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Call me maybe : Methods and practical implementation of artificial intelligence in call center arrivals' forecasting

Title data

Albrecht, Tobias ; Rausch, Theresa Maria ; Derra, Nicholas Daniel:
Call me maybe : Methods and practical implementation of artificial intelligence in call center arrivals' forecasting.
In: Journal of Business Research. Vol. 123 (February 2021) . - pp. 267-278.
ISSN 0148-2963
DOI: https://doi.org/10.1016/j.jbusres.2020.09.033

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Customer Relationship ManagementNo information
Projektgruppe WI Künstliche IntelligenzNo information

Abstract in another language

Machine learning (ML) techniques within the artificial intelligence (AI) paradigm are radically transformingorganizational decision-making and businesses’ interactions with external stakeholders. However, in timeseries forecasting for call center management, there is a substantial gap between the potential and actualuse of AI-driven methods. This study investigates the capabilities of ML models for intra-daily call centerarrivals’ forecasting with respect to prediction accuracy and practicability. We analyze two datasets of anonline retailer’s customer support and complaints queue comprising half-hourly observations over 174.5 weeks.We compare practically relevant ML approaches and the most commonly used time series models via cross-validation with an expanding rolling window. Our findings indicate that the random forest (RF) algorithmyields the best prediction performances. Based on these results, a methodological walk-through example of acomprehensive model selection process based on cross-validation with an expanding rolling window is providedto encourage implementation in individual practical settings

Abstract in another language

Artificial intelligence; Machine learning; Call center forecasting; Predictive analytics

Further data

Item Type: Article in a journal
Refereed: Yes
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 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 > 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 Information Systems and Value-Based Business Process Management
Research Institutions
Research Institutions > Affiliated Institutes
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 15 Oct 2020 11:28
Last Modified: 21 Oct 2020 05:54
URI: https://eref.uni-bayreuth.de/id/eprint/58393