Next-Best-Action Systems in CRM: A Quantitative Study of Uplift, Policy Learning, and Business Impact

Next-best-action (NBA) systems are increasingly used in customer relationship management (CRM) to recommend personalized actions (e.g., outreach channel, offer, timing) intended to improve conversion, retention, or customer satisfaction. This paper presents a quantitative study design for evaluating NBA approaches on historical and experimental CRM data. We frame NBA as a policy learning problem, compare predictive response modeling, uplift modeling, and contextual bandits under a common set of business constraints, and report evaluation protocols that bridge offline metrics (AUC, expected uplift) with online outcomes (incremental conversion, revenue lift, and operational cost). We also discuss robustness to distribution shift, governance requirements, and practical deployment considerations.