AI-Driven Talent Lifecycle Analytics for Employee Retention

Thesis Abstract

This study investigates how AI-driven talent lifecycle analytics can enhance employee retention by predicting turnover risk and informing targeted retention strategies within mid-to-large technology firms. The problem addressed is the persistent gap between predictive insights and actionable retention interventions, which often leads to suboptimal allocation of HR resources and higher inadvertent turnover. The aim is to develop a robust analytics framework that integrates data from recruitment, onboarding, performance management, learning and development, and separations to forecast attrition propensity and prescribe evidence-based interventions. Specific objectives include (1) designing an AI-enabled, end-to-end talent analytics pipeline; (2) identifying salient predictors of turnover across career stages; (3) evaluating the impact of personalized retention interventions on actual turnover rates; and (4) assessing ethical and governance implications of AI-driven HR analytics. A mixed-methods approach is employed. The quantitative component uses a longitudinal panel comprising 3,000 employees from three multinational technology firms over a five-year horizon (2019–2023), with annual data waves. Predictors drawn from human resource information systems (HRIS) include tenure, role complexity, compensation, training hours, performance ratings, promotion history, workload indicators, and engagement survey scores, augmented by behavioral signals derived from collaboration networks and digital workplace metrics. AI techniques include survival analysis to model time-to-turnover, random forest and gradient boosting for feature importance, and XGBoost for predictive accuracy, complemented by SHAP (SHapley Additive exPlanations) values for interpretability. A nested case-control design within the cohort allows examination of high-risk subgroups. The qualitative component comprises 40 in-depth interviews with HR practitioners and team managers to contextualize predictive models and validate intervention mechanisms. Thematic analysis will be guided by the Job Demands-Resources (JD-R) theory and Social Exchange Theory to interpret motivational and organizational-support factors. Data collection instruments include standardized HRIS extracts, validated employee engagement and job satisfaction surveys, exit interview records, and semi-structured interview guides. Validity and reliability are ensured through triangulation of HR data, survey instruments with Cronbach’s alpha >0.80, and inter-coder reliability checks (? > 0.70) for qualitative coding. Data preprocessing will address missing data via multiple imputation and detective data quality issues through consistency checks. Model performance will be evaluated using area under the ROC curve, precision-recall metrics, calibration plots, and time-dependent AUC for turnover predictions. Causal inference will be approached using propensity score matching to estimate the effect of implementing personalized retention interventions on observed turnover, controlling for confounding variables. The research will implement ethical safeguards including data minimization, employee consent where required, model explainability, and governance protocols to mitigate bias and discrimination, aligned with relevant data protection regulations. Key expected findings include a parsimonious set of high-impact predictors of turnover across career stages, demonstrated improvement in turnover predictive accuracy when integrating lifecycle data and network metrics, and statistically significant reductions in voluntary turnover for units employing personalized retention interventions over a 12–18 month follow-up. The study anticipates that interventions informed by AI-driven insights—such as targeted development plans, proactive workload balancing, and transparent career progression communications—will yield measurable retention gains while preserving employee trust and privacy. The study contributes to knowledge by operationalizing an end-to-end AI-driven talent lifecycle analytics framework that unites predictive analytics with prescriptive retention strategies, advancing understanding of how lifecycle-contextual features interact with organizational support to influence retention. It extends the application of JD-R and Social Exchange Theory within AI-enabled HR analytics, providing empirical evidence on how data-driven interventions align with employee perceived support and resource availability. Practically, the research offers a replicable methodology for firms seeking to deploy scalable retention analytics, with a governance blueprint addressing data ethics, model transparency, and continuous monitoring. In conclusion, the findings are expected to demonstrate that AI-enabled talent lifecycle analytics can meaningfully reduce turnover by delivering actionable, ethically sound, and context-aware retention strategies. Recommendations include implementing an enterprise-wide analytics platform with modular, audit-ready models; embedding HR analytics literacy initiatives for managers; establishing ongoing monitoring of model performance and fairness; and adopting a phased rollout to validate effectiveness across diverse organizational units before broader deployment.