Knowledge that Transforms

Whereas the extant literature on entry-order effects establishes that first entrants often earn higher market shares (“market-share advantage”), the literature on distribution suggests that increased distribution has a positive effect on sales. Can distribution help us better understand entry-order effects on market shares? This paper examines how the first entrant in a geographical market achieves a market-share advantage through distribution. For this purpose, I propose a simple method of decomposing sales into physical distribution and sales performance. The data come from a manually collected panel on six major Japanese convenience-store chains from 47 geographical markets between 1991 and 2007. Using an instrumental variable approach to address the potential endogeneity of entry order, I find first entrants have a positive market-share advantage over later entrants. Specifically, the physical distribution, measured by the number of outlets in a market, drives most of the advantage. Meanwhile, the positive effect on sales performance for the first chain brand becomes nonexistent when I control for the outlet density. This paper further finds that the density of own outlets is nonmonotonically (inverted U) related to sales performance per outlet, suggesting dynamic outlet expansion faces a trade-off between the business-stealing effect in a chain (“cannibalization”) and the advertising effect through repetition. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1029 .

It has been shown that a monopolist can use advance selling to increase profits. This paper documents that this may not hold when a firm faces competition. With advance selling a firm offers its service in an advance period, before consumers know their valuations for the firms’ services, or later on in a spot period, when consumers know their valuations. We identify two ways in which competition limits the effectiveness of advance selling. First, while a monopolist can sell to consumers with homogeneous preferences at a high price, this homogeneity intensifies price competition, which lowers profits. However, the firms may nevertheless find themselves in an equilibrium with advance selling. In this sense, advance selling is better described as a competitive necessity rather than as an advantageous tool to raise profits. Second, competition in the spot period is likely to lower spot period prices, thereby forcing firms to lower advance period prices, which is also not favorable to profits. Rational firms anticipate this and curtail or eliminate the use of advance selling. Thus, even though a monopolist fully exploits the practice of advance selling, rational firms facing competition either mitigate it or avoid it completely. The online appendix is available at https://doi.org/10.1287/mksc.2016.1006 .

Marketing Science greatly benefited from the admirable and fastidious efforts of more than 200 different individuals who provided manuscript reviews last year. Beyond those individuals already recognized on the editorial board, the editor-in-chief and senior/guest editors of Marketing Science are indebted to the many guest editors-in-chief, guest associate editors, and ad hoc reviewers who provided expert counsel and guidance on a voluntary basis. The following list acknowledges the contribution of guest editors-in-chief, guest associate editors, and ad hoc reviewers who served from January 1, 2015 to December 31, 2015. Finally, let us not forget to thank the authors. Marketing Science requires and receives outstanding submissions from many leading researchers and prestigious organizations. K. Sudhir Yale University

Consumers’ preferences can often be represented using a multimodal continuous heterogeneity distribution. One explanation for such a preference distribution is that consumers belong to a few distinct segments, with preferences of consumers in each segment being heterogeneous and unimodal. We propose an innovative approach for modeling such multimodal distributions that builds on recent advances in sparse learning and optimization. We apply the model to conjoint analysis where consumer heterogeneity plays a critical role in determining optimal marketing decisions. Our approach uses a two-stage divide-and-conquer framework, where we first divide the consumer population into segments by recovering a set of candidate segmentations using sparsity modeling, and then use each candidate segmentation to develop a set of individual-level heterogeneity representations. We select the optimal individual-level heterogeneity representation using cross-validation. Using extensive simulation experiments and three field data sets, we show the superior performance of our sparse learning model compared to benchmark models including the finite mixture model and the Bayesian normal component mixture model. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0992 .

We study the effects of information content in 59,814 pharmaceutical sales calls on doctors’ prescription decisions for statins, in the face of entry of competing brands and generics, using a hierarchical Bayesian distributed lag model. We conclude that adding information content to the prescription response model improves the in- and out-of-sample performance of the model. In the first six months following generic entry, it is more effective for incumbent brands to detail on drug contraindications and indications, compared to other periods, to positively differentiate from generics. In the first six months following branded entry, it is less effective for incumbent brands to detail on drug indications and costs, given increased competitive clutter. We also document substantial heterogeneity among doctors in their response to information content. Our model is helpful for analysts to more accurately assess the effectiveness of detailing. Our empirical results are also informative for drug manufacturers as they set or change their messaging policies in response to entry and help firms to tailor their message content at the doctor level. Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2015.0971 .

The marketing and operations disciplines have increasingly accounted for the presence of strategic consumer behavior. Theory suggests that such behavior exists when consumers are able to consider future distribution of prices, and that this behavior exposes firms to intertemporal competition that results with a downward pressure on prices. However, deriving future distribution of prices is not a trivial task. Online decision support tools that provide consumers with information about future distributions of prices can facilitate strategic consumer behavior. This paper studies whether the availability of such information affects transacted prices by conducting an empirical analysis in the context of the airline industry. Studying the effect at the route level, we find significant price reduction effects as such information becomes available for a route, both in fixed-effects and difference-in-differences estimation models. This effect is consistent across the different fare percentiles and amounts to a reduction of approximately 4%–6% in transactions’ prices. Our results lend ample support to the notion that price prediction decision tools make a statistically significant economic impact. Presumably, consumers are able to exploit the information available online and exhibit strategic behavior. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0965 .