Search

This paper empirically investigates how marketers can retarget consumers based on their behaviors in the previous search process, a practice we call “search-based retargeting”. To infer the relationship between search activities and preferences of retargeted consumers, we develop a dynamic consumer search model that is based on Weitzman’s model (1979) but it incorporates several key modifications. Based on the model estimation results, we explore the effectiveness of retargeting by studying two types of marketing actions. We find that, when the marketing action is to recommend a seller and provide full seller information, the retargeting value is high for consumers with intensive search and for sellers with high conversion rates. When the marketing action is couponing, it is more useful for a seller to target “own” customers rather than competitors’ customers who have previously clicked into other sellers’ pages but not his. The retargeting value is high for consumers who have searched multiple sellers and have searched the seller first in the search process, and for sellers with high click rates. By charging sellers for either the seller recommendation or the coupon delivery, the online retail platform will obtain a substantial profit gain, and sellers and consumers are also benefited. However, the gains from the seller recommendation are significantly higher, suggesting that providing the seller information is more important as a retargeting strategy.

The operations management discipline has made substantial progress developing analytical models to help firms make tactical and strategic decisions (e.g., inventory control, assortment planning). The application of these models to real contexts requires several inputs, such as an estimate of the benefits and costs of various levels of the operational decision under study. It is often the case that the associated benefits depend on how customers react to changes in the operational decisions being modeled. This in turn requires an empirical validation of the consumer model that serves as an input when making operation decisions. Bringing together researchers from economics, marketing and operations management, this paper reviews and discusses recent findings in terms of modeling and empirically validating consumer choices in response to operational decisions. We structure this discussion along four major operational decisions and issues: product variety (including inventory and assortment), service capacity, pricing and supply chain coordination. For each of these areas, we review recent work and identify challenges that we believe are important to address in future research aimed at connecting operations management research to practice.

Marketing researchers have documented significant biases in estimated price elasticities from brand choice models that ignore state dependence effects. Yet empirical research on firm behavior has thus far ignored the effects of state dependence. In this paper we investigate the supply side consequences of ignoring state dependence using household-level data from the cereal category. Our key results are as follows: We find evidence of both positive (i.e. inertia) and negative (i.e. variety-seeking) state dependence in the cereal category. Ignoring state dependence in demand biases inferences about firm behavior. Specifically, we attribute the observed prices in the cereal category to tacit collusion between manufacturers, when in fact accounting for the effects of state dependence shows that competitive behavior is indeed non-cooperative. We find that firms are forward looking in that they incorporate the effect of their current prices on future profits. However, the relative "bang for the buck," in terms of improvements in prediction and fit, is much greater from modeling state dependence in demand than from modeling inter-temporal firm behavior on the supply side.