Open Collections

Boutet, Patrick

University of British Columbia

Contact centres are one of the most important channels by which many private and public organizations, such as retail brands, banks, airlines, and government departments, interact with millions of people every day. Customer satisfaction is the dominant quality metric that contact centres use for a wide variety of evaluative purposes such as helping inform customer experience, voice of customer reporting, agent service quality, or product and service improvements. Through a qualitative study with six contact centres and drawing on methods from the human-computer interaction field such as design thinking, interviews, and affinity diagrams, we identify several important tasks in contact centres that rely heavily on customer satisfaction scores. We form a collaborative research partnership with the contact centre at lululemon athletica inc., a large apparel retail organization, in order to gain access to a large unique corpus of customer service live chat dialogues and domain experts. Through this partnership, we collaboratively develop a solution to improve the measurement of customer satisfaction. We propose a solution that predicts customer satisfaction scores for live chats based on a supervised machine-learning approach. We treat predicting customer satisfaction scores for live chats as a single-label document classification task and utilize transformer models to achieve notable performance versus several other common document classification models. To the best of our knowledge, this is the first study that shows fine-tuned transformer models are very effective at predicting customer satisfaction for live chats, thus improve the measurement of customer satisfaction in contact centres.

Text

2023-01-05

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/83573

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/