A Dynamic Model of Sponsored Search Advertising

Sponsored search advertising is ascendant Jupiter Research reports expenditures rose 28% in 2007 to $8.9B and will continue to rise at a 15landscape. Yet little, if any empirical research focuses upon search engine marketing strategy by integrating the behavior of various agents in sponsored search advertising (i.e., searchers, advertisers, and the search engine platform). The dynamic structural model we propose serves as a foundation to explore these and other sponsored search advertising phenomena. Fitting the model to a proprietary data set provided by an anonymous search engine, we conduct several policy simulations to illustrate the bene ts of our approach. First, we explore how information asymmetries between search engines and advertisers can be exploited to enhance platform revenues. This has consequences for the pricing of market intelligence. Second, we assess the effect of allowing advertisers to bid not only on key words, but also by consumers searching histories and demographics thereby creating a more targeted model of advertising. Third, we explore several different auction pricing mechanisms and assess the role of each on engine and advertiser profits and revenues. Finally, we consider the role of consumer search tools such as sorting on consumer and advertiser behavior and engine revenues. One key finding is that the estimated advertiser value for a click on its sponsored link averages about 24 cents. Given the typical$22 retail price of the software products advertised on the considered search engine, this implies a conversion rate (sales per click) of about 1.1%, well within common estimates of 1-2% (gamedaily.com). Hence our approach appears to yield valid estimates of advertiser click valuations. Another finding is that customers appear to be segmented by their clicking frequency, with frequent clickers placing a greater emphasis on the position of the sponsored advertising link. Estimation of the policy simulations is in progress

Información personal: la nueva moneda de la economía digital

Technological progress has profoundly changed the way personal data are collected, accessed and used. Those data make possible unprecedented customization of advertising which, in turn, is the business model adopted by many of the most successful Internet companies. Yet measuring the value being generated is still a complex task. This paper presents a review of the literature on this subject. It has been found that the economic analysis of personal information has been conducted up to now from a qualitative perspective mainly linked to privacy issues. A better understanding of a quantitative approach to this topic is urgently needed

Does Position Matter More on Mobile? Ranking Effects across Devices

Achieving a better rank online is often costly. Is the effect of ranking different for mobile devices and traditional PC? This study empirically examines the ranking effect across different device types in an e-commerce environment. With over 4 million observations from Tweaker.net, the largest shopbot in Netherlands, we estimated the ranking effect between mobile and PC. Surprisingly, and contrary to prior findings, our results across different model specifications consistently show that ranking effect is smaller on mobile devices. This study extends the understanding about the effect of position in e-commerce context by empirically examining the ranking effect across devices. This study has important managerial implications for retailers and e-commerce platforms. As the ranking effect is smaller on mobile devices, retailers should take account of the source of traffic (mobile or PC) while bidding for a particular position. And platforms should consider the different ranking effects on different channels

Multi-Click Attribution in Sponsored Search Advertising: An Empirical Study in Hospitality Industry

Sponsored search advertising has become a dominant form of advertising for many firms in the hospitality vertical, with Priceline and Expedia each spending in excess of US$2 billion in online advertising in 2015. Given the competition in online advertising, it has become essential for advertisers to know how effectively to allocate financial resources to keywords. Central to budget allocation for keywords is an attribution of revenue (from converted ads) to the keywords generating consumer interest. Conventional wisdom suggests several ways to attribute revenues in the sponsored search advertising domain (e.g., last-click, first & last-click, or evenly distributed approach). We develop a multi-click attribution methodology using a unique multi-advertiser data set, which includes full advertiser and consumer-level click and purchase information. We add to the literature by developing a two-stage multi-click attribution methodology with a specific focus on sponsored search advertising in the hospitality industry with which we develop a parametric approach to calculate the value function from each stage of the estimation process. Given our multi-advertiser data set, we are able to illustrate the inefficiency of single-click attribution approaches, which undervalue assist clicks while overvaluing converted clicks

"To Sponsor or not to Sponsor: Sponsored Search Auctions with Organic Links"

