Adopting a dynamic AI price optimisation model to encourage retail customer engagement

Steven Keith Platt and Martin Paul Block
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Steven Keith Platt: Quinlan School of Business, USA
Martin Paul Block: Medill School of Journalism, Media, Integrated Marketing Communications, Northwestern University, USA

Journal of AI, Robotics & Workplace Automation, 2023, vol. 2, issue 2, 184-195

Abstract: Technology innovation, changing consumer preferences and behaviours and competition compel successful enterprises to embrace change. Nowhere are these pressures more acute than in the retail industry and, in particular, for those engaged in the sale of fashion merchandise. As this paper will demonstrate, customer engagement (CE) strategies that leverage artificial intelligence (AI) afford retailers the ability to connect with customers in unique ways. The paper focuses on an AI optimisation model that was built for a fashion retailer. The objective was to build a demand prediction price optimisation model to increase margins realised on the clearance of fashion products. While our discussion will focus on that work, we also present techniques whereby such a model can be employed by CE enthusiasts in their businesses. More specifically, we advance that our model can enhance a company’s CE efforts as a method by which it enables a collaborative customer/company value creation system.

Keywords: customer engagement; artificial intelligence; demand prediction; price optimisation; retail management; retail promotion (search for similar items in EconPapers)
JEL-codes: G2 M15 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:aza:airwa0:y:2023:v:2:i:2:p:184-195

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