AI-assisted sales coaching framework: Empirically-derived models for B2B communication analysis

Abstract

This research addresses the design challenge of integrating multimodal communication analytics into AI-assisted coaching systems suitable for real-time deployment. Drawing on analysis of 5183 Finnish B2B sales calls, this study provides the first empirically-grounded design specifications for multimodal sales coaching by unifying graph-theoretic conversation analysis, temporal prediction, and rejection modeling. Network analysis reveals that successful conversations exhibit 4.3× lower structural density (0.0224 vs. 0.0960) while covering broader topic ranges, establishing conversational efficiency rather than complexity as a guiding design principle. Temporal prediction identifies a 60-second optimal intervention window, achieving 78.4% AUC for reliable real-time guidance. Rejection modeling achieves 95.7% AUC with interpretable early-warning signals validated through SHAP analysis. These findings are operationalized through evidence-based quality indicators spanning acoustic, semantic, and linguistic modalities, supported by computationally efficient formulations suitable for real-time processing. Duration-matched validation confirms threshold robustness independent of call length, and bias auditing demonstrates equitable performance across salesperson groups (FPR disparity = 0.027). The framework provides validated design specifications aligned with EU AI Act compliance provisions, demonstrating how multimodal communication analytics can be transformed into deployable coaching systems.