In 2010 sponsored search advertisements generated over $12 billion in revenue for search engines in the US market and accounted for 46% of online advertising revenue. A substantial portion of this revenue was generated by the sale of search keywords using auction mechanism. We analyze a game-theoretic model to understand the interplay between organic and sponsored links in keyword auctions. Our model allows both the relevance of the advertising firm as well as the position of its sponsored link to impact click-through-rates. Our results demonstrate how the presence of organic links (links generated by the search engine algorithm) may lead to either more or less aggressive bidding for sponsored link positions depending on consumers attitudes toward sponsored links and the extent to which sponsored and organic links are complements or substitutes. In contrast to equilibrium results in existing literature, the firm with the highest value per click does not necessarily win the first spot in the sponsored search listing. It also may be optimal for a firm to bid an amount greater than the expected value (or sale) from a click.sponsored search, organic search, online advertising, keyword auction

To Sponsor or Not to Sponsor: Sponsored Search Auctions with Organic Links

In 2010 sponsored search advertisements generated over $12 billion in revenue for search engines in the US market and accounted for 46% of online advertising revenue. A substantial portion of this revenue was generated by the sale of search keywords using an auction mechanism. We analyze a game-theoretic model to understand the interplay between organic and sponsored links in keyword auctions. Our model allows both the relevance of the advertising firm as well as the position of its sponsored link to impact click-through-rates. Our results demonstrate how the presence of organic links (links generated by the search engine algorithm) may lead to either more or less aggressive bidding for sponsored link positions depending on consumer attitudes toward sponsored links and the extent to which sponsored and organic links are complements or substitutes. In contrast to equilibrium results in existing literature, the …rm with the highest value per click does not necessarily win the first spot in the sponsored search listings. It also may be optimal for a firm to bid an amount greater than the expected value (or sale) from a click.

An Empirical Analysis

학위논문(석사) -- 서울대학교대학원 : 경영대학 경영학과, 2021.8. 명준구.With the emergence of new technologies and due to the recent COVID-19 pandemic, e-commerce and its subsequent e-marketplaces are constantly gaining attention. Simultaneous to the popularity, competition is becoming fierce for both e-marketplace operators and its participating sellers. As a result, they are striving for a competitive edge. Incorporating decision-supporting services in e-marketplaces can be considered as a strategic activity for the platform operators, which can enhance the performance of sellers actively using such services. We therefore hypothesize that the usage of decision support systems will lead to an enhanced performance of e-marketplace participants, i.e., sellers. By utilizing a secondary data provided by one of the leading e-marketplace operators in Korea, we have empirically found out that usage of decision support systems, namely, seller dashboard and review systems, lead to an increase in sales, which is the measurement of a seller’s performance. This study will serve as a literature for DSS effectiveness, e-marketplace success strategies, and will provide theoretical implications for the resource-based view and competitive dynamics theory by adding an empirical evidence for those field of study. Also, this study possesses managerial implications for not only e-marketplace operators seeking success, but sellers within the platform also.IT의 발전과 함께 특히 최근 COVID-19 팬데믹의 영향으로 전자상거래는 꾸준한 성장과 관심을 받고 있다. 하지만 이와 동시에 경쟁 역시 치열해지고 있기 때문에, 전자상거래 플랫폼 운영사들과 플랫폼에 참여하는 판매자들은 경쟁우위를 점하게 위해 노력을 하고 있는 실정이다. 이를 위한 한가지 전략적 방안으로는 전자상거래 플랫폼 내에 의사결정 지원도구를 제공함으로써 판매자들을 돕는 방법이 있다. 이에 따라 실제 이러한 의사결정 지원도구가 실제 전자상거래 플랫폼 참여자에게 도움이 될 것이라는 가설을 세우고 연구를 진행하였다. 한국의 한 전자상거래 플랫폼의 데이터를 제공받아 분석한 결과, 대시보드 및 리뷰 시스템의 의사결정 지원도구를 사용한 경우 매출이 증가하여 판매자의 실적이 유의미하게 개선되었다는 사실을 확인할 수 있었다. 본 연구는 의사결정 지원도구의 효과성, 그리고 전자상거래 플랫폼 성공 요인에 관한 문헌으로서 그 의의가 있다고 할 수 있고, 자원기반이론 및 역동적 능력 이론에 관한 실증이라는 점에서 의의를 찾을 수 있다. 또한, 전자상거래 플랫폼 운영사뿐만 아니라 판매자들에게도 경영적인 시사점을 줄 수 있을 것이다.Table of Contents Chapter 1. Introduction 1 1.1 Study Background 1 1.2 Study Goals and Question 3 Chapter 2. Literature Review 5 2.1 E-marketplace 5 2.2 Decision Support Systems 9 2.3 Platform Strategy 10 Chapter 3. Hypotheses Development 12 3.1 Hypotheses and Research Model 12 Chapter 4. Research Methodology 17 4.1 Propensity Score Matching 17 4.2 Variables 18 Chapter 5. Data Analysis and Results 21 5.1 Data Description 21 5.2 Data Analysis 25 5.3 Results 27 Chapter 6. Discussion and Conclusion 31 6.1 Implications 31 6.2 Limitations and Further Research 32 6.3 Conclusion 33 References 35 국문 초록 40석

