Search Advertising Research Articles

Cross channel effects of search engine advertising on brick & mortar retail sales: Meta analysis of large scale field experiments on Google.com

We investigate the cross channel effects of search engine advertising on Google.com on sales in brick and mortar retail stores. Obtaining causal and actionable estimates in this context is challenging: Brick and mortar store sales vary widely on a weekly basis; offline media dominate the marketing budget; search advertising and demand are contemporaneously correlated; and estimates have to be credible to overcome agency issues between the online and offline marketing groups. We report on a meta-analysis of a population of 15 independent field experiments, in which 13 well-known U.S. multi-channel retailers spent over $4 Million in incremental search advertising. In test markets category keywords were maintained in positions 1-3 for 76 product categories with no search advertising on these keywords in the control markets. Outcomes measured include sales in the advertised categories, total store sales and Return on Ad Spending. We estimate the average effect of each outcome for this population of experiments using a Hierarchical Bayesian (HB) model. The estimates from the HB model provide causal evidence that increasing search engine advertising on broad keywords on Google.com had a positive effect on sales in brick and mortar stores for the advertised categories for this population of retailers. There also was a positive effect on total store sales. Hence the increase in sales in the advertised categories was incremental to the retailer net of any sales borrowed from non-advertised categories. The total store sales increase was a meaningful improvement compared to the baseline sales growth rates. The average Return on Ad Spend (ROAS) is positive, but does not breakeven on average although several retailers achieved or exceeded break-even based only on brick and mortar sales. We examine the robustness of our findings to alternative assumptions about the data specific to this set of experiments. Our estimates suggest online and offline are linked markets, that media planners should account for the offline effects in the planning and execution of search advertising campaigns, and that these effects should be adjusted by category and retailer. Extensive replication and a unique research protocol ensure that our results are general and credible.

Comparative Study on the Effectiveness of Keyword Search Advertising to Provide Tourists Information

Comparative Study on the Effectiveness of Keyword Search Advertising to Provide Tourists Information

Active Learning with Multiple Localized Regression Models

Oftentimes businesses face the challenge of requiring costly information to improve the accuracy of prediction tasks. One notable example is obtaining informative customer feedback (e.g., customer-product ratings via costly incentives) to improve the effectiveness of recommender systems. In this paper, we develop a novel active learning approach, which aims to intelligently select informative training instances to be labeled so as to maximally improve the prediction accuracy of a real-valued prediction model. We focus on large, heterogeneous, and dyadic data, and on localized modeling techniques, which have been shown to model such data particularly well, as compared to a single, “global” model. Importantly, dyadic data with covariates is pervasive in contemporary big data applications such as large-scale recommender systems and search advertising. A key benefit from incorporating dyadic information is their simple, meaningful representation of heterogeneous data, in contrast to alternative local modeling techniques that typically produce complex and incomprehensible predictive patterns. We develop a computationally efficient active learning policy specifically tailored to exploit multiple local prediction models to identify informative acquisitions. Existing active learning policies are often computationally prohibitive for the setting we explore, and our policy makes the application of active learning computationally feasible for this setting. We present comprehensive empirical evaluations that demonstrate the benefits of our approach and explore its performance in real world, challenging domains.

A Study on CTR (Click Through Ratio) in Relation to Product Involvement for Keyword Search Advertising Strategy in E-commerce

A Study on CTR (Click Through Ratio) in Relation to Product Involvement for Keyword Search Advertising Strategy in E-commerce

The Branding Effect of Sponsored Search Advertising: Value-per-Click vs. Value-per-Impression

Advertising efforts mainly aim to build broader brand image and increase sales. In the online context, widely-successful sponsored search advertising (SSA), which predominantly employs performance-based schemes (e.g., pay-per-click), has been largely treated as a direct-selling mechanism. Recent empirical studies, however, have revealed the significant branding effect in SSA. Research investigating the branding effect and its interplay with the well-studied direct response effect is urgently called for. This paper is among the first attempts to formalize both types of advertising effects and the related budget decisions. One key element of our optimal control-based formulation is extending classical advertising response functions to capture the direct response and branding effects. Based on this formulation, we have derived the closed-form optimal budget policy. Both theoretical analyses and computational experiments have been performed. Our analytical results indicate that the advertiser's optimal budget decisions can be substantially different when the branding effect is explicitly considered. We also reveal experimentally that the optimal budgets for the advertisers with varying degrees of emphasis on branding can be substantially different. The reported work has important managerial implications. For online advertisers intending to promote (either exclusively or in combination with direct response) brand awareness and image, our work provides an analytical framework to facilitate their budgeting decisions and provide a closed-form solution. For search service providers such as Google, the reported work points to interesting opportunities in developing new business models and refining sponsored search market designs.

