Analysis of online position auctions for search engine marketing

Santos, Margarida V. B.; Mota, Isabel; Campos, Pedro

doi:10.1057/s41270-022-00170-x

Cited by 4 publications

(8 citation statements)

References 41 publications

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“…Here, search engine advertising offers advertisers an attractive opportunity to reach targeted audiences at a time when prospects are already thematically engaged and activated (Schultz, 2016). When users click on advertisements, they are directed to the advertiser's website, for which the advertiser pays the search engine (“pay per click”) (Santos et al, 2022; Varian, 2007). Such payment represents the main source of revenue for search engines (Varian, 2016).…”

Section: Research Backgroundmentioning

confidence: 99%

“…The starting point for any mechanism is an advertiser's bid. Under rational decision‐making, the bidding amount should equal the maximum amount that an advertiser is willing to pay for each clicked advertisement (Santos et al, 2022) following the generalized second‐price auction (Edelman et al, 2007; Varian, 2007). Advertisers thus regularly assign different values, and subsequently bidding amounts, to their selected search words.…”

Section: Research Backgroundmentioning

confidence: 99%

“…As mentioned above, advertisers compete for ranking positions on search engine results pages in an automated generalized second price auction (Edelman et al, 2007; Varian, 2007), which can include various additional metrics (Santos et al, 2022). Thus, in view of the research objective, we next address the auction ranking mechanisms of the advertisements on search engine results pages.…”

Section: Research Backgroundmentioning

confidence: 99%

“…Following the above‐mentioned heuristics, advertising companies generally value higher positions on a search engine results page to receive more clicks and thus increase traffic to their target website (Santos et al, 2022). Consequently, bidding for the top position has become the dominant strategy for advertisers (Agarwal et al, 2015; Balachander et al, 2009; Lahaie & Pennock, 2007; Schultz, 2020a).…”

Section: Research Backgroundmentioning

confidence: 99%

“…Following the second‐price paradigm, advertisers generally pay an amount based on the next lowest bid plus an increment for every click on their advertisement. The resulting placement is referred to as “ad rank”; the highest ad rank is assigned to the advertiser who has the best combination of the auctioning criteria (Santos et al, 2022). One consequence of this is that competition also affects ad ranking (Ayanso & Karimi, 2015).…”

Section: Research Backgroundmentioning

confidence: 99%

See 4 more Smart Citations

Dark sides of artificial intelligence: The dangers of automated decision‐making in search engine advertising

Schultz

Koch

Olbrich

2023

Asso for Info Science & Tech

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With the growing use of artificial intelligence, search engine providers are increasingly pushing advertisers to use automated bidding strategies based on machine learning. Such automated decision‐making systems leave advertisers in the dark about the data being used and how they can influence the outcome of the decision‐making process. Previous literature on artificial intelligence lacks an understanding of the dangers related to artificially intelligent systems and their lack of transparency. In response, our paper addresses the inherent risks of the automated optimization of advertisers' bidding strategies in search engine advertising. The selected empirical case of a service company therefore demonstrates how data availability can trigger a long‐term decline in advertising performance and how search engine advertising performance metrics develop before and after an event of data scarcity. Based on data collected for 525 days, difference‐in‐differences analysis shows that the algorithmic approach has a considerable and lasting negative impact on advertising performance. Furthermore, the empirical case indicates that self‐regulated learning can initialize a downward spiral that gradually impairs advertising performance. Thus, the aim of this study is to increase awareness regarding automated decision‐making dangers in search engine advertising and help advertisers take preventive measures to reduce the risks of algorithm missteps.

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Section: Research Backgroundmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

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Dark sides of artificial intelligence: The dangers of automated decision‐making in search engine advertising

Schultz

Koch

Olbrich

2023

Asso for Info Science & Tech

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Fighting against de-pooling effect of airport advertising spaces: A supply chain perspective

Zhao,

Xiao,

Yang

et al. 2024

Transportation Research Part E: Logistics and Transportation Re

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Estimation of Lockdowns’ Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data

Drachal

Cortés

2022

IJERPH

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Lockdowns introduced in connection with the COVID-19 pandemic have had a significant impact on societies from an economic, psychological, and health perspective. This paper presents estimations of their impact on well-being, understood both from the perspective of mental health and considering economic security and similar factors. This is not an easy task because well-being is influenced by numerous factors and the changes happen dynamically. Moreover, there are some obstacles when using the control group. However, other studies show that in certain cases it is possible to approximate selected phenomena with Google search queries data. Secondly, the econometric issues related to the suitable modeling of such a problem can be solved, for example, by using Bayesian methods. In particular, herein the recently gaining in popularity Bayesian structural time series and Bayesian dynamic mixture models are used. Indeed, these methods have not been used in social sciences extensively. However, in the fields where they have been used, they have been very efficient. Especially, they are useful when short time series are analyzed and when there are many variables that potentially have a significant explanatory impact on the response variable. Finally, 15 culturally different and geographically widely scattered countries are analyzed (i.e., Belgium, Brazil, Canada, Chile, Colombia, Denmark, France, Germany, Italy, Japan, Mexico, the Netherlands, Spain, Sweden, and the United Kingdom). Little evidence of any substantial changes in the Internet search intensity on terms connected with negative aspects of well-being and mental health issues is found. For example, in Mexico, some evidence of a decrease in well-being after lockdown was found. However, in Italy, there was weak evidence of an increase in well-being. Nevertheless, the Bayesian structural time series method has been found to fit the data most accurately. Indeed, it was found to be a superior method for causal analysis over the commonly used difference-in-differences method or Bayesian dynamic mixture models.

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Analysis of online position auctions for search engine marketing

Cited by 4 publications

References 41 publications

Dark sides of artificial intelligence: The dangers of automated decision‐making in search engine advertising

Dark sides of artificial intelligence: The dangers of automated decision‐making in search engine advertising

Fighting against de-pooling effect of airport advertising spaces: A supply chain perspective

Estimation of Lockdowns’ Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data

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