<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sauter-etal-2026-actionable">
    <titleInfo>
        <title>Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Adrian</namePart>
        <namePart type="family">Sauter</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Vera</namePart>
        <namePart type="family">Neplenbroek</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Georgios</namePart>
        <namePart type="family">Vlassopoulos</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gianluigi</namePart>
        <namePart type="family">Bardelloni</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yunyao</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Georg</namePart>
            <namePart type="family">Rehm</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mei</namePart>
            <namePart type="family">Tu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">San Diego, California, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-394-4</identifier>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">sauter-etal-2026-actionable</identifier>
    <location>
        <url>https://aclanthology.org/2026.acl-industry.70/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>1005</start>
            <end>1024</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider
%A Sauter, Adrian
%A Neplenbroek, Vera
%A Vlassopoulos, Georgios
%A Bardelloni, Gianluigi
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F sauter-etal-2026-actionable
%X 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.
%U https://aclanthology.org/2026.acl-industry.70/
%P 1005-1024

Markdown (Informal)

[Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider](https://aclanthology.org/2026.acl-industry.70/) (Sauter et al., ACL 2026)

• Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider (Sauter et al., ACL 2026)

ACL

• Adrian Sauter, Vera Neplenbroek, Georgios Vlassopoulos, and Gianluigi Bardelloni. 2026. Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 1005–1024, San Diego, California, USA. Association for Computational Linguistics.