Demand Learning and Pricing for Varying Assortments

Problem Definition: We consider the problem of demand learning and pricing for retailers who offer assortments of substitutable products that change frequently, e.g., due to limited inventory, perishable or time-sensitive products, or the retailer’s desire to frequently offer new styles. Academic/Practical Relevance: We are one of the first to consider the demand learning and pricing problem for retailers who offer product assortments that change frequently, and we propose and implement a learn-then-earn algorithm for use in this setting. Our algorithm prioritizes a short learning phase, an important practical characteristic that is only considered by a few other algorithms. Methodology: We develop a novel demand learning and pricing algorithm that learns quickly in an environment with varying assortments and limited price changes by adapting the commonly used marketing technique of conjoint analysis to our setting. We partner with Zenrez, an e-commerce company that partners with fitness studios to sell excess capacity of fitness classes, to implement our algorithm in a controlled field experiment to evaluate its effectiveness in practice using the synthetic control method. Results: Relative to a control group, our algorithm led to an expected initial dip in revenue during the learning phase, followed by a sustained and significant increase in average daily revenue of 14–18% throughout the earning phase, illustrating that our algorithmic contributions can make a significant impact in practice. Managerial Implications: The theoretical benefit of demand learning and pricing algorithms is well understood—they allow retailers to optimally match supply and demand in the face of uncertain pre-season demand. However, most existing demand learning and pricing algorithms require substantial sales volume and the ability to change prices frequently for each product. Our work provides retailers who do not have this luxury a powerful demand learning and pricing algorithm that has been proven in practice.