Mining the Minds of Customers from Online Chat Logs
Abstract
This study investigates factors that may determine satisfaction in customer service operations. We utilized more than 170,000 online chat sessions between customers and agents to identify characteristics of chat sessions that incurred dissatisfying experience. Quantitative data analysis suggests that sentiments or moods conveyed in online conversation are the most predictive factor of perceived satisfaction. Conversely, other session related meta data (such as that length, time of day, and response time) has a weaker correlation with user satisfaction. Knowing in advance what can predict satisfaction allows customer service staffs to identify potential weaknesses and improve the quality of service for better customer experience.
Keywords
Cite
@article{arxiv.1510.01801,
title = {Mining the Minds of Customers from Online Chat Logs},
author = {Kunwoo Park and Jaewoo Kim and Jaram Park and Meeyoung Cha and Jiin Nam and Seunghyun Yoon and Eunhee Rhim},
journal= {arXiv preprint arXiv:1510.01801},
year = {2015}
}
Comments
4 pages, ACM CIKM'15