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Measuring the Effectiveness of AI-Enabled Chatbots in Customer Service Using AnyLogic Simulation

Permanent link
https://hdl.handle.net/10037/33159
DOI
https://doi.org/10.1007/978-981-19-9338-1_33
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Date
2023-01-26
Type
Chapter
Bokkapittel

Author
Sun, Xu; Yu, Hao; Solvang, Wei Deng
Abstract
With the rapid technological advancement and innovation in Industry 4.0/5.0, digitalization has become the mainstream that transforms the paradigm of many industries, businesses, and services. In this regard, AI-enabled chatbots have been increasingly adopted by many companies for providing customer services instead of human agents. On the one hand, the use of AI-enabled chatbots provides opportunities to reduce customers’ waiting time, improve efficiency, and minimize costs. On the other hand, it may, however, negatively affect customers’ experiences due to several reasons, e.g., unfamiliarity with the IT system, incapability to properly answer individualized questions by chatbots, etc. Several qualitative analyses have been done to investigate the benefits and challenges of using AI-enabled chatbots for companies and also to reveal the customers’ perceptions and experiences, but there is still a lack of quantitative research that may help companies to adopt this new technology in their customer service. To fill this gap, in this paper, we develop a discrete event simulation model using the AnyLogic simulation software package to measure the effectiveness of AIenabled chatbots, which, through scenario analyses, provides managerial implications related to the average time in the system, response rate, satisfaction level, and cost savings. Thus, this method can help companies with a better understanding of the impact of adopting AI-enabled chatbots in their customer service.
Publisher
Springer Nature
Citation
Sun X, Yu H, Solvang WDS: Measuring the Effectiveness of AI-Enabled Chatbots in Customer Service Using AnyLogic Simulation. In: Wang Y, Yu T, Wang K. Advanced Manufacturing and Automation XII. Conference proceedings IWAMA 2022., 2023. Springer p. 266-274
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