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Improving Corporate Support by Predicting Customer e-Mail Response Time: Experimental Evaluation and a Practical Use Case
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-8929-7220
Telenor AB, SWE.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-9316-4842
2021 (English)In: Enterprise Information Systems / [ed] Filipe J., Śmiałek M., Brodsky A., Hammoudi S., Springer Science and Business Media Deutschland GmbH , 2021, p. 100-121Conference paper, Published paper (Refereed)
Abstract [en]

Customer satisfaction is an important aspect for any corporations customer support process. One important factor keeping the time customers’ wait for a reply at acceptable levels. By utilizing learning models based on the Random Forest Algorithm, the extent to which it is possible to predict e-Mail time-to-respond is investigated. This is investigated both for customers, but also for customer support agents. The former focusing on how long until customers reply, and the latter focusing on how long until a customer receives an answer. The models are trained on a data set consisting of 51, 682 customer support e-Mails. The e-Mails covers various topics from a large telecom operator. The models are able to predict the time-to-respond for customer support agents with an AUC of 0.90, and for customers with an AUC of 0.85. These results indicate that it is possible to predict the TTR for both groups. The approach were also implemented in an initial trial in a live environment. How the predictions can be applied to improve communication efficiency, e.g. by anticipating the staff needs in customer support, is discussed in more detail in the paper. Further, insights gained from an initial implementation are provided. © 2021, Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2021. p. 100-121
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 417
Keywords [en]
Decision support, e-Mail time-to-respond, machine learning, Prediction, Random forest, Decision trees, Electronic mail, Forecasting, Information systems, Information use, Sales, Communication efficiency, Customer support, Customer support process, Experimental evaluation, Learning models, Practical use, Random forest algorithm, Telecom operators, Customer satisfaction
National Category
Computer Sciences Business Administration
Identifiers
URN: urn:nbn:se:bth-22342DOI: 10.1007/978-3-030-75418-1_6Scopus ID: 2-s2.0-85106400443ISBN: 9783030754174 (print)OAI: oai:DiVA.org:bth-22342DiVA, id: diva2:1610528
Conference
22nd International Conference on Enterprise Information Systems, ICEIS 2020, Virtual, Online, 5 May through 7 May
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge Foundation
Funder
Knowledge Foundation, 20140032Available from: 2021-11-11 Created: 2021-11-11 Last updated: 2022-12-02Bibliographically approved

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Borg, AntonBoldt, Martin

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