EROSKI: Artificial Intelligence for Pricing and Electronic Shelf Labels
Eroski is challenged with modernizing grocery pricing using AI and electronic shelf labels without eroding trust and fairness. Students analyze price elasticity, revenue versus profit objectives, price zones, competitive price matching, and human-in-the-loop governance to determine the optimal pricing strategy.
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Description
Eroski is challenged with modernizing grocery pricing using AI and electronic shelf labels without eroding trust and fairness. Students analyze price elasticity, revenue versus profit objectives, price zones, competitive price matching, and human-in-the-loop governance to determine the optimal pricing strategy.
Learning Objective
This case study brings students to the forefront of artificial intelligence in retail pricing. Eroski, a Spanish supermarket chain based in the Basque Country, uses price zones, elasticity-based optimization, price discrimination, human-in-the-loop rules, and electronic shelf labels to increase revenue and margins while maintaining fairness, trust, and customer satisfaction. The case includes a real dataset that enables students to estimate price elasticities and determine optimal prices based on revenue or profit maximization.
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