A Practical Price Optimization Approach for Omnichannel Retailing

Abstract

Consumers are increasingly navigating across sales channels to maximize the value of their purchase. The existing retail practices of pricing channels either independently or matching competitor prices are unable to achieve the desired profitable coordination between channels. We engaged with three major retailers over two years and developed omnichannel pricing (OCP) solutions in partnership with IBM Commerce to overcome these challenges. We implement an integrated data processing and machine learning framework that enables estimation of location-specific, cross-channel price elasticities and competitive effects. We develop an integrated OCP optimization formulation to profitably coordinate prices for nonperishable products offered across channels and store locations while satisfying practical constraints on volume and price. The resultant optimization formulations for discrete choice demand models are nonconvex and NP-hard, and we prescribe practically efficient mixed-integer programs that can be used to recover (near) optimal solutions. An OCP implementation in two categories for a major retail chain projected a 7% profit lift while preserving the sales volume. This benefit was achieved by lowering online prices and optimally raising and lowering location-specific store prices. This integrated pricing approach allows the retailer to be competitive and preserve market share without aggressively matching the low price of e-tail giants.

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