research-article

Modeling Customer Experience in a Contact Center through Process Log Mining

Authors:

Guido Zampieri,

Claudio Angione,

Yifeng ZengAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 12, Issue 4

Article No.: 48, Pages 1 - 21

https://doi.org/10.1145/3468269

Published: 12 August 2021 Publication History

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.

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Cited By

Occhipinti ARogers LAngione C(2022)A pipeline and comparative study of 12 machine learning models for text classificationExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117193201:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117193

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Modeling Customer Experience in a Contact Center through Process Log Mining

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 12, Issue 4

August 2021

368 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3468075

Editor:
Huan Liu
Arizona State University, USA

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Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2021

Accepted: 01 May 2021

Revised: 01 May 2020

Received: 01 November 2018

Published in TIST Volume 12, Issue 4

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Cited By

Occhipinti ARogers LAngione C(2022)A pipeline and comparative study of 12 machine learning models for text classificationExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117193201:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117193

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