Revamping Employee Engagement Practices by Augmenting Data Analytics

With the advent of COVID-19, the corporate world underwent a massive change. This stressed the need of effective employee engagement more than ever. Therefore, this study aims to understand the various dimensions of employee engagement and the use of Natural Language Processing to ensure an engaged workforce. To understand the problem, primary data collection was done with the help of interviews. The authors conducted interviews of 100 professionals from manufacturing, FMCG and IT companies in India to understand the problem from the root level. The paper then proposes a model, i.e., the PAUSE model which aims at categorizing the broad topic of employee engagement into five distinct categories. These categories tell us about the various areas in which employees can feel a sense of detachment or disengagement towards the organization. The model then suggests corrective actions that can be taken at the ground level to improve engagement in the targeted category.