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

1-800 Contacts v. FTC

FTC Answering Brie

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Using of Trademarks in Keyword Advertising in Web Search Engines

Use of Trademarks in keyword advertising has been one of the most debatable issues in trademark law for several years. This entirely new way of using Trademarks over the Internet has provoked a discussion concerning the core concepts of Trademark law. Harmonized EU trademark law proved to be ambiguous on whether it amounted to trademark infringement or not. This ambivalence was also exhibited by the case law of EU Member States. European keyword advertisers simply could not tell which use of a competitor‘s trademark was lawful. In recent years CJEU has continuously expanded the scope and reach of trademark protection in the EU .It is notable that Inconsistencies in the court’s system of infringement criteria clearly come to the fore and this approach has been criticized by analysts who believe that the Court should have adopt a more traditional approach to the analysis of trademark infringement, which was suggested by its Advocate General, in order to arrive at the same conclusion. The premise on which the Court rested its ruling, it is believed, missed salient parts of the evidence, circumvented its preexisting jurisprudence, and most of all, threatened to open the floodgates of abusive trademark use in the future. With reference to above , this thesis will address issue of keyword advertising under EU legislations and will evaluate ECJ case law together with national members case law

Decision support system for search engine advertising campaign management by determining negative keywords

Search engine advertisers need to determine the best keyword set for their campaigns. Every company has particular constraints and expectations from the Search Engine Advertising (SEA). In this research we worked on a Decision Support System (DSS) that can be used in SEA campaign management. The DSS determines the negative keywords (which should be eliminated from the keyword set in order to improve the performance) based on the data obtained from the earlier campaigns. Current metrics used to determine the negative keywords are not sufficient/adequate, since they don’t incorporate other important aspects such as bounce rate, quality score etc. which are often used by the advertisers in order to evaluate the traffic but rely mostly to conversion rate. In our research first we analyze the keywords at unigram level (similar to some of the existing approaches available in the literature) in order to identify the set of unigrams which are negatively and/or positively effecting the campaign by using various machine learning techniques (either as is or used the core concepts associated with them) such as Naïve Bayes, Decision Trees, Logistic Regression. We further extended these algorithms by incorporating ideas borrowed from Greedy Randomized Adaptive Search (GRASP). We also introduced novel metrics which incorporate more aspects used in real life SEA campaigns by the advertisers as part of this process. The performance of our approach is evaluated with an experimental analysis conducted on real life data obtained from a major FMCG producer

The Seven Stages of the Digital Marketing Cycle

The theme of this book is digital marketing. We now live in the digital age – indeed, there are more than 3 billion people connected to the internet. For every 100 people on the planet, there are 96 mobile telephone subscriptions. And more and more of our everyday objects –cuddly toys, cars, even kettles – have created an “internet of things.” It is no surprise, therefore, that companies are eager to harness this digital world. Marketers, in particular, hope that so-called digital marketing will allow them to gain new customer insights, refine customer segmentation, and communicate to customers more efficiently and effectively. They anticipate that the digital age will offer possibilities for new product innovation, advanced methods for engaging customers and original vehicles for creating brand communities. Despite the pervasiveness of digital technologies, however, digital marketing is seemingly still in its infancy. To begin, what exactly is digital marketing? The term is commonly used, but its meaning – its scope, outline, boundary and limits – is far from concise. This book explores the realities of digital marketing, with contributions from both academics and practitioners who are experts in the field

Leveraging Predictive Modeling, Machine Learning Personalization, NLP Customer Support, and AI Chatbots to Increase Customer Loyalty

AI, ML, and NLP are profoundly altering the way organizations work. With the increasing influx of data and the development of AI systems to understand it in order to solve business challenges, the excitement surrounding AI has grown. Massive datasets, computer capacity, improved algorithms, accessible algorithm libraries, and frameworks have compelled today's organizations to use AI to enhance their operations and profits. These technologies aid every kind of industry, from agriculture to finance. More specifically, AI and ML, and NLP are assisting organizations in areas such as customer service, predictive modeling, customer personalization, picture identification, sentiment analysis, offline and online document processing. The purpose of this study was twofold. We first review the several applications of AI in business and then empirically test whether these applications increase customer loyalty using the datasets of 910 firms around the world.  The datasets include the integration scores of four different AI features, namely, AI-powered customer service, predictive modeling, ML-powered personalization, and natural language processing integration. The target is the customer loyalty measure as binary. All the features are measured on a 5-pint Likert scale. We applied six different supervised machine learning algorithms, namely, Logistic regression, KNN, SVM, Decision Tree, Random Forest, and Ada boost Classifiers. the performance of each algorithm was evaluated using confusion matrices and ROC curves. The Ada boost and logistic classifiers performed better with test accuracies of 0.639 and 0.631, respectively. The decision tree and KNN had the performance with accuracies of 0.532 and 0.570, respectively.  The findings of this study highlight that by incorporating AI, ML, and NLP, businesses may analyze data to uncover what's useful, gaining valuable insights that can be used to automate processes and drive business strategies. As a result, firms that wish to remