Optimizing AI strategies in e-commerce customer service: An agent-based simulation

Yali Zhang, Zhenbin Ding, Jun Sun, Xingfang Zhao, Xu Hu and Zhaojun Yang ()
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Yali Zhang: Northwestern Polytechnical University
Zhenbin Ding: Northwestern Polytechnical University
Jun Sun: University of Texas Rio Grande Valley
Xingfang Zhao: Northwestern Polytechnical University
Xu Hu: North China Electric Power University – Baoding Campus
Zhaojun Yang: Xidian University

Electronic Markets, 2025, vol. 35, issue 1, No 77, 22 pages

Abstract: Abstract The growing integration of chatbots in e-commerce customer service presents opportunities and challenges for online retailers in shaping effective artificial intelligence (AI) strategies. This study evaluates human-only, AI-only, and human–AI collaboration strategies using an agent-based simulation model across varying levels of task complexity, service volume, and product margin. Results show that the AI-only strategy excels in low-volume, simple tasks due to its cost-effectiveness, while the human–AI collaboration strategy proves superior in managing high-volume or complex inquiries by scaling human involvement to meet demand. For high-margin products, this collaborative approach delivers the best service, whereas the AI-only strategy is optimal for low-margin items. Enhancing chatbots’ anthropomorphic qualities could further improve service performance, but only if technological advancements are sufficient. The findings provide actionable insights for optimizing AI deployment and fostering adaptive customer service.

Keywords: Platform e-tailer; Online customer service; Human–AI collaboration; Request subjectivity; Time sensitivity; Agent-based simulation (search for similar items in EconPapers)
JEL-codes: C63 L81 M31 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12525-025-00821-8

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