Modeling Customer Experience in a Contact Center through Process Log Mining

Fu, Teng, Zampieri, Guido, Hodgson, David, Angione, Claudio and Zeng, Yifeng (2021) Modeling Customer Experience in a Contact Center through Process Log Mining. ACM Transactions on Intelligent Systems and Technology. ISSN 2157-6904 (In Press)

[img]
Preview
Text
teleware_paper_final.pdf - Accepted Version

Download (3MB) | Preview

Abstract

The use of data mining and modeling methods in service industry is a promising avenue for optimizing current processes in a targeted manner, ultimately reducing costs and improving customer experience. However, the introduction of such tools in already established pipelines often must adapt to the way data is sampled and to its content. In this study, we tackle the challenge of characterizing and predicting customer experience having available only process log data with time-stamp information, without any ground truth feedback from the customers. As a case study, we consider the context of a contact center managed by TeleWare and analyze phone call logs relative to a two months span. We develop an approach to interpret the phone call process events registered in the logs and infer concrete points of improvement in the service management. Our approach is based on latent tree modeling and multi-class Naïve Bayes classification, which jointly allow us to infer a spectrum of customer experiences and test their predictability based on the current data sampling strategy. Moreover, such approach can overcome limitations in customer feedback collection and sharing across organizations, thus having wide applicability and being complementary to tools relying on more heavily-constrained data.

Item Type: Article
Uncontrolled Keywords: Customer experience, process log data, latent tree model, contact center
Subjects: G400 Computer Science
N100 Business studies
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 02 Jul 2021 14:59
Last Modified: 31 Jul 2021 10:30
URI: http://nrl.northumbria.ac.uk/id/eprint/46596

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics