil Lisboa
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/20983
• topic: Employee churn; Misconception; Visualization; Correlation; Framework; Rotatividade de colaboradores; Premissas; Visualização de dados; Correlações

Employee turnover is a costly problem for any business. For every position left vacant, the time and cost associated with identifying, acquiring, and training the right person can lessen a businesses impact on revenue growth and brand expansion [1]. Employee retention becomes a crucial strategy for the company since it can reduce costs. To mitigate this problem, organizations must create strategy plans as a way to retain their employees. To create these strategies, in the first instance, there is a need to understand and evaluate the reasons employees leave the company. These reasons can be validated based on existing reasons, or by creating analyses of the organization's existing data regarding employees. The reasons an employee leaves the company can be classified into two events. The first can be initiated by the employee. In this case, it is a voluntary turnover. The second one is an involuntary turnover, characterized by the initiative of company. This dissertation includes the creation of a framework for the acknowledgement of the reasons for an employee to leave the company. The framework is created based on a real project of an organization and intends to demystify common misconceptions/premises, based on literature review, regarding the reasons for an employee turnover. It proposes to validate if the misconceptions apply to the business context and if they are true. Additionally, this framework also allows the study of the main reasons that lead to the decision to leave an organization (e.g. search for career progress) by suggesting some key variables for the application of these analyses to understand how they affect employee churn.