A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
AI-assisted sales coaching framework: Empirically-derived models for B2B communication analysis
Tekijät: Mäntyvaara, Joona; Nevalainen, Paavo; Glavatskiy, Kirill; Heikkonen, Jukka
Kustantaja: Elsevier BV
Julkaisuvuosi: 2026
Lehti: Array
Artikkelin numero: 100755
Vuosikerta: 30
eISSN: 2590-0056
DOI: https://doi.org/10.1016/j.array.2026.100755
Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla
Julkaisukanavan avoimuus : Kokonaan avoin julkaisukanava
Verkko-osoite: https://doi.org/10.1016/j.array.2026.100755
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/523219036
Rinnakkaistallenteen lisenssi: CC BY
Rinnakkaistallennetun julkaisun versio: Kustantajan versio
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
Business Finland supported this co-research project. Funding number: 6684/31/2023.
