Data-Driven Business Analytics Practices in Haidilao: Artificial Intelligence Applications and Ethical Challenges in Customer Experience Optimisation

Abstract

This study focuses on Haidilao's Artificial Intelligence (AI)-driven customer experience optimisation practices, aiming to explore how AI technology can enhance customer experience in food service. The study adopts the methods of literature induction and actual case observation (questionnaires, interviews and internal data were not used), and uses the perceived value theory, technology acceptance model (TAM) and service-dominant logic as the analytical framework. On this basis, Haidilao's AI application practices in intelligent queuing, personalised recommendation, automated ordering and intelligent feedback are analysed. In the case of Haidilao, AI-driven personalised recommendation, intelligent ordering and inspection management effectively shorten the ordering and response time and improve process stability and service consistency; customer experience in terms of convenience, fun and interactivity is enhanced. Based on the perceived value perspective, there is a positive correlation between value enhancement and loyalty, while technology acceptance affects customers' willingness to use smart touchpoints (e.g., service robots, recommendation systems). Privacy sensitivity poses an important trade-off with a preference for the "human touch"; customers are more likely to develop positive attitudes when the purpose of data use is transparent and optional. Overall, this case shows that "customer-centric" AI applications can optimise experience while improving operational efficiency, but the benefits depend on the fine-grained management of privacy, emotional service and scenario adaptation.