Using Bayesian demand modeling and mixed-integer programming to set optimal prices for a new product line — balancing revenue, cannibalization, and market share
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Why retailers can't escape markdown pricing—a deep dive into the lifecycle, inventory, and pricing constraints that shape modern retail optimization strategies
Understanding why retail revenue functions are concave, how this property enables convex optimization, and the network flow interpretation that powers modern markdown pricing algorithms
How retailers transform predictive demand models into prescriptive pricing decisions through stochastic programming, jointly optimizing markdown prices and inventory allocation under uncertainty