The optimal choice of pre-launch reviewer

Abstract

We consider the impact of pre-launch reviews on sales of products of quality unknown to consumers. Sales occur simultaneously after consideration by a reviewer with a known level of bias. Consumers observe the reviewer's decision and a private signal. Using convex analysis we prove three major results: (i) a monopolist, which can choose the type of reviewer and the price conditional on the chosen reviewer's decision, will always want to have its product reviewed; (ii) choosing an extreme reviewer, that is either the toughest or softest reviewer type available, is always optimal; and (iii) the switch from the toughest to softest reviewer occurs when the precision of private information reaches a calculable threshold. The paper provides a justification for the existence of heavily biased reviews and tests, and also suggests a clear course of action for firms in different situations. For example, an entrant with a very innovative product should risk a tough pre-launch test, while an incumbent with a new product that provides only a small change over an existing one would do best using a soft pre-launch review.

Demand for Slant: How Abstention Shapes Voters' Choice of News Media

“…He discusses how priors, preferences and coarseness of observation interact, such that the decisionmaker sometimes chooses to observe experimenters with different preferences. Gill and Sgroi (2012) study a company about to launch a new product. It can publicly test the product and condition its price on whether the product passes or fails.…”

How Should Peer‐review Panels Behave?

“…Following the more general theoretical literature on testing and evaluation, such as Gill and Sgroi (, ), a Bayesian model in this context is simply one that produces a posterior probability, p i , that a given submitted paper indexed by i is of type a rather than type b . An external rule could then be used to further divide papers into categories: for example if we wanted four categories as indicated by the REF on page 10 of the consultation document we could make use of four ordered threshold probabilities { p r }, where r ∈ {1, 2, 3, 4}.…”

“…16 For just a few from a pool of many possible examples in the literature on evaluation, see Calvert (1985), Sah and Stiglitz (1986), Taylor (1999), Sgroi (2002), Farhi et al (2005), Lerner and Tirole (2006), Chiao et al (2007), Gill and Sgroi (2008) and Demange (2010) all of which use simple binary models. Gill and Sgroi (2012) have some binary features but take random draws to be from a continuous quality-dependent signal distribution and offer a way to generalise the framework in this article though at the cost of simplicity and tractability. The methods suggested here are general.…”

“…(), Gill and Sgroi () and Demange () all of which use simple binary models. Gill and Sgroi () have some binary features but take random draws to be from a continuous quality‐dependent signal distribution and offer a way to generalise the framework in this article though at the cost of simplicity and tractability.…”

Selling in Advance to Loss Averse Consumers

“…Investigating a firm's incentive to provide consumers with information about its product's characteristics, Lewis and Sappington (), Bar Isaac et al. (), and Gill and Sgroi () show that a monopolistic seller may often find it optimal to provide consumers with either full information or none. The limiting cases of an uninformed () and an informed market () therefore deserve some special attention: Corollary Without consumer loss aversion, a monopolist's inter‐temporal allocation of sales is independent of the quality of consumers' information.…”