Optimizing Click-Through in Online Rankings for Partially Anonymous Consumers∗

Optimizing Click-through in Online Rankings for Partially Anonymous Consumers∗ Babur De los Santosy Sergei Koulayevz May 28, 2013 First Version: December 21, 2011 Abstract The vast amount of information available online has revolutionized the way firms present consumers with product options. Presenting the best alternatives first reduces search costs asso- ciated with a consumer finding the right product. We use novel data on consumer click-stream behavior from a major web-based hotel comparison platform to estimate a random coefficient discrete choice model and propose an optimal ranking tailored to anonymous consumers that differ in their partially revealed price sensitivity. We are able to customize rankings by relating price sensitivity to request parameters, such as the length of stay, number of guests, and day of the week of the stay. In contrast to a myopic popularity-based ranking, our model accounts for the rapidly changing prices that characterize the hotel industry and consumers' search refinement strategies, such as sorting and filtering of product options. We propose a method of determining the hotel ordering that maximizes consumers' click-through rates (CTR) based on the information available to the platform at that time, its assessment of consumers' preferences, and the expected consumer type based on request parameters from the current visit. We find that CTRs almost double when consumers are provided with customized rankings that reflect the price/quality trade-off inferred from the consumer's request parameters. We show that the optimal ranking results in an average consumer welfare 173 percent greater than in the default ranking. Keywords: consumer search, hotel industry, popularity rankings, platform, collaborative filtering, click-through rate, customization, sorting, filtering, search refinement ∗A previous version of the paper was circulated under the title: \Using Consumer Preferences to Improve upon Popularity Rankings in Online Markets." We thank Elizabeth Honka, Ali Horta¸csu,Joowon Kim, Matthijs Wilden- beest, and participants of the Industrial Organization Society sessions at the Allied Social Sciences Association annual meeting and the Workshop of Switching and Search Costs in Moscow, Russia for helpful comments. yKelley School of Business, Indiana University, E-mail: [email protected]. zKeystone Strategy, E-mail: [email protected]. 1 1 Introduction In recent years, Internet commerce has achieved significant advances in the reach and complexity of recommendation systems that shape consumer choices in almost all areas of commerce: from looking for product information in online search-engine results to comparing and selecting consumer electronics, books, mortgages, hotel rooms, and flights.1 Although the problem of optimal recommendation is not new|salespeople everywhere have long struggled with which product to endorse to a particular consumer|the vast amount of consumer information generated online has revolutionized the way firms collect, analyze, and customize product choices. At the core of product recommendations is the challenge of matching a set of products to a set of consumers whose tastes are heterogeneous and often unobserved. The accuracy of the match depends on how firms leverage their available information to infer consumer preferences for products. For example, Amazon makes recommendations in diverse product categories by exploiting product correlations present in other customers' transaction histories. Although potential buyers' preferences for a new product are unobservable, the recommendation system is based on the notion that preferences for various products are similar across the set of customers who bought or rated other products in a similar way.2 A major challenge for a recommendation system is to serve the category of partially anonymous consumers. Such customers are anonymous to the platform and their past behavior is unobserved (including both their search and clicking behavior) but relevant observable information is available in the current customer visit. For instance, a search request for a hotel room in an online travel platform, which includes the date of stay and the number of guests, reveals whether the consumer is more likely to be a business traveler if the stay is during weekdays, the room request is for single occupancy, and the number of days in advance of the stay the request was made. The premise of this paper is that even though preferences of a particular consumer are unobserved, a significant portion of heterogeneity can be inferred from the request parameters, and can be used to offer a customized 1See, for example, Ansari and Mela (2003) for a discussion of customization and targeting options available to online sellers. 2This is also the underlying assumption of collaborative filtering and other hybrid methodologies would likely improve product rankings and thus reduce search costs for consumers. Examples of these models include Anderson, Ball, Boley, Greene, Howse, Lemire, and Mcgrath (2003); Basilico and Hofmann (2004); Huang, Zeng, and Chen (2004); Melville, Mooney, and Nagarajan (2001); Vozalis and Margaritis (2008); Yu, Schwaighofer, and Tresp (2003). For a nice example of collaborative filtering discussion and application see Moon and Russell (2008). 2 ranking to the customer. Other information about the consumer that can be used to improve and customize ranking might include location of the user connection, type of browser, IP address, etc. and the consumer's browsing behavior. A recent example of the use of such information is the new ranking strategy of travel site Orbitz, which, after observing that Mac users spend more on hotel rooms, decided to present pricier options to consumers who were searching from a Mac.3 In this paper we use highly detailed data on consumer search and click-stream behavior within a major web-based hotel comparison platform to propose an optimal ranking strategy for partially anonymous users.4 Platforms such as Orbitz, Kayak, or Travelocity act as intermediaries between hotels and consumers: they collect information about available hotel options and present them in an organized way.5 The goal of this paper is to maximize the aggregate click-through rate (CTR) for the presented choices, where a click indicates a consumer's interest and potentially a decision to purchase.6 The optimal ranking is derived from a discrete choice model of consumer choice. It models a click on a particular hotel, conditional on the hotel's observed attributes, such as star rating or price, and conditional on the consideration set of hotels observed in the search process. In this framework, the CTR is maximized by ranking products in the order of decreasing expected utility for a given consumer. Indeed, an extant marketing literature on consumer choice has shown that the distribution of heterogeneous tastes plays an important role in the substitution patterns of consumer demand, which are at the core of the recommendation problem. For instance, Ansari et al. (2000), Belanger (2005), Burke (2000), Ricci et al. (2011), and Ghose et al. (2012) use utility based approaches to build recommendations. However methods of estimating attribute weights in the utility ranking vary across these studies. The closest to our approach is work by Ghose et al. (2012), who incorporate user-generated content (e.g., online hotel reviews) into the ranking mechanism. 3Wall Street Journal, June 26, 2012. 4See Bucklin, Lattin, Ansari, Bell, Coupey, Gupta, Little, Mela, Montgomery, and Steckel (2002) for a helpful discussion of research on the Internet using clickstream data. 5The influence of these online travel agencies has greatly increased over time. In 2010 almost a half of all non-conference bookings in the United States were made online, and 75 percent were influenced by online search. 6CTR is a standard measure of product relevance, although it is usually complemented with other metrics, such as user reviews. A CTR is defined as the ratio of the number of clicks to the number of impressions (displays) that a product received during a given time period. Although short-term profit objectives may not entirely coincide with click maximization, our ranking is consistent with the long-term success of a search platform, where consumer satisfaction and retention is a key factor. 3 This study makes two important contributions to the existing literature. First, our ranking exploits information about taste heterogeneity contained in the search parameters, as opposed to a ranking based on the average consumer. This information allows us to categorize consumers into different types and present a customized ranking. We estimate a discrete distribution of unobserved consumer types, and depending on current hotel prices, an optimal ranking is chosen that targets one of these types. Because CTR is non-linear in the unobserved component, the optimal ranking does not necessarily target the median type. For instance, prioritizing more expensive, higher- quality products may result in click gains among quality-centered consumer types that do not outweigh expected click losses among price-sensitive consumer types. Second, we explicitly account for the endogeneity of search refinements on the ranking, such as sorting and filtering of search results, when deriving the optimal ranking. Search preference is an important factor, as the composition of consideration sets is a product of both the default product ranking and user search refinements.7 For example, searchers who sort hotels by increasing

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