<p>Customer churn prediction is widely studied, but most models stop at risk scores and do not indicate how firms should intervene. We propose a counterfactual explanation framework that turns churn scores into individual retention actions by formulating churn prevention as a constrained cost-minimization problem and implementing a greedy, business-rule-based approximation. The framework encodes actionable versus non-actionable features, domain-specific intervention bounds and cost weights, and yields per-customer counterfactual profiles together with a Digital Retention Action Index that jointly evaluates churn reduction, intervention cost, and sparsity. We evaluate the approach on five public churn datasets (banking, e-commerce, internet service provision, credit cards, and streaming). Across domains, greedy counterfactuals significantly outperform random and single-feature baselines, yet success rates vary strongly (0.36–69.35%), revealing a sharp dissociation between classification accuracy and the feasibility of actionable recourse. Financial services exhibit much higher counterfactual success than digital services, suggesting that explicit service attributes provide more effective intervention levers than behavioral engagement metrics. These results show that high predictive accuracy alone is insufficient for retention management and that assessing the actionability of the feature space is essential when deploying churn models in practice.</p>