This is like a smart shopkeeper who remembers each regular customer and quietly adjusts offers and prices based on their habits, loyalty, and sensitivity to price—so they buy more and stay longer, while the shopkeeper still protects their overall profit across the whole supply chain.
Traditional one-size-fits-all pricing leaves money on the table: some customers would pay more, others are overcharged and churn, and promotions are poorly targeted. This work shows how analyzing customer profiles across the supply chain can enable differentiated, personalized pricing that increases profit and improves allocation of discounts and inventory.
Proprietary historical transaction data plus rich customer profiles (behavioral, demographic, loyalty data) that competitors cannot easily copy, embedded in pricing and promotion workflows that become sticky once adopted.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Data quality and granularity of customer-level transaction history across all tiers of the supply chain; organizational and regulatory constraints on using individualized prices.
Early Majority
This approach focuses on integrating customer profile–driven pricing decisions across the entire supply chain (manufacturer, distributor, retailer), rather than treating retail personalization in isolation. It emphasizes analytical modeling of customer heterogeneity and supply chain interactions, not just black-box dynamic pricing at the point of sale.