Search Ads Research Articles

A study of Leakage Ratio for increasing performance of online keyword search advertising

In this study, research to improve efficiency of online advertising market, we would like to propose a new performance index called "Leakage Ratio" which can increase the efficiency of advertisement.
 Methods/Statistical analysis: Naver, the Internet portal site in Korea, is the most influential medium for online keyword search advertising. In this study, Leakage Ratio management is applied to online keyword search ads for five medium and large size online shopping malls at Naver. Based on the performance trend of each search keyword, we tried to improve the efficiency of the whole advertisement by changing the bid of the low efficiency keyword.

Search Advertising and Information Discovery: Are Consumers Averse to Sponsored Messages?

We analyze a large-scale randomized field experiment in which a search engine varied the prominence of search ads for 3.3 million US users: one group of users saw the status quo, while the other saw a lower level of advertising (with prominence of search ads decreased). Revealed preference data reject that users are, overall, averse to the experimentally varied search ads which span a diverse set of searches and advertisers. At the margin, users prefer the status quo with the higher level of advertising. On the supply side, newer websites are more likely to advertise. Going from the lower to the higher level of advertising increases traffic to newer websites, with the newest decile of websites gaining traffic by 10%. Users also respond more positively to advertising when local businesses in their state create new websites. Taken together, patterns in our data are consistent with a constructive role of search advertising in which it fills significant information gaps by conveying new information, which is hard for the search engines to gather, and therefore missed by the organic algorithms. Viewing search ads, at the margin we study, makes consumers better off on average.

Content-Based Model of Web Search Behavior: An Application to TV Show Search

We develop a flexible content-based search model that links the content preferences of search engine users to query search volume and click-through rates. Content preferences are defined over latent topics that describe the content of search queries and search result descriptions. Moreover, our model allows content preferences to vary systematically based on the context of the search. To facilitate efficient and scalable inference, we develop a full Bayesian variational inference algorithm. We evaluate our modelling framework using real-world data on searches for TV shows on the Bing search engine. We illustrate how our model can quantify the content preferences associated with each query, and how these preferences vary systematically based on whether the query is observed before, during, or after a TV show is aired. We also show that our model can help the search engine improve its ranking of search results. In addition to search engines, advertisers can apply our model to understand their search advertising campaign data and gain insights into which search ads should be targeted to which users, when, and on which devices.

A study of Leakage Ratio for increasing performance of online keyword search advertising

Background/Objectives: In this study, research to improve efficiency of online advertising market, we would like to propose a new performance index called "Leakage Ratio" which can increase the efficiency of advertisement.
 Methods/Statistical analysis: Naver, the Internet portal site in Korea, is the most influential medium for online keyword search advertising. In this study, Leakage Ratio management is applied to online keyword search ads for five medium and large size online shopping malls at Naver. Based on the performance trend of each search keyword, we tried to improve the efficiency of the whole advertisement by changing the bid of the low efficiency keyword.

Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising

One of the central challenges in online advertising is attribution, namely, assessing the contribution of individual advertiser actions such as e-mails, display ads and search ads to eventual conversion. Several heuristics are used for attribution in practice; however, most do not have any formal justification. The main contribution in this work is to propose an axiomatic framework for attribution in online advertising. We show that the most common heuristics can be cast under the framework and illustrate how these may fail. We propose a novel attribution metric, that we refer to as counterfactual adjusted Shapley value (CASV), which inherits the desirable properties of the traditional Shapley value while overcoming its shortcomings in the online advertising context. We also propose a Markovian model for the user journey through the conversion funnel, in which ad actions may have disparate impacts at different stages. We use the Markovian model to compare our metric with commonly used metrics. Furthermore, under the Markovian model, we establish that the CASV metric coincides with an adjusted unique-uniform attribution scheme. This scheme is efficiently implementable, and can be interpreted as a correction to the commonly used uniform attribution scheme. We supplement our theoretical developments with numerical experiments using a real-world large-scale dataset.