LemonAds: Impression Quality in Programmatic Advertising

The display advertising practice relies on the real-time exchange of large volumes of impressions. Advertisers and publishers typically carry out their transactions through Reservation contracts, Real Time Bidding (RTB), or a mixture of the two. The co-existence of multiple transaction methods is problematic since impression quality is difficult to assess. As such, the display advertising market is characterized by high uncertainty and asymmetric information. In this paper, we use viewability as a measure of impression quality and show how the co-existence of different transaction methods leads to allocation and pricing inefficiencies. Using bid-request level data from a European Demand Side Platform, we find that publishers who engage in both Reservation Contracts and RTB offer higher quality impressions through Reservation Contracts, while allocating the remaining lower quality impressions to RTB. We find that, by doing so, publishers can leverage on asymmetric information on impression quality to extract excess profit from advertisers

Hybrid Advertising Auctions

Several major websites offer hybrid auctions that allow advertisers to bid on a per-impression or a per-click basis. We present the first analysis of this hybrid advertising auction setting. The conventional wisdom is that brand advertisers (e.g. Coca-Cola) will bid per impression, while direct response advertisers (e.g. Amazon.com) will bid per click. We analyze a theoretical model of advertiser bidding to ask whether this conventional wisdom will hold up in practice. We find the opposite in a static game: brand advertisers bid per click, while direct response advertisers bid per impression. In a more realistic repeated game, we find that direct response advertisers bid per click, but brand advertisers may profitably alternate between bidding for clicks and bidding for impressions. The analysis implies that sellers of online advertising (a) may sometimes prefer not to offer advertisers multiple bidding options, (b) should try to ascertain advertisers' types when they do use hybrid auctions, and (c) should consider advertisers' strategic incentives when forming click-through rate expectations in hybrid auction formats

A Novel Approach for Bidding on Newly Set-Up Search Engine Advertising Campaigns

Advertisers setting up search engine advertising campaigns for the first time need to place bids on keywords, but typically lack experience and data to determine ranks that maximize a keyword's profit (generally referred to as a cold-start problem). This article aims at solving the problem of bidding on keywords in newly set-up search engine advertising campaigns. We suggest that advertisers collect data from the Google Keyword Planner to obtain precise estimates of the percentage increases in prices per click and clickthrough rates, which are needed to calculate optimal bids (exact approach). Together with the profit contribution per conversion and the conversion rate, the advertiser might then set bids that maximize profit. In case advertisers cannot afford to collect the required data, we suggest two proxy approaches and evaluate their performance using the exact approach as a benchmark. The empirical study shows that both proxy approaches perform reasonably well-the easier approach to implement (proxy 2) sometimes performs even better than the more sophisticated one (proxy 1). As a consequence, advertisers might just use this very simple proxy when bidding on keywords in newly set-up SEA campaigns. This research extends the stream of literature on how to determine optimal bids, which so far focuses on campaigns that are already running and where the required data to calculate bids is already available. This research offers a novel approach of determining bids when advertisers lack the aforementioned information