Intertemporal Content Variation with Customer Learning

Abstract

Problem definition: We analyze a firm that sells repeatedly to a customer population over multiple periods. Although this setting has been studied extensively in the context of dynamic pricing—selling the same product in each period at a varying price—we consider intertemporal content variation, wherein the price is the same in every period, but the firm varies the content available over time. Customers learn their utility on purchasing and decide whether to purchase again in subsequent periods. The firm faces a budget for the total amount of content available during a finite planning horizon, and allocates content to maximize revenue. Academic/practical relevance: A number of new business models, including video streaming services and curated subscription boxes, face the situation we model. Our results show how such firms can use content variation to increase their revenues. Methodology: We employ an analytical model in which customers decide to purchase in multiple successive periods and a firm determines a content allocation policy to maximize revenue. Results: Using a lower bound approximation to the problem for a horizon of general length T, we show that, although the optimal allocation policy is not, in general, constant over time, it is monotone: content value increases over time if customer heterogeneity is low and decreases otherwise. We demonstrate that the optimal policy for this lower bound problem is either optimal or very close to optimal for the general T period problem. Furthermore, for the case of T = 2 periods, we show how two critical factors—the fraction of “new” versus “repeat” customers in the population and the size of the content budget—affect the optimal allocation policy and the importance of varying content value over time. Managerial implications: We show how firms that sell at a fixed price over multiple periods can vary content value over time to increase revenues.

Uncovering the trends and developments in subscription business models through bibliometric analysis

“…Mathematical modelling (Carroni and Paolini, 2020;Huotari and Ritala, 2021;Schiff, 2003;Wang et al, 2014) Propensity Score Matching (Oestreicher-Singer and Zalmanson, 2013) PLS-SEM (Kang et al, 2014;Khuntia et al, 2017;Parry et al, 2012;Tani et al, 2022) Simulation (Xu and Duan, 2018) Analytical modelling (Bernstein et al, 2022;Feng et al, 2022;Li et al, 2019) Prototype Evaluation (Liu and Author's keywords are selected by the authors of a research paper or article to represent the main themes, concepts or ideas explored in their work. These keywords are typically listed at the end of the abstract or introduction section and help to highlight the primary focus of the study.…”