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Beyond the beaten paths of forecasting call center arrivals : on the use of dynamic harmonic regression with predictor variables

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00006037
URN to cite this document: urn:nbn:de:bvb:703-epub-6037-8

Title data

Rausch, Theresa Maria ; Albrecht, Tobias ; Baier, Daniel:
Beyond the beaten paths of forecasting call center arrivals : on the use of dynamic harmonic regression with predictor variables.
In: Journal of Business Economics = Zeitschrift für Betriebswirtschaft. (17 December 2021) .
ISSN 1861-8928
DOI der Verlagsversion: https://doi.org/10.1007/s11573-021-01075-4

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Project information

Project financing: Bayerisches Staatsministerium für Wirtschaft, Infrastruktur, Verkehr und Technologie

Abstract

Modern call centers require precise forecasts of call and e-mail arrivals to optimize stafng decisions and to ensure high customer satisfaction through short waiting times and the availability of qualifed agents. In the dynamic environment of multichannel customer contact, organizational decision-makers often rely on robust but simplistic forecasting methods. Although forecasting literature indicates that incorporating additional information into time series predictions adds value by improving model performance, extant research in the call center domain barely considers the potential of sophisticated multivariate models. Hence, with an extended dynamic harmonic regression (DHR) approach, this study proposes a new reliable method for call center arrivals’ forecasting that is able to capture the dynamics of a time series and to include contextual information in form of predictor variables. The study evaluates the predictive potential of the approach on the call and e-mail arrival series of a leading German online retailer comprising 174 weeks of data. The analysis involves time series cross-validation with an expanding rolling window over 52 weeks and comprises established time series as well as machine learning models as benchmarks. The multivariate DHR model outperforms the compared models with regard to forecast accuracy for a broad spectrum of lead times. This study further gives contextual insights into the selection and optimal implementation of marketing-relevant predictor variables such as catalog releases, mail as well as postal reminders, or billing cycles.

Further data

Item Type: Article in a journal
Keywords: Forecasting; Call center arrivals; Dynamic harmonic regression; Time series analysis; Machine learning; Customer relationship management
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Institutions of the University: Faculties
Faculties > Faculty of Law, Business and Economics
Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XIV - Marketing and Innovation
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XIV - Marketing and Innovation > Chair Business Administration XIV - Marketing and Innovation - Univ.-Prof. Dr. Daniel Baier
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVII - Information Systems and Value-Based Business Process Management > Chair Information Systems and Value-Based Business Process Management - Univ.-Prof. Dr. Maximilian Röglinger
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVII - Information Systems and Value-Based Business Process Management
Research Institutions
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-6037-8
Date Deposited: 15 Mar 2022 07:14
Last Modified: 15 Mar 2022 07:14
URI: https://epub.uni-bayreuth.de/id/eprint/6037

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