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Adopting a dynamic AI price optimisation model to encourage retail customer engagement
Journal of AI, Robotics & Workplace Automation,
2
(2),
184-195
(2023)
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
The full article is available to subscribers to the journal.
Author's Biography
Steven Keith Platt ’s teaching and research are focused on artificial intelligence and statistics. Before joining the Quinlan School of Business in 2021, Steven served as Research Director at the Retail Analytics Council, Northwestern University, where he ran the AI Lab and Retail Robotics Initiative, as well as teaching various AI courses. Steven has consulted with companies including AT&T, Kroger, McDonald’s and Microsoft, among many others. In addition to his academic publications, he has published in trade publications including the ABA Banking Journal, American Marketing Association, Computing Technology Industry Association, Global Retail Management, Hospital Information Technology Europe and Retail Information Systems News. He has been quoted in publications including Bloomberg, Business Week, Chain Store Age, The Chicago Tribune, CNN Business, Inc. magazine, MIT Technology Review, The San Jose Mercury News, Time magazine, USA Today and The Wall Street Journal. He has also appeared as a guest analyst on the CBS Evening News and Early Show and ABC World News, as well as lectured at conferences around the globe. Steven received his BSBA, JD, and LLM from Boston University.
Martin Paul Block Professor Emeritus, Integrated Marketing Communications, Northwestern University and Executive Director of the Retail Analytics Council. Martin is co-author of ‘Understanding China's Digital Generation, Media Generations: Media Allocation in a Consumer-Controlled Marketplace’, ‘Retail Communities: Customer Driven Retailing, Analyzing Sales Promotion, Business-to-Business Market Research’ and ‘Cable Advertising: New Ways to New Business’. He has also been published in many academic research journals and trade publications and is the author of several book chapters. His PhD is from Michigan State University.