Large-scale Ensemble Model for Customer Churn Prediction in Search Ads

Customer churn prediction is one of the most important issues in search ads business management, which is a multi-billion market. The aim of churn prediction is to detect customers with a high propensity to leave the ads platform, then to do analysis and increase efforts for retaining them ahead of time. Ensemble model combines multiple weak models to obtain better predictive performance, which is inspired by human cognitive system and is widely used in various applications of machine learning. In this paper, we investigate how the ensemble model of gradient boosting decision tree (GBDT) to predict whether a customer will be a churner in the foreseeable future based on its activities in the search ads. We extract two types of features for the GBDT: dynamic features and static features. For dynamic features, we consider a sequence of customers’ activities (e.g., impressions, clicks) during a long period. For static features, we consider the information of customers setting (e.g., creation time, customer type). We evaluated the prediction performance in a large-scale customer data set from Bing Ads platform, and the results show that the static and dynamic features are complementary, and get the AUC (area under the curve of ROC) value 0.8410 on the test set by combining all features. The proposed model is useful to predict those customers who will be churner in the near future on the ads platform, and it has been successfully daily run on the Bing Ads platform.

A Study on the Effect of Internet Marketing Activities on Real Estate Brokerage Performance

This study used a number of online marketing activities of authorized real estate brokers and portal sites to select among these tools and to focus on Internet marketing activities. Based on the demographic differences, the government conducted a survey of authorized brokers in Seoul, Daejeon, Daegu and Busan according to the characteristics of internet marketing activities. Research methods are as follows. First, conducting frequency analysis, Internet activities of established public service brokers are categorized into search ads, SNS marketing, search ads and social networking sites marketing, SNS marketing and portal site advertisement, total SNS site advertising and portal marketing, Among the seven Internet marketing activities, the results of real estate brokerage are high when they are engaged in search, SNS marketing, portal site advertisement and SNS marketing together. Second, through factor analysis, the main reasons for internet marketing activities were extracted as explanatory variables, and reliability analysis was conducted.Third, the analysis of one-way distribution looked at real estate brokerage performance by opening public utility certified brokers according to their type of Internet marketing activities by demographic characteristics, region and specialty. The results confirmed that the opening public brokers were engaged in Internet marketing activities, focusing on search and portal site advertising to quickly deliver products. SNS marketing was helping to communicate with customers through the use of real estate information in a limited marketing area due to the characteristics of real estate. Therefore, if they are actively marketed separately or in combination, they could be effective in marketing brokerage performance.

The Long Road to Online Conversion: A Model of Multi-Channel Attribution

Consumers are exposed to advertisers across a number of channels. As such, a conversion or a sale may be the result of a series of ads that were displayed to the consumer. This raises the key question of attribution: which ads get credit for a conversion and how much credit does each of these ads get? This is one of the most important questions facing the advertising industry today. Although the issue is well documented, current solutions are often simplistic; for e.g., attributing the sale to the most recent ad exposure. In this paper, we address the problem of attribution by developing a Hidden Markov Model (HMM) of an individual consumer's behavior based on the concept of a conversion funnel. We apply the model to a unique data-set from the online campaign for the launch of a car. We observe that different ad formats, e.g. display and search ads, affect consumers differently based on their states in the decision process. Display ads usually have an early impact on the consumer, moving him from a disengaged state to an state in which he interacts with the campaign. On the other hand, search ads have a pronounced effect across all stages. Further, when the consumer interacts with these ads (e.g. by clicking on them), the likelihood of a conversion increases considerably. Finally, we show that attributing conversions based on the HMM provides fundamentally different insights into ad effectiveness relative to the commonly used approaches for attribution. Contrary to the common belief that display ads as are not useful, our results show that display ads affect early stages of the conversion process. Furthermore, we show that only a fraction of online conversions are driven by online ads.

