@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.",
}

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.
M erscheid, Etienne Lee, Matthias
Predicting Customer Satisfaction with Soft Labels for Ordinal Classification