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Authors: Anton Borg 1 and Jim Ahlstrand 2

Affiliations: 1 Blekinge Institute of Technology, 37179 Karlskrona, Sweden ; 2 Telenor AB, Karlskrona, Sweden

Keyword(s): E-Mail Outliers, Customer Support System, Outlier Detection, Machine Learning, Decision Support.

Abstract: Customer support can affect customer churn both positively and negatively. By identify non-routine e-mails to be handled by senior customer support agents, the customer support experience can potentially be improved. Complex e-mails, i.e. non-routine, might require longer time to handle, being more suitable for senior staff. Non-routine e-mails can be considered anomalous. This paper investigates an approach for context-based unsupervised anomaly detection that can assign each e-mail an anomaly score. This is investigated in customer support setting with 43523 e-mails. Context-based anomalies are investigated over different time resolutions, by multiple algorithms. The likelihood of anomalous e-mails can be considered increased when identified by several algorithms or over multiple time resolutions. The approach is suitable to implement as a decision support system for customer support agents in detecting e-mails that should be handled by senior staff.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Borg, A. and Ahlstrand, J. (2021). Detecting Non-routine Customer Support E-Mails. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 387-394. DOI: 10.5220/0010396203870394

@conference{iceis21,
author={Anton Borg. and Jim Ahlstrand.},
title={Detecting Non-routine Customer Support E-Mails},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2021},
pages={387-394},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010396203870394},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Detecting Non-routine Customer Support E-Mails
SN - 978-989-758-509-8
IS - 2184-4992
AU - Borg, A.
AU - Ahlstrand, J.
PY - 2021
SP - 387
EP - 394
DO - 10.5220/0010396203870394
PB - SciTePress

- Science and Technology Publications, Lda.
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