How can HR professionals use big data to improve employee retention?

By aggregating HRIS data (tenure, performance, compensation, engagement scores) and applying predictive analytics, big-data models can calculate “flight risk” probabilities, flagging employees who are statistically most likely to resign. This allows targeted retention interventions and demonstrably improves retention rates.

A. Social-media scraping aids recruitment, not retention forecasting.

B. Real-time performance dashboards describe current output but do not predict quitting behaviour.

C. Image advertising uses demographic targeting for attraction; it does not address turnover risk.

1. Bassi, L. & McMurrer, D. (2016). Four lessons learned in how HR analytics improves workforce outcomes, People & Strategy, 39(2), p. 30–31.

2. Jain, R. & Singh, P. (2019). Predictive analytics for employee turnover: A machine-learning approach, International Journal of Manpower, 40(2), pp. 286-299. https://doi.org/10.1108/IJM-05-2017-0110

3. MIT Sloan School of Management. (2020). Course 15.279 “People Analytics”, Lecture 6 slides, pp. 12-14.