Work

Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider

Year: 2026

Type: conference-paper

Abstract: In subscription-based businesses, understanding why a customer intends to churn is as vital as the classification itself.We present a case study at a large European telecommunications provider, where we implement Text Bottleneck Models (TBMs) for post-call churn classification.The TBM distills dialogues into a sparse set of human-interpretable concepts and provides faithful, snippet-based evidence for every decision.We show that the TBM performs competitively with black-box baselines and demonstrate potential business impact via automated call profiling and an interactive stakeholder dashboard.Our work demonstrates that the perceived trade-off between interpretability and predictive performance can be bridged, providing the high-accuracy evidence needed for industrial retention strategies.* Work conducted during an industry internship.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2026.acl-industry.70

Authors: Adrian Sauter, Vera Neplenbroek, Georgios Vlassopoulos, Gianluigi Bardelloni

Institution: University of Amsterdam

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Award: Low Resource Chat-based Conversational Intelligence