B2B opportunity forecasting with explainable segmentation and cost-sensitive prediction

Abstract Business-to-business (B2B) sales forecasting is critical for organizational growth, but available data are often small, imbalanced, and noisy, complicating the identification of high-potential customers. This study proposes a hybrid, explainable framework for opportunity forecasting that integrates customer segmentation using Self-Organizing Maps (SOM), feature attribution via SHapley Additive exPlanations (SHAP), and probabilistic prediction with Random Forests (RF). Purchase probabilities are further refined through cost-sensitive ROC-based threshold optimization to account for class imbalance and business costs. The framework explicitly addresses noisy and heterogeneous data and unstable purchase signals via explainability-guided segmentation that isolates behaviorally consistent customer groups. Empirical evaluation on two telecommunications products demonstrates that high-probability segments, characterized by top-ranked features, achieve high predictive performance (F1-score = 0.964 for Product 1, 0.922 for Product 2), whereas heterogeneous segments show weaker signals. These results highlight that explainability-guided segmentation isolates actionable customer groups and improves decision-making reliability. Overall, the framework provides a modular, interpretable approach for identifying B2B sales opportunities, prioritizing resources, and supporting data-driven business decisions.