Influence of Web Advertising Strategies, Consumer Goal-Directedness, and Consumer Involvement on Web Advertising Effectiveness

Abstract: Determining the best way to utilize on-line media for advertising purposes is a critical question. This research, based on the Elaboration Likelihood Model (ELM), incorporates consumer goal-directedness and involvement as moderators influencing when a particular advertising strategy (i.e., variation strategy vs. appeal strategy) will be appropriate for Web advertisements. The results of a laboratory experiment supported the use of ELM in the Web environment and showed how advertising strategies should be designed and implemented in accordance with consumer goal-directedness and involvement to achieve Web advertising effectiveness. The results demonstrated the uniqueness of the Web media and the importance of goal-directedness and consumer involvement in the Web advertising context.

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Author(s): Wang K (Wang, Kai)1, Wang ETG (Wang, Eric T. G.), Farn CK (Farn, Cheng-Kiang)2 Source: INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE Volume: 13 Issue: 4 Pages: 67-95 Published: SUM 2009

Click on the following link for details information.
http://portal.acm.org/citation.cfm?id=1554437

Audience Selection for On-line Brand Advertising: Privacy-friendly Social Network Targeting

Abstract

Conference Information: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningParis, FRANCE, JUN 28-JUL 01, 2009ACM SIGKDD; ACM SIGMOD Abstract: This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behaviour on user-generated content sites, for the purpose of finding good audiences for brand advertising (as opposed to click maximizing, for example). Targeting social-network neighbours resonates well with advertisers, and on-line browsing behaviour data counter intuitively can allow the identification of good audiences anonymously. Besides being one of the first papers to our knowledge on data mining for on-line brand advertising, this paper makes several important contributions. We introduce a framework for evaluating brand audiences, in analogy to predictive-modeling holdout evaluation. We introduce methods for extracting quasi-social networks from data on visitations to social networking pages, without collecting any information on the identities of the browsers or the content of the social-network pages. We introduce measures of brand proximity in the network, and show that audiences with high brand proximity indeed show substantially higher brand affinity. Finally, we provide evidence that the quasi-social network embeds a true social network, which along with results from social theory offers one explanation for the increase in brand affinity of the selected audiences.

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Author(s): Provost F (Provost, Foster)1, Dalessandro B (Dalessandro, Brian)1, Hook R (Hook, Rod), Zhang XH (Zhang, Xiaohan)1, Murray A (Murray, Alan) Book Group Author(s): ACM Source: KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING Pages: 707-715 Published: 2009


Conference Information: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Paris, FRANCE, JUN 28-JUL 01, 2009

ACM SIGKDD; ACM SIGMOD

Click the following link for further details.
http://pages.stern.nyu.edu/~fprovost/Papers/kdd_audience.pdf

 

Ontological analysis of web surf history to maximize the click-through probability of web advertisements

Abstract: Due to an enormous in lux of capital over the past decade, the online advertising industry has become extremely robust and competitive. The difference between success and failure in such a competitive market often rests in the ability to deliver advertisements that are closely in line with a user's interests. In this work, we propose and test a new online advertisement targeting technique which adapts and utilizes several powerful and well tested information retrieval and lexical techniques to develop an estimate of a user's affinity for particular products and services based on an analysis of a user's web surfing behavior. This new online ad targeting technique performs extremely well in Our empirical tests. (C) 2009 Elsevier B.V. All rights reserved.

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Author(s): Deane J (Deane, Jason)1, Pathak P (Pathak, Praveen)2 Source: DECISION SUPPORT SYSTEMS Volume: 47 Issue: 4 Special Issue: Sp. Iss. SI Pages: 364-373 Published: NOV 2009

Click on the following link to have more detailed information.

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8S-4W1BV2S-1&_user=835417&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1132956889&_rerunOrigin=google&_acct=C000045119&_version=1&_urlVersion=0&_userid=835417&md5=9c71187d594452ef7eae2a7c2d7c3cdeOntological

Definitions of online advertising

It's always good to start with the definition of the subject which is our talking point.

Definition by Businessdictionary.com

Use of internet as an advertising medium where promotional messages appear on a computer screen. Since the communication software (browser) reveals sufficient information about the site's visitors, online advertising can be custom-tailored to match user preferences.