Empirical Work on Auctions of Multiple Objects

Abundant data has led to new opportunities for empirical auctions research in recent years, with much of the newest work on auctions of multiple objects, including: (1) auctions of ranked objects (such as sponsored search ads), (2) auctions of identical objects (such as Treasury bonds), and (3) auctions of dissimilar objects (such as FCC spectrum licenses). This paper surveys recent developments in the empirical analysis of such auctions. (JEL D44, H82)

Service online search ads: from a consumer journey view

PurposeThis study aims to address the following research questions: Do the two types of service firms (individual or aggregator) have similar competitiveness on online search ads? How should the two types of service firms select optimal branded keywords to improve search performance? In addition, how do consumers’ search queries influence the service search performance of the two types of service firms?Design/methodology/approachIn this study, the authors conduct an empirical analysis by building a two-stage choice modeling on the process of search engine ranking and consumer click-through decisions. The authors estimate the parameter coefficients and test the hypotheses using maximum likelihood estimation in the logistic regression model.FindingsThe empirical findings suggest that consumer response rates are highly dependent upon three aspects (service types, branded keyword strategy and consumer search query). First, the authors found that service aggregators receive greater consumer responses than individual service providers. Second, depending upon the various branded keyword strategies (e.g. generic vs branded, “within-type” vs “cross-type”) implemented by service aggregators or individual firms, the expected consumer responses could be quite different. Finally, customer’s search query, being either generic or branded, also has direct effect and interactive effect with service type on how consumers would response to the sponsored ads in the service search process.Research limitations/implicationsThe limitation of the research is twofold. First, conversion rate is not considered in the model estimation due to the nature of the data set. Second, the discussion about the keywords selection strategies is focusing on the hospitality industry. Future research shall further validate the generalizability into other industries.Practical implicationsFirst, given this competitive advantage, service aggregators should take an aggressive approach to adopting paid search strategy in acquiring new users and enhance its brand salience in the service ecosystem. Second, when considering other competitor’s brand names to include, if a firm is a service provider (e.g. hotel), a strategy that can help it receive higher consumer response would be to use “within-type” rather than “cross-type” branded keyword strategy. If a firm is a service aggregator, a better branded keyword strategy would be to use “across-type” instead of “within-type” approach. In addition, given that consumer’s brand awareness can influence the effectiveness of branded keyword strategy, online service search should target consumers in earlier stages of a decision journey.Social implicationsThe authors believe their theoretical framework can provide actionable solutions to service firms to ease customer’s search process, increase customer’s stickiness using search engines and add value to the customer relationships with all services entities within the digital ecosystem.Originality/valueThis study is the first to expand online search marketing into granule examinations (main and interactive effects of three key factors) in the service search domain. First, the authors differentiate service firms into two categories – online travel aggregators and individual hotels in the model. Second, the authors introduce two sets of new classifications of branded keywords for online service search research (i.e. own versus other brand and “cross-type” versus “within-type” branded keywords). Third, this study integrates service consumers’ search word specificity into the conceptual framework which is often missing in previous online search research.

A New Method to Aid Copy Testing of Paid Search Text Advertisements

The authors propose a new approach to evaluate the perceptions and performance of a large set of paid search ads. This approach consists of two parts. First, primary data on hundreds of ads are collected through paired comparisons of their relative ability to generate awareness, interest, desire, action, and click performance. The authors use the Elo algorithm, a statistical model calibrated on paired comparisons, to score the full set of ads on relative perceptions and click performance. The estimated scores validate the theoretical link between perceptions and performance. Second, the authors predict the perceptions and performance of new ads relative to the existing set using textual content metrics. The predictive model allows for direct effects and interactions of the text metrics, resulting in a “large p, small n” problem. They address this problem with a novel Bayesian implementation of the VANISH model, a penalized regression approach that allows for differential treatment of main and interaction effects, in a system of equations. The authors demonstrate that this approach ably forecasts relative ad performance by leveraging perceptions inferred from content alone.

Biosimilars & Exclusive Dealing Antitrust Law: The Case of Pfizer Inc v Johnson & Johnson et al.

