Analysis of Advertising Strategies: Consumer Switching, Competition and Learning

Advertising is considered as an important strategic tool to promote product and improve sales. Extensive research has been devoted to advertising strategies and their effect on product sales. Chiefly because aggregate-level sales data are easy to collect, the prior studies predominately develop and analyze aggregate advertising models which relate product sales to advertising spending under a known sales response function. Nowadays, however, the emergence of Internet, e-commerce and data analytics approaches has made collecting data on individual consumer behavior and real-time sales feasible. Therefore, studying more sophisticated advertising models which can exploit these data is necessary and meaningful. In this dissertation, we consider two dynamic advertising models, one incorporates customer satisfaction and customer switching behavior and the other involves dynamic sales learning.;The first model focuses on the markets of experience goods whose quality levels are unobservable to the buyers. The buyers make the purchase decisions based on their past usage experience of the goods and the advertising outlays of the sellers. We first consider the competitive market where there are multiple brands planning their advertising campaigns. We derive the long-term steady-state equilibrium advertising strategies and market shares of the brands. We study how customer reaction to their past usage experience of the product (satisfaction) affects the sellers' advertising strategies and market shares. We further analyze the monopoly market, where the focus is on the question of whether the monopolist should use even-level advertising or pulsing advertising strategy.;In the second model, we study the dynamic advertising budget allocation problems, in which the relationship between the advertising expenditure and the product sales is unknown to the retailer and the retailer can only learn this information through observing realized sales. We propose nonparametric advertising budget allocation policies for both single- and multi-product problems. We show that such policies are asymptotically optimal. In particular, for the single-product problem, by constructing a lower-bound instance, we show that our policy achieves near-best asymptotic performance.