Marketplace Design

Advertising supported content sampling is ubiquitous in online markets for digital information goods. Yet, little is known about the profit impact of sampling when it serves the dual purpose of disclosing content quality and generating advertising revenue. This paper proposes an analytical framework to study the optimal content strategy for online publishers and shows how it is determined by characteristics of both the content market and the advertising market.

To date the effectiveness of inducing lower-calorie choices by providing consumers with calorie information has yielded mixed results. Here four controlled experiments show that adding dish-specific calorie information to menus (calorie posting) tends to result in lower-calorie choices. However, additionally grouping low-calorie dishes into a single "low-calorie" category (calorie organizing) ironically diminishes the positive effects of calorie posting. This outcome appears to be caused by the effect that grouping low-calorie options has on consumers' consideration sets.

Many factors introduce the prospect of changes for the demand environment that a firm faces, with the specifics of such changes not necessarily known in advance. If and when realized, such changes affect the delicate balance between demand and supply and thus should be anticipated to the extent possible. We study the dynamic pricing problem of a retailer facing the prospect of a change in the demand function during a finite selling season with no inventory replenishment opportunity.

We study the drivers of the emergence of opinion leaders in a networked community where users establish links to others, indicating their "trust" for the link receiver's opinion. This leads to the formation of a network, with high in-degree individuals being the opinion leaders. We use a dyad-level proportional hazard model with time-varying covariates to model the growth of this network. To estimate our model, we use Weighted Exogenous Sampling with Bayesian Inference, a methodology that we develop for fast estimation of dyadic models on large network data sets.

We consider a repeated newsvendor problem in which the decision-maker (DM) does not have access to the underlying distribution of discrete demand. We analyze three informational settings: i) the DM observes realized demand in each period; ii) the DM only observes realized sales; and iii) the DM observes realized sales but also a lost sales indicator that records whether demand was censored or not. We analyze the implications of censoring on performance and key characteristics that effective policies should possess.

Should advertising be approached differently in emerging than in developed markets? Using data from 256 TV commercial tests conducted by a multinational FMCG company in 23 countries, we consider two routes of persuasion: a functional route, which emphasizes the features and benefits of a product, and an experiential route, which evokes sensations, feelings, and imaginations. Whereas in developed markets the experiential route mostly drives persuasion, the functional route is a relatively more important driver in emerging markets.

A high quality customer database is a cornerstone of successful interactive marketing strategies and tactics. Based on the notion that customer data quality is not only a technical but also an organizational problem, this study develops and tests an organizational learning framework of the relationship between organizational processes, customer data quality and firm performance. The findings show that high quality customer data impact both customer and business performance and that the most important driver of customer data quality comes from the executive suite.