Abstract
This paper conceptualizes a consumer-centric, regenerative artificial intelligence (“ReGenAI”) model for the Fast-Moving Consumer Goods (“FMCG”) retailing channel. The system uses its awareness of context, time, and users to (re)generate customer touchpoints and other marketing communications. Its output provides deep insights into regular and altered FMCG customer journeys, such as shopping behaviors under stressors like lifestyle choices or cataclysmic socio-economic and weather events. The recursive model advances from current, generative AI systems. It uses “tired or inspired” as a simplified bifurcated grocery shopper taxonomy to operationalize customers’ purchasing and consumption behaviors into actionable data for demand planning and retail operations.
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No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
Russell J. Zwanka
Russell J. Zwanka is the director of the Food Marketing Program and professor of category management and food marketing at Western Michigan University, one of the top Food Marketing programs in the world. Delivering high-quality curriculum and applied food marketing skills, along with the Food Industry Research and Education Center, Western Michigan University endeavors to work with the food industry to provide real-time solutions while also helping educate the future leaders of the food industry.
Marcel M. Zondag
Marcel M. Zondag is an associate professor of marketing and supply chain management, the Supply Chain Management Program director, and the Food Industry Research & Education (FIRE) Center director at Western Michigan University. The Supply Chain Management Program is ranked 14th nationwide by Gartner. The FIRE center works closely with industry and academic partners worldwide, providing thought leadership in food marketing and supply chain management.