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Communication Dans Un Congrès Année : 2019

CAN WE PREDICT SELF-REPORTED CUSTOMER SATISFACTION FROM INTERACTIONS?

Résumé

In the context of contact centers, customers' satisfaction after a conversation with an agent is a critical issue which has to be collected in order to detect problems and improve quality of service. Automatically predicting customer satisfaction directly from system logs, without any survey or manual annotation is a challenging task of a great interest for the field of human-human conversation understanding and for improving contact center quality of service. Unlike previous studies that have focused on questions directly related to the content of a conversation, we look at a more general opinion about a service which is called the "Net Promoter Score" (NPS) where customers are considered either as promoters, detractors or neutral. On a very large corpus of chat-conversations with customer satisfaction surveys, we explore several classification scheme in order to achieve this prediction task, only using conversation logs.
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Dates et versions

hal-02439687 , version 1 (14-01-2020)

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Citer

Jeremy Auguste, Delphine Charlet, Geraldine Damnati, Frédéric Béchet, Benoit Favre. CAN WE PREDICT SELF-REPORTED CUSTOMER SATISFACTION FROM INTERACTIONS?. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, Brighton, United Kingdom. ⟨10.1109/ICASSP.2019.8683896⟩. ⟨hal-02439687⟩
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