Model of the Effectiveness of Google Adwords Advertising Activities

Szymański, Grzegorz; Lininski, Piotr

doi:10.1109/stc-csit.2018.8526633

Cited by 20 publications

(12 citation statements)

References 4 publications

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“…At present, global Internet advertising has a scale of hundreds of billions of dollars, and search advertising has become an important form of online advertising [1]. e advertising industry has gradually developed from targeted delivery, where the value of ad delivery can be precisely measured, with a user-friendly and advertiser-beneficial advertising market [2]. Search engine revenue (Revenue Per Search, RPS) is one of the important evaluation indicators of the success of the search advertising recommendation system, which can be reflected by the pricing method of search advertising (such as CPC, Cost Per Click) and the ability of ads to attract users to click (Click-roughRate, CTR); that is, RPS � CTR × CPC [3].…”

Section: Introductionmentioning

confidence: 99%

Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

Yang

Kong²,

Liu

2021

Security and Communication Networks

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To accurately predict the click-through rate (CTR) and use it for ad recommendation, we propose a deep attention AD popularity prediction model (DAFCT) based on label recommendation technology and collaborative filtering method, which integrates content features and temporal information. First, we construct an Attention-LSTM model to capture the popularity trends and exploit the temporal information based on users’ feedback; finally, we use the concatenate method to fuse temporal information and content features and design a Deep Attention Popularity Prediction (DAVPP) algorithm to solve DAFCT. We experimentally adjust the weighted composite similarity metric parameters of Query pages and verify the scalability of the algorithm. Experimental results on the KDDCUP2012 dataset show that this model collaborative filtering and recommendation algorithm has better scalability and better recommendation quality. Compared with the Attention-LSTM model and the NFM model, the F1 score of DAFCT is improved by 9.80 and 3.07 percentage points, respectively.

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Section: Introductionmentioning

confidence: 99%

Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

Yang

Kong²,

Liu

2021

Security and Communication Networks

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“…Keyword selection is the basis for the effectiveness of SSA campaigns (Szymanski and Lininski, 2018). Researchers have addressed the keyword selection problem through semantic mapping (Kiritchenko and Jiline, 2008;Arroyo-Cañada and Gil-Lafuente, 2019;Nagpal and Petersen 2020) and optimization techniques (Rusmevichientong and Williamson, 2006;Zhang et al, 2014;Yang et al 2019;Symitsi et al, 2022).…”

Section: Abstractelectionmentioning

confidence: 99%

Keyword targeting optimization in sponsored search advertising: Combining selection and matching

Yang²

2022

Electronic Commerce Research and Applications

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“…Google Adwords offers the marketer a comprehensive system to advertise itself on various digital platforms through techniques like keyword planner, Search Engine Optimisation (SEO), Google display network, and advertisement auctions for different kinds of advertisements including display advertisement, banner advertisement, expandable advertisement, video advertisement, and bidding for advertisement spaces as well (keyword bidding) (Geddes, 2014). A few models have also been recently developed after assessing the viability of Google Adwords where the different factors influencing the effectiveness of the campaign are quantified (Szymanski & Lininski, 2018).…”

Section: Digital Advertising and Google Adwordsmentioning

confidence: 99%

Online Advertising Strategies to Effectivly Market a Business School

Kumar

Raman

Meenakshi³

2021

IJHE

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Advertising has always played an important role in creating visibility for educational institutions. In today’s time, digital marketing is the sought-after mode as there has been a significant shift from offline to online advertising. With the evolving times, flexibility and convenience take significant importance and it is critical for educational institutions to shift gears and adapt to the new formats. In order to stay relevant and have a competitive advantage, digital advertising helps higher educational institutions go that extra mile in engaging with their potential customers. It also helps in building awareness and attract good quality of students. In the world of digital advertising, ‘Google Advertisement’ is an online advertising platform developed by Google, where advertisers bid to display brief advertisements, service offerings, product listings, or videos to web users. It can place advertisements both in the results of search engines like Google Search and on non-search websites, mobile apps, and videos. Google AdWords offers the most pragmatic solutions and tools to all strategic issues of digital advertising. Click Through Ratio (CTR) stands out as the most significant index of reflecting its influence and impact. Amongst the array of choices, the right strategy requires an academic and strategic backing. The objective of this paper is to assess on the impact of Google Adwords is used in digital advertising campaigns promoting business schools in specific. This research concentrates on CTR as a measure of the campaign’s effectiveness. This paper try’s to understand CTR in the context related to the type of content embedded in these digital advertisements; the structure of this content; and hence identify and suggest new strategies. This paper identifies and proposes the right online advertising strategy that can be used by a Business School (B School).Purposive/non-probabilistic sampling was carried out to choose the specific of Business Schools (B-schools) for this study. The business schools selected were based on the National Institution Ranking Framework (NIRF) 2018 of the Indian Human Resource Development. The data was analyzed using to the Social Sciences Statistical Suite (SPSS). There was only access to publicly available and publicly displayed advertisement with no access to user profile data. CTR was utilized to measure total and proportional engagement. The advertisements were then categorized based on their content and analyzed through a one-way ANOVA test. For the purpose of an operationalizing, CTR was utilized as defined by Pak et. al. (2018): “A ratio showing how often people who see your advertisement end up clicking it.” The main components analyzed are the characteristics of an effective advertisement appearing on the digital platform measured through its Click Through Ratio. One-way ANOVA has been conducted to assess the Click Through Ratio of advertisement segregated in twenty categories based on their format, content and time of appearance. The analysis reflects that Click Through Ratio differs for different format of advertisements, the information that they contain and for the time and day that they appear. Strategies based on these findings are suggested along with discussion, limitations and further scope of research.

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Model of the Effectiveness of Google Adwords Advertising Activities

Cited by 20 publications

References 4 publications

Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

Keyword targeting optimization in sponsored search advertising: Combining selection and matching

Online Advertising Strategies to Effectivly Market a Business School

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