Dynamic Pricing and Learning under the Bass Model

Most of the dynamic pricing and learning literature has focused on a relatively simple setting where given current pricing decision, demand is independent of past actions and demand values. With the evolution of online platforms and marketplaces, the focus on such homogeneous modeling environments is becoming increasingly less realistic. For example, platforms now rely more and more on online reviews and ratings to inform and guide consumers. Product quality information is also increasingly available on online blogs, discussion forums, and social networks, that create further word-of-mouth effects. One clear implication on the dynamic pricing and learning problem is that the demand environment can no longer be assumed to be static; for example, in the context of online reviews, sales of the product trigger reviews/ratings, and these in turn influence subsequent demand behavior etc. To that end, product diffusion models, such as the popular Bass model [1, 2], are known to be extremely robust and parsimonious, capturing aforementioned word-of-mouth and imitation effects on the growth in sales of a new product. The Bass model describes the process by which new products get adopted as an interaction between existing users and potential new users. It creates a state-dependent evolution of market response which is well aligned with the impact of recent technological developments, such as online review platforms, on the customer purchase behavior.