Following the generally accepted theories of the legal scholar Robert Bork and his Chicago School colleagues, vertical restraints such as exclusive dealing contracts are presumptively procompetitive and welfare-enhancing because it would be irrational for a buyer to exclude the lowest cost supplier. On September 20, 2017, the drug manufacturer Pfizer filed a lawsuit Pfizer Inc, v Johnson & Johnson et al (link to the full court filing) claiming that Johnson & Johnson (J&J) violated Section 2 of the Sherman Antitrust Act by monopolizing the market for its incumbent biologic drug Remicade®. This was achieved via rebate contracts with the largest insurance companies in the USA that had the effect of excluding from coverage Pfizer’s biosimilar drug Inflectra®. We will present the case that Pfizer’s antitrust case is weak because it was unlikely that Pfizer was the low cost supplier based on a view of lump sum rebate offers as efficiency-enhancing “signals” of expected consumer demand for a product. What insurance companies are doing is similar to the AdWord algorithm Google came up with for “slotting” search ads based on a calculated “AdRank”, which is a function of both unit bids and expected demand as measured by estimated click-through rates.

청소년 인터넷게임중독에 대한 인터넷게임광고의 영향

Objectives: Studies on internet game addiction focused mostly on individual or family factors, and social or environmental influence has received less attention. In order to raise awareness of the social responsibility in preventing internet game addiction, this study examined among youth the extent of exposure to internet game advertisements and the relationship of their exposure to the risk of internet game addiction. Methods: The sample consisted of 1,838 middle school students living in Seoul and Gyeonggi province, who completed a survey measuring exposure to internet game advertisements and other game related behaviors. Results: About 6% of the respondents were classified as the internet game addiction risk group. Respondents were exposed to internet game advertisements through various media including TV, application search windows and ads posted in public transportation. The addiction risk group showed higher perception of game advertisements. Multiple logistic regression results indicated that the exposure to advertisements and receptivity of the advertisements increased the risk of internet game addiction. Conclusions: Study findings confirmed the role of environmental factors associated with internet game addiction. In order to decrease and prevent internet game addiction, more attention should be given to strategies targeting social responsibility of the society and the game industry. More effective regulation and self-regulation efforts in regards to game advertisements are needed.

Advertiser Prominence Effects in Search Advertising

Search advertising is the ordered list of advertisements that appears when a user searches for something in an online search engine. By construction, these ads differ in prominence: ads higher up the list are more prominent than ads lower down the list. However, search ads also differ in prominence in another way: prominence of advertiser. This paper examines how these two types of prominence interact in determining the click-through rate (CTR) of these ads. Using individual-level click-stream data from Microsoft’s Live Search platform and measures of advertiser prominence from Alexa.com , we find that ad position and advertiser prominence are substitutes. Specifically, in searches for camera brands, a retailer not in the top 100 of Alexa rankings has a 30%–50% higher CTR in position 1 than in position 2, whereas a retailer in the top 100 of Alexa rankings has only a 0%–13% higher CTR for the same position improvement. Qualitatively similar results are obtained for several other search strings. These findings demonstrate, first, that advertiser brand matters even for search ads, and, second, the way it matters is the opposite of what is usually assumed in the theoretical literature on search advertising. The online appendix is available at https://doi.org/10.1287/mnsc.2016.2677 . This paper was accepted by Matthew Shum, marketing.

