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The system considers cross-channel behaviors—such as past offline shopping patterns—to better understand individual preferences. It also imposes constraints related to pricing and quantity—for instance, avoiding replacements that are too expensive or too cheap—maintaining quality and customer satisfaction.
Adapting to Data Drift and Local Context
A critical aspect of this system is its capacity to adapt swiftly to data drift, especially post-pandemic. Patterns of stock and substitution preferences evolved during COVID-19, with customers more accepting frozen strawberries or different brands temporarily. Walmart's AI continuously retrains models to stay aligned with local demand and supply dynamics, varying significantly across regions like New York, Hawaii, or California.