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.

Size, quantity and management capabilities: How Google updates AdWords to increase revenue

Google's primary source of revenue comes from paid search advertising, and it makes regular updates to make ad units more prevalent and attractive to searchers in order to invite more clicks. The vast majority of these updates can be categorised as changes to the size of ad units, the volume of ad units, and/or ad management capabilities afforded to advertisers. This paper explores the impact of Google's most significant recent changes in these areas, using Merkle advertiser data from some of the largest online brands to quantify the effects. It also offers insight into how Google is likely to expand advertising traffic in the future.

“First in first out” or “last in first out”: Presentation of information order on evaluation of utilitarian products

This study investigates how the presentation order of information may affect the evaluation of utilitarian products. Three experiments with different conditions (hedonic/utilitarian product; experience/search advertisement; unattractive/attractive packaging) were conducted with a total of 334 participants in Macau. This study tests both the affective and cognitive product evaluation. The findings indicate that assimilation effect occurs if the search advertisement of a utilitarian product is presented before and if the consumers have better cognitive evaluation. When an experience advertisement is presented before, contrast effect occurs and affective evaluation is reduced. However, when unattractive packaging is presented before, assimilation effect occurs, and cognitive evaluation is increased. When attractive packaging is presented before, the consumers demonstrate a positive cognitive and affective evaluation for the utilitarian product. These findings contribute to a better understanding of the role of presentation order on the consumers’ evaluation of utilitarian products from theoretical and managerial perspectives.

Dynamic Budget Allocation in Competitive Search Advertising

Search advertising marketplace is highly competitive in nature. As such, advertisers have to consider their competitors' responses when making advertising decisions, which concern both bid determination in individual keyword auction instances, and budget allocation over time at the aggregate market level. Although bidding decisions have been extensively treated in competitive auction settings in the literature, competitive budgeting models are yet to be developed. Applying the differential game framework, we propose a dynamic budget allocation model in a duopolistic competitive setting. We first examine the steady-state Nash equilibrium policies for the advertisers, and then investigate the optimal paths of open-loop Nash equilibrium. In addition, we provide an algorithm to compute the optimal path of budget policies. Computational experiments were conducted to derive additional managerial insights. One important finding of this study is that the effects of two advertising factors, quality score and potential market revenue, on the steady-state budget levels are non-monotonic. This finding to some extent challenges the commonly-held view that higher quality scores lead to lower optimal advertising expenditures. Another interesting finding suggests that advertisers should set a budget cap even in a competitive environment to avoid the potential risk of over-spending in sponsored search advertising.

Investigating the Spillover Effect of Keyword Market Entry in Sponsored Search Advertising

As Internet advertising infomediaries nowadays provide rich competition information, sponsored search advertisers are becoming more strategic when selecting keywords. This paper empirically examines the spillover effects in advertisers’ keyword market entry decisions, that is, how an advertiser’s likelihood of using a keyword is affected by competitors’ keyword entry decisions. We develop a structural model to characterize advertisers’ keyword market entry decisions. We apply the model to a panel data set of 1,252 laptop-related keywords mainly used by 28 manufacturers, retailers, and comparison websites that advertise on Google. Our analysis leads to several interesting findings. First, an advertiser’s expected position affects the nature of the competition. In particular, the spillover effect from below-ranked competitors is always positive, while the spillover effect from above-ranked competitors is either positive or negative. Second, the spillover effect from above-ranked ads is directionally affected by firms’ product-line characteristics: the effect among firms offering homogenous products (e.g., comparison sites) is negative, whereas the effect among firms with more differentiated products (e.g., manufacturers and retailers) is positive. Third, the spillover effect from above-ranked ads is directionally affected by firms’ positions in a distribution channel: the effect from upstream (downstream) on downstream (upstream) firms tends to be negative (positive). Finally, a downstream firm is more likely to learn new keywords from an upstream firm but not vice versa. Our counterfactual simulations demonstrate that the keyword-specific competition information provided by infomediaries can improve the search engine’s revenue by about 5.7%.Data are available at https://doi.org/10.1287/mksc.2017.1053 .