Attitudes toward mobile search ads: a study among Mexican millennials

PurposeThe purpose of this study is to conduct a survey of Mexican millennials to measure the extent of negative bias and perceived advertising value they experienced toward the ads they encountered while performing a search for local products and services from their smartphones.Design/methodology/approachUsing a paper survey with a scenario question, responses were collected from 1,215 millennial smartphone owners about the strategies they used for scanning mobile search organic and sponsored results and quickly reaching the information they needed when performing a mobile search. The 315 participants who reported clicking on ads were further surveyed on their perceptions of ad informativeness, entertainment, irritation and credibility. These constructs were used as the predictors of advertising value in a structural equations model which was estimated with partial least squares.FindingsA substantial bias against sponsored results was found, with two-thirds of respondents skipping the ads when performing a mobile search from their smartphones. However, 28.2 per cent reported clicking on the most relevant result without regard to it being organic or sponsored, and an additional 5.6 per cent reported clicking on an ad as their first strategy. In the structural model, all four hypothesized antecedents of advertising value were significant, and some gender differences were detected.Practical implicationsWith the increasing penetration of smartphones, and rapid growth of mobile search, these results are particularly relevant for local merchants, who can use mobile search ads to leverage their location and communicate with searching consumers at the precise moment when they are most receptive to timely and relevant advertising.Originality/valueThis study is the first to measure the extent of consumer bias against sponsored results in a mobile search, and the first empirical estimation of the advertising value of mobile-sponsored results.

Paid Search Advertising

This note provides a primer on paid search advertising, which is an important component of digital marketing. The mechanics of paid search is explained using the Google search engine platform. The note covers metrics for evaluating the performance of paid search, the strategic objective of paid search, the relationship between customer lifetime value and search ads, how to overcome sparse data problems using keyword clouds, and the nature of Google AdWords's enhanced campaigns. Excerpt UVA-M-0860 Rev. May 3, 2018 Paid Search Advertising You're in the market for a baseball cap, an espresso maker, and a Ferrari F430 (lucky you). You decide you'd like to purchase each of the items in an online auction. You'd like to pay $ 10 for the hat, $ 1,000 for the coffee maker, and $ 150,000 for the Ferrari, so you set aside $ 151,010 to make sure you land your items. Without understanding the nuances of the bidding structure, you decide to take an average of your total budget and allocate that as your bid amount on each item—$ 50,336.67 for the hat, $ 50,336.67 for the espresso maker, and $ 50,336.67 for the Ferrari. Much to your dismay, you later learn you've won the auctions for the hat and espresso machine but missed out on the Ferrari. As ridiculous as the strategy sounds, this is what can happen when businesses run paid search advertising campaigns without trying to optimize or target their efforts to the business context and nuances of the system at hand. . . .

Causal Inference in Marketing Applications

Marketing applications offer many difficult and unique challenges in causal inference. In particular, targeted marketing activities, the arch-typical example of is search ads, can be difficult to evaluate using purely observational data. I review causal methods proposed in the recent econometrics literature and consider their suitability for various problems in marketing. In particular, I call attention to problems of how to evaluate various estimation procedures in the marketing context.

Are Your Patients Using ‘Digital Supplements’?

Are Your Patients Using ‘Digital Supplements’?

How should search ads platform balance ads quality and quantity: comparison between business search and community search ads

The pervasiveness innovation of search advertisement calls for more researches on the managerial strategies of search advertisements. These search advertising platforms, which emphasise pay for performance or clicks, has to balance the different needs of ads quantity and ads quality to attract more clicks. Based on two-sided market theory and social judgement theory, our research collected data from two kinds of search ads (business search and community search ads, represented for less nuisance and more nuisance ads) in the same platform, the analysis results showed us that, the advertisement quality improvement for users are much more prominent than the advertisement quantity. Furthermore, the ads quantity increment will moderate the effect of ads quality with inverse U relationship if the search ads is less nuisance, and the ads quantity increment will hinder the effect of ads quality if the search ads is high nuisance.

Perception and effectiveness of search advertising on smartphones

ABSTRACTThis paper explores the perception and effectiveness of mobile search ads from the perspective of users. The study investigates the attention and interaction of users as well as their subjective estimation of paid listings within Google search results on smartphones. During the tests, each of the 20 users has to accomplish four different search tasks. Data collection methods combine eye‐tracking with click‐through analysis and interviews. Results indicate that there is no “ad blindness” on mobile search, but similar to desktop search, users also tend to avoid search advertising on smartphones. For mobile search, ads appear to cause higher usability costs than on desktop.