A Novel Keyword Suggestion Method to Achieve Competitive Advertising on Search Engines

Search engine advertising is a popular business model for online advertising and recently a new strategy (i.e. competitive advertising) is emerging. Competitive advertising is helpful for organizations to expand market shares from competitors, which is crucial to sustain competitive advantage. To achieve the goal of competitive advertising, appropriate and fruitful competitive keywords should be provided to advertisers. However, existing keywords suggestion methods usually recommend general business keywords based on co-occurrence analysis. They not only fail to enable competitive advertising, but also limit advertisers to a small number of hot keywords, causing high bidding costs. As a response, this study proposes a competitive keywords suggestion method based on query logs. It uses the indirect associations between keywords and the hidden topic information captured by query logs to recommend competitive keywords. Through the method, massive competitive keywords are mined out to help organizations achieve competitive advertising and simultaneously broaden the choices of keywords for search engine advertising. Experiments are conducted to demonstrate that the proposed method could have a good performance than other methods, proving that it can help organizations well achieve the goal of competitive advertising

Keyphrase extraction by synonym analysis of n-grams for e-journals categorisation

Automatic keyword or keyphrase extraction is concerned with assigning keyphrases to documents based on words from within the document. Previous studies have shown that in a significant number of cases author-supplied keywords are not appropriate for the document to which they are attached. This can either be because they represent what the author believes the paper is about not what it actually is, or because they include keyphrases which are more classificatory than explanatory e.g., “University of Poppleton” instead of “Knowledge Discovery in Databases”. Thus, there is a need for a system that can generate appropriate and diverse range of keyphrases that reflect the document. This paper proposes a solution that examines the synonyms of words and phrases in the document to find the underlying themes, and presents these as appropriate keyphrases. The primary method explores taking n-grams of the source document phrases, and examining the synonyms of these, while the secondary considers grouping outputs by their synonyms. The experiments undertaken show the primary method produces good results and that the secondary method produces both good results and potential for future work

A Framework to Generate Optimal Keyword List for Pay-Per Click Advertising in an Organization

Web advertising is playing a key role making huge turnovers in terms of revenue for the retailers marketing various products online. Internet became a primary source of valuable information for everyone. The search engines are playing an important role as information providers over a decade. Online Advertising and Search Engine Optimization (SEO) became two primary sources for advertisers to implant their information online and keywords play an active role in driving traffic to the retailer’s website. Though there is ample literature available on search engine marketing, it is always a challenge for the advertisers to identify an ideal strategy for gathering keywords that would yield impressive results. This research article is an effort to provide an ideal framework that can be adopted by a retailer/advertiser to generate an optimal list of keywords that can drive good quality traffic to the website. The author refers an optimal list to state the quality of keyword list that can be generated using the proposed framework

Historical query data as business intelligence tool on an internationalization contex

[EN] This article reports theory concerning the key strategies of information search behaviour on an international market-orientation context, proposing a common framework to identify search goals using data generated from the keyword planner from google covering the period 2014/17 applied to the clothing retail sector. A conceptual framework of user goal identification from search data within clothing retail sector is presented and discussed in light of existing empirical studies. For that, a case study of an important fashion retailer has been analyzed: Zara. This firm is situated in the second position within the ranking of most valuable brands of clothes 2015 around the world (Kantar, 2016). A comparative analysis of search patters of this company, between United Kingdom and Spain, has been developed in order to offer the possible internationalization strategies in the online retail sector. User goals are identifyed and are stable over the period of study, a framework that covers main clothing consumer search goals have been identifyed.This study has been developed within the Research Project funded by Fundación Ramón Areces, entitled “La colaboración abierta en Internet como estrategia de innovación e internacionalización del sector de moda y complementos [open collaboration in the Internet as innovation and internationalization strategies by fashion sector] (2015-2018).Carro-Rodríguez, J.; Lorenzo-Romero, C.; Gómez-Borja, M. (2018). Historical query data as business intelligence tool on an internationalization contex. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 221-228. https://doi.org/10.4995/CARMA2018.2018.8361OCS22122