Dynamic Content Variation with Customer Learning

Problem Definition: We analyze a firm that sells repeatedly to a customer population over multiple periods. While this setting has been studied extensively in the context of dynamic pricing—selling the same product in each period at a dynamically varying price—we consider a different approach: dynamic content variation wherein the product is sold at the same price in every period, but the firm may vary the content of its product over time. Customers learn about their utility on purchasing and decide whether to repeat a purchase in each future period. The firm faces a budget for the total value of content it can use over a finite planning horizon, and decides how to allocate this budget to each period to maximize revenue.Academic/Practical Relevance: A number of new business models, such as video streaming services and curated subscription boxes, face the situation that we model. Our results show how such firms can optimally use dynamic content variation to increase their revenues.Methodology: We use an analytical model in which rational customers decide whether to purchase in a period and a firm determines an optimal content allocation policy to maximize revenue. Results: The firm's optimal allocation policy is not, in general, constant over time, but is monotone. Specifically, content value should increase during a planning horizon if customer heterogeneity is low and decrease otherwise. Across successive planning horizons, content value is cyclical with periods of high and low value, but unless the market growth rate is consistently high (e.g., exponential), content value will converge to a constant and equal allocation in every period as more customers learn their utility.Managerial Implications: We show how firms can dynamically vary content to increase revenues. Thus, managers at such firms may engage in dynamic content variation, instead of dynamic pricing, as a revenue management strategy