Location, Location, Location: An Analysis of Profitability of Position in Online Advertising Markets

Agarwal, Ashish; Hosanagar, Kartik; Smith, Michael D.

doi:10.1509/jmr.08.0468

Cited by 310 publications

(201 citation statements)

References 58 publications

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“…A myriad of researchers have turned to SSA data to uncover new insights about consumer search (Ghose and Yang, 2009;Rutz and Bucklin, 2011), choice and related purchasing behaviors (Jeziorski and Segal, 2009;Yang and Ghose, 2010;Agarwal et al, 2011) and advertiser/search engine strategies (Animesh et al, 2009;Yao and Mela, 2011;Rutz et al, 2012). Many of these papers have used random utility models to study the effect of ad position, keyword length, presence or absence of brand name, etc.…”

Section: Introductionmentioning

confidence: 99%

Aggregation Bias in Sponsored Search Data: The Curse and the Cure

2015

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There has been significant recent interest in studying consumer behavior in sponsored search advertising (SSA). Researchers have typically used daily data from search engines containing measures such as average bid, average ad position, total impressions, clicks and cost for each keyword in the advertiser's campaign. A variety of random utility models have been estimated using such data and the results have helped researchers explore the factors that drive consumer click and conversion propensities. However, virtually every analysis of this kind has ignored the intra-day variation in ad position. We show that estimating random utility models on aggregated (daily) data without accounting for this variation will lead to systematically biased estimates -specifically, the impact of ad position on click-through rate (CTR) is attenuated and the predicted CTR is higher than the actual CTR. We demonstrate the existence of the bias analytically and show the effect of the bias on the equilibrium of the SSA auction. Using a large dataset from a major search engine, we measure the magnitude of bias and quantify the losses suffered by the search engine and an advertiser using aggregate data. The search engine revenue loss can be as high as 11% due to aggregation bias. We also present a few data summarization techniques that can be used by search engines to reduce or eliminate the bias.

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Section: Introductionmentioning

confidence: 99%

Aggregation Bias in Sponsored Search Data: The Curse and the Cure

2015

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“…Ghose et al (2014) show that hotels with lower customer ratings received less clicks than higher-ranked competitors. Examining an online retailing environment, Agarwal et al (2011) found that top positions had a higher CTR but this did not automatically translate into higher conversion rates. Recently, Van der Rest et al (2016) find a positive relationship between the probability that a hotel is booked and the hotel's rank on the landing page of an OTA website's customer search result.…”

Section: Introductionmentioning

confidence: 94%

From OTA interface design to hotels’ revenues: the impact of sorting and filtering functionalities on consumer choices

Loosschilder¹,

Rest

Schwartz

et al. 2016

J Revenue Pricing Manag

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Gerard Loosschilder is a market research practitioner with extensive experience on the client, agency and academic side. Having held the position of Chief Methodology Officer at a highly quantitative research firm, Gerard Loosschilder is comfortable with advanced research approaches including conjoint analysis. Yet, from working at the client side (Philips Electronics) and working with clients, he learned to care most about partnering with businesses to make sure that they understand study results and act on it. That's why Gerard Loosschilder explores creative ways of generating insights and encouraging businesses to act on them. Loosschilder has a PhD from Delft University of Technology, Delft, The Netherlands. Presently, Loosschilder has his own consultancy firm. Zvi Schwartz's scholarly research and industry consulting focus on the core technical and strategic elements of the hospitality revenue management cycle: forecasting, optimization and monitoring, as well as the closely related topics of strategic pricing, and consumer and firm decisions in advanced reservation environments. Recent projects explored novel hotel forecasting approaches, occupancy forecasting accuracy measures, manipulation of hotel competitive sets, overbooking optimization and revenue management performance measures. He received his doctoral degree from Purdue University and holds an MBA and a bachelor's degree in Economics.Paolo Cordella is Manager at the Advanced Analytics Centre of Excellence at LEGO. In his previous role at as consultant at SKIM, a market research agency, he specialized in choice modelling, conjoint analysis and ABSTRACT Using conjoint analysis and choice data from 1492 Dutch participants, this experimental study explores the impact of user interface functionalities on hotels' customer online behavior and the subsequent economic ramifications for both the search engine service providers and their hotel clients. Specifically, it explores the impact of sorting and filtering on the relationship between a hotel's placements on the initial search results booking page and the likelihood of being booked. The findings indicate that the availability of sort and filter functions generates a more balanced distribution of booking choices, as users pay more attention to the hotel characteristics that are subject to sorting and filtering functionality. If the sort and filter functions are applied to price, visitors are more likely to choose cheaper rooms, whereas when applied to customer ratings, visitors are more likely to choose rooms with better ratings. The functions affect the search agenda and consequently the economic value of placement in top positions. In addition, sorting and filtering increase the competitiveness of the search engine because it encourages users to apply additional choice criteria beyond merely relying on the hotel's placement on the search result page.

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“…Research has in fact shown that people avoid advertising, irrespectively of the position on the screen where the advert is placed [6].…”

Section: Introductionmentioning

confidence: 99%

MyAds—A Proposed Adaptive Social Online Advertising Framework

Qudah¹,

Cristea²

2013

JOEBM

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Abstract-The seemingly 'magical' developments of the World Wide Web have challenged the computer science researchers, analysts, designers and developers to think outside the box and apply their creativity. One of the main sectors that have paid great attention to the World Wide Web is the advertisement sector. Online advertisement is known to be one of the major incomes for many companies, social networks and search engines online. This paper proposes a framework that provides adaptive advertisements to be displayed to online users based on their social network profiles via creating an individual user model for each user. The framework was built based on literature research and an exploratory study conducted with actual web users. The framework is a layered model, where each layer provides a different functionality, but works towards a homogenous outcome of adaptive ads.

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Location, Location, Location: An Analysis of Profitability of Position in Online Advertising Markets

Cited by 310 publications

References 58 publications

Aggregation Bias in Sponsored Search Data: The Curse and the Cure

Aggregation Bias in Sponsored Search Data: The Curse and the Cure

From OTA interface design to hotels’ revenues: the impact of sorting and filtering functionalities on consumer choices

MyAds—A Proposed Adaptive Social Online Advertising Framework

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