Sequential Search with Refinement: Model and Application with Click-Stream Data

Abstract

We propose a structural model of consumer sequential search under uncertainty about attribute levels of products. Our identification of the search model relies on exclusion restriction variables that separate consumer utility and search cost. Because such exclusion restrictions are often available in online click-stream data, the identification and corresponding estimation strategy is generalizable for many online shopping websites where such data can be easily collected. Furthermore, one important feature of online search technology is that it gives consumers the ability to refine search results using tools such as sorting and filtering based on product attributes. The proposed model can integrate consumers’ decisions of search and refinement. The model is instantiated using consumer click-stream data of online hotel bookings provided by a travel website. The results show that refinement tools have significant effects on consumer behavior and market structure. We find that the refinement tools encourage 33% more searches and enhance the utility of purchased products by 17%. Most websites by default rank search results according to their popularity, quality, or relevance to consumers (e.g., Google). When consumers are unaware of such default ranking rules, they may engage in disproportionately more searches using refinement tools. Consequently, overall consumer surplus may deteriorate when total search cost outweighs the enhanced utility. In contrast, if the website simply informs consumers that the default ranking already reflects product popularity, quality, or relevance, consumers search less and their surplus improves. We also find that refinement tools lead to a less concentrated market structure. This paper was accepted by J. Miguel Villas-Boas, marketing.

Targeted Search and Platform Design

“…One of their key findings, that relevance ranking increases platform revenue, is consistent with our model prediction. Chen and Yao (2016) find that refinement tools increase consumer utility of purchased products, also consistent with our model prediction. Ursu (2016) shows that rankings affect consumer search and increase consumer welfare by reducing search cost as well as platform revenue.…”

On the Disclosure of Promotion Value in Platforms with Learning Sellers

“…The phenomenon that consumers are more likely to consider and purchase products that are ranked higher or given more prominence on a webpage has been documented empirically in many settings; see, e.g. Kim et al (2010) and Chen and Yao (2016) in the context of consumer products, as well as Besbes et al (2016) in the context of content recommendations in media sites. Moreover, the implications of consumer search behavior on optimal product rankings has been studied given different assumptions on the consumer search behavior.…”

Optimal Search and Awareness Expansion

“…Given the prevalence of ranking effects in click-stream data, empirical studies have proposed an approach to alleviate this problem (e.g. Chen and Yao, 2017;Ursu, 2018). In this approach, search costs are modeled such that they depend on the position at which the alternatives are revealed to the consumer.…”