@inproceedings{sauter-etal-2026-actionable,
    title = "Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider",
    author = "Sauter, Adrian  and
      Neplenbroek, Vera  and
      Vlassopoulos, Georgios  and
      Bardelloni, Gianluigi",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.70/",
    pages = "1005--1024",
    ISBN = "979-8-89176-394-4",
    abstract = "In subscription-based businesses, understanding why a customer intends to churn is as vital as the classification itself. We present a casestudy at a large European telecommunications provider, where we implement Text Bottleneck Models (TBMs) for post-call churn classifica-tion. The TBM distills dialogues into a sparse set of human-interpretable concepts and provides faithful, snippet-based evidence for everydecision. We show that the TBM performs competitively with black-box baselines and demonstrate potential business impact via automatedcall profiling and an interactive stakeholder dashboard. Our work demonstrates that the perceived trade-off between interpretability andpredictive performance can be bridged, providing the high-accuracy evidence needed for industrial retention strategies."
}