@inproceedings{manderscheid-lee-2023-predicting,
title = "Predicting Customer Satisfaction with Soft Labels for Ordinal Classification",
author = "Manderscheid, Etienne and
Lee, Matthias",
editor = "Sitaram, Sunayana and
Beigman Klebanov, Beata and
Williams, Jason D",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-industry.62",
doi = "10.18653/v1/2023.acl-industry.62",
pages = "652--659",
abstract = "In a typical call center, only up to 8{\%} of callersleave a Customer Satisfaction (CSAT) surveyresponse at the end of the call, and these tend tobe customers with strongly positive or negativeexperiences. To manage this data sparsity andresponse bias, we outline a predictive CSATdeep learning algorithm that infers CSAT onthe 1-5 scale on inbound calls to the call centerwith minimal latency. The key metric to maximize is the precision for CSAT = 1 (lowestCSAT). We maximize this metric in two ways. First, reframing the problemas a binary class, rather than five-class problem during model fine-tuning, and then mapping binary outcomes back to five classes usingtemperature-scaled model probabilities. Second, using soft labels to represent the classes. Theresult is a production model able to support keycustomer workflows with high accuracy overmillions of calls a month.",
}
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%0 Conference Proceedings
%T Predicting Customer Satisfaction with Soft Labels for Ordinal Classification
%A Manderscheid, Etienne
%A Lee, Matthias
%Y Sitaram, Sunayana
%Y Beigman Klebanov, Beata
%Y Williams, Jason D.
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F manderscheid-lee-2023-predicting
%X In a typical call center, only up to 8% of callersleave a Customer Satisfaction (CSAT) surveyresponse at the end of the call, and these tend tobe customers with strongly positive or negativeexperiences. To manage this data sparsity andresponse bias, we outline a predictive CSATdeep learning algorithm that infers CSAT onthe 1-5 scale on inbound calls to the call centerwith minimal latency. The key metric to maximize is the precision for CSAT = 1 (lowestCSAT). We maximize this metric in two ways. First, reframing the problemas a binary class, rather than five-class problem during model fine-tuning, and then mapping binary outcomes back to five classes usingtemperature-scaled model probabilities. Second, using soft labels to represent the classes. Theresult is a production model able to support keycustomer workflows with high accuracy overmillions of calls a month.
%R 10.18653/v1/2023.acl-industry.62
%U https://aclanthology.org/2023.acl-industry.62
%U https://doi.org/10.18653/v1/2023.acl-industry.62
%P 652-659
Markdown (Informal)
[Predicting Customer Satisfaction with Soft Labels for Ordinal Classification](https://aclanthology.org/2023.acl-industry.62) (Manderscheid & Lee, ACL 2023)
ACL