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.

Google, King of Search

This survey of Google's rise to prominence as the de facto search engine of choice focuses on the theme of relevance as a guiding principle. As Google has diversified its offering through both organic growth and acquisition, the potential manifestations of relevance have multiplied, to the point where the nature of its market may need to be reassessed. Excerpt UVA-M-0818 Aug. 11, 2011 Google, King of Search In 2010, Google had an 80% market share of the world's Internet searches and over $ 29 billion in revenue. The following year, the U.S. Federal Trade Commission (FTC) began an investigation to determine whether Google was abusing its dominance in search advertising. Over the previous 12 years, the corporation had also diversified into a variety of products, from those developed organically, such as Gmail and Google Maps, to large acquisitions, such as YouTube. How had Google become such a powerhouse in just over a decade that it faced antitrust charges? And, with so many products, how exactly should Google's market be defined? Becoming an Advertising Machine Larry Page and Sergey Brin founded Google on their initial insight that effective searches relied less on key words than on links, and they emphasized maintaining focus on that core competency (Table 1). Less evident to them was how to monetize the concept. Other search engines had employed banner ads and pop-ups, which they considered intrusive and distracting, so instead, they developed AdWords, simple text-only ads that appeared above and alongside search results, the goal being “for the ads you see on Google to be as useful to you [the internet searcher] as they are to the advertisers.” . . .

РОЛЬ ТА ОСНОВНІ НАПРЯМКИ РОЗВИТКУ ІНТЕРНЕТ-МАРКЕТИНГУ

The author conducted a study of the role and actual functions of Internet marketing nowadays, analyzed the latest publications and studies of domestic and foreign scientists relating to Internet marketing. The definitions of Internet marketing by various scientists were considered. It was determined by the problematic that exists in the field of internet marketing at the moment. A review of modern tools we identified the main tools of Internet marketing, by which the communication occurs on the Internet: SEO (Search Engine Optimisation), search (paid) advertising, Media advertising, SMM (Social Media Marketing) social media marketing, content marketing, E-mail marketing; Hootsuit's company research analysis determined the number of Internet users and purchasing power in the online environment, which averaged 500 USD per year. The number of Internet users in the online environment averaged 500 USD per year. And also to prioritise specific types of products. We have also identified that search engines are the main source of learning new information for consumers. Having analysed the dynamics of internet penetration in Ukraine, it appears that the number of internet users increased by 8% in 2019, with 22.96 million Ukrainians using the internet at the end of 2019. The article examines the possibilities of Internet marketing tools that become available when using such a tool as web analytics, including: personalised offers to customers, building relationships with customers, after a purchase is made and increasing their retention rate. A study of advertising costs on the social network Facebook, during the presidential elections in Ukraine in 2019 and during the presidential race in the USA in 2020 was conducted. And also defined the role played by social networks in building relationships with the target audience. During the research, recommendations for the use of the main tools of Internet marketing were made and it was determined which business areas are not suitable for each of the tools. Based on the research and statistical data was predicted directions of development of Internet marketing.

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. . . .

A Large-Scale Field Experiment to Evaluate the Effectiveness of Paid Search Advertising

Companies spend billions of dollars online for paid links to branded search terms. Measuring the effectiveness of this marketing spending is hard. Blake, Nosko and Tadelis (2015) ran an experiment with eBay, showing that when the company suspended paid search, most of the traffic still ended up on its website. Can findings from one of the largest companies in the world be generalized? We conducted a similar experiment with Edmunds.com, arguably a more representative company, and found starkly different results. More than half of the paid traffic is lost when we shut off paid-links search. These results suggest money spent on search-engine marketing may be more effective than previously documented

Investigating the Spillover Effect of Keyword Market Entry in Sponsored Search Advertising

