Predicting sales offer success of industrial configure-to-order equipment: evaluating explainable AI approach with B2B sales experts

Abstract This study investigates the use of explainable artificial intelligence (AI) for predicting sales offer success in the business-to-business (B2B) context of configure-to-order industrial equipment. Using real sales data from over 400 sales cases and extracted from customer relationship management and Configure-Price-Quote systems of an industrial partner, we built a gradient boosted decision tree (XGBoost) model to predict win/loss outcomes of sales cases. Model explainability was enabled through Shapley additive explanations (SHAP) to identify and quantify feature contributions to individual predictions. The predictive performance of the model was compared against assessments made by experienced sales experts using the same cases. The model achieved balanced accuracies of 83.9% on the full dataset and 74.1% on a regional subset, substantially outperforming expert predictions, which reached 63.0 and 54.4%, respectively. To evaluate the practical value of explainable AI, 5 sales experts reviewed SHAP-based explanations through a structured questionnaire using real regional sales cases. The results show partial alignment between model-identified important features and expert reasoning, while also revealing features with strong predictive power that experts regarded as unintuitive or unimportant. Despite these discrepancies, experts reported that the explanations were understandable and actionable in a sales context. In particular, they perceived the system as useful for prioritizing sales opportunities and identifying ways to modify offers to increase success probability. Overall, the findings demonstrate that explainable AI can outperform human experts in sales success prediction while also providing interpretable insights that support decision-making in B2B sales practice.