The Role of Predictive Analytics in Enhancing Employee Retention in India’s Start-Up Ecosystem
Bratati Kundu, Rahul Shyam, Bijoy Mondal
Department of Management & NSHM Knowledge Campus (MAKAUT)
Department of Computer Science and Engineering & NSHM Institute of
Engineering and Technology (MAKAUT)
Department of Management & ABS Academy of Science, Technology and
Management (MAKAUT)
Abstract
One of the biggest issues that have been observed with start-ups in India is that of employee attrition which currently stands at an average of 18%-25%. This paper aims to examine these identified retention challenges, in the view of answering how predictive analytics can help start-ups to avoid high levels of employee turnover. Conducted from an interpretivist perspective, the study takes an inductive and exploratory stance, that involves analysing secondary qualitative data gathered from reputable databases.
Predictive analytics has been identified to enhance the rates at which employees are retained due to risk analysis and follow-up intervention. For example, where companies use predictive instruments, they are likely to see up to 25% less staff turnover. However, financial barriers, poor availability of data, and lack of sophisticated skills are the problems that prevent global use, especially among new start-ups.
Recommendations are more likely to be in the form of multi-tenanted, cloud-based, predictive analytics models, building HR’s analytics capability and including predictive insights into more people-oriented processes such as career management. These steps are designed to improve relative stability in the workforce while tackling particularities in the Indian start-up environment. Thus, the findings of this study should be followed by the primary data collection for further elucidation of the discussed topic and the broadening of the usage of predictive analytics in various organisational environments.
Keywords: Employee Retention, Predictive Analytics, Indian Start-Ups, Workforce Attrition, HR Analytics, Risk Analysis, Employee Turnover, Cloud-Based Models, Organizational Stability, Talent Management.
