@inproceedings{krawczyk-etal-2026-interpretable,
    title = "Interpretable Satisfaction Modeling in Sales Dialogues: A Metrics-based Approach Validated Against Text-based Model",
    author = "Krawczyk, Natalia  and
      Kasicka, Alicja  and
      Przyby{\l}, Bartosz  and
      Gromada, Justyna",
    editor = "Choi, Jinho D.  and
      Chen, Yun-Nung  and
      Funakoshi, Kotaro  and
      Emami, Ali",
    booktitle = "Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = aug,
    year = "2026",
    address = "Atlanta, Georgia, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.sigdial-1.40/",
    pages = "565--584",
    abstract = "Understanding drivers of customer satisfaction in sales conversations is crucial for dialogue system optimization and customer experience improvement, yet existing neural approaches lack interpretability. We introduce a sales action annotation framework that tracks offers, cross-sells, and customer decisions in sales dialogues. From these structured annotations, we derive interpretable metrics spanning conversion rates, timing and negotiation patterns, and sales outcomes to predict user satisfaction. We validate our metrics-based regression model against a transformer-based text-embedding baseline on 5200 simulated phone sales dialogues. Our interpretable model achieves strong performance ($R^2$=0.87 vs. 0.92 for the baseline) with full transparency, identifying key satisfaction drivers with practical implications for dialogue design: purchase completion, conversation efficiency, and negotiation patterns."
}