As Internet advertising infomediaries nowadays provide rich competition information, sponsored search advertisers are becoming more strategic when selecting keywords. This paper empirically examines the spillover effects in advertisers’ keyword market entry decisions, that is, how an advertiser’s likelihood of using a keyword is affected by competitors’ keyword entry decisions. We develop a structural model to characterize advertisers’ keyword market entry decisions. We apply the model to a panel dataset of 1,252 laptop-related keywords mainly used by 28 manufacturers, retailers, and comparison websites that advertise on Google. Our analysis leads to several interesting findings. First, an advertiser’s expected position affects the nature of the competition. In particular, the spillover effect from below-ranked competitors is always positive, while the spillover effect from above-ranked competitors is either positive or negative. Second, the spillover effect from above-ranked ads is directionally affected by firms’ product-line characteristics: the effect among firms offering homogenous products (e.g. comparison sites) is negative, whereas the effect among firms with more differentiated products (e.g. manufacturers and retailers) is positive. Third, the spillover effect from above-ranked ads is directionally affected by firms’ positions in a distribution channel: the effect from upstream (downstream) on downstream (upstream) firms tends to be negative (positive). Finally, a downstream firm is more likely to learn new keywords from an upstream firm but not vice versa. Our counterfactual simulations demonstrate that the keyword-specific competition information provided by infomediaries can improve the search engine’s revenue by about 5.7%.

Analysing clickstream data: do paid and organic search affect each other?

Investments in both paid and organic search are crucial for today's online retailer. In this paper, we analyse how consumers respond to organic and paid search advertising and gain insight into the interrelationship between these online marketing channels. We use a binary logit model and Markov-Chain-Monte-Carlo techniques and analyse the interaction effects between paid and organic search and the conversion probability based on clickstream data from a major electronic retailer. Our findings indicate that there is a positive relationship: when consumers interact with both paid and organic search, a strong positive effect occurs towards the conversion probability in the short-term compared to when users only interact with one channel. Our findings are valuable for organizations in regards to their online marketing strategies and budgeting.

Analysing clickstream data: do paid and organic search affect each other?

Investments in both paid and organic search are crucial for today's online retailer. In this paper, we analyse how consumers respond to organic and paid search advertising and gain insight into the interrelationship between these online marketing channels. We use a binary logit model and Markov-Chain-Monte-Carlo techniques and analyse the interaction effects between paid and organic search and the conversion probability based on clickstream data from a major electronic retailer. Our findings indicate that there is a positive relationship: when consumers interact with both paid and organic search, a strong positive effect occurs towards the conversion probability in the short-term compared to when users only interact with one channel. Our findings are valuable for organizations in regards to their online marketing strategies and budgeting.

Television and Digital Advertising: Second Screen Response and Coordination with Sponsored Search

We consider the potential to improve the efficiency and efficacy of broader advertising efforts through cross-channel coordination. Past work has demonstrated a positive relationship between television advertising and online search activity. Here, we consider the types of devices on which search response predominantly manifests following TV advertisements, and the degree to which shifts in search activity can be used to evaluate the success of TV advertisers' targeting efforts. We leverage data on TV advertising around Microsoft Windows 10 and an Xbox app (NFL Game Day Evolution), in combination with large-scale proprietary search data from Microsoft Bing. Our identification strategies hinge on a combination of geographic heterogeneity in TV advertising exposure and continuous variation in the cost of TV advertisements (a proxy for TV audience size). We first demonstrate that search response peaks within three minutes of the airing of a TV advertisement, and that this manifests primarily via second-screen mobile devices. Our estimated elasticities indicate that a 20% increase in advertising spend equates to an approximate 2.5% (3.4%) increase in search volumes for Windows 10 (the Xbox app). Second, we show that, indeed, the demographic groups targeted by TV advertisements are those most likely to respond, and we thereby demonstrate that TV ad effectiveness can be usefully measured via online search data. Third, examining sponsored search clicks in our query-level data, for queries involving brand-related keywords, we demonstrate a significant increase in rank-ordering effects in searches that take place in the minutes immediately following a TV advertisement, which implies a complementarity between TV and sponsored search advertisements.

Комплекс Интернет-маркетинга для продвижения туристического бизнеса

The article considers Internet marketing complex for promotion of travel business.
 The article gives a review of instruments of marketing communication in Internet
 network for touristic enterprises. The author describes the most common ways
 for users to interact with the network. The main devices through which users log
 on to the Internet are considered. The data on the percentage of use mobile
 devices and personal computers in Russia and European countries are given.
 The article summarizes the complex of instruments marketing communication
 on the Internet for tourist enterprises. The author considers types websites
 to promote businesses in travel industry, describes the structure of communication
 between organization and customer through various types of websites. The article
 presents a complex marketing communications through search engines, briefly
 describes search advertising and search optimization, presents the most popular
 search system of the Russian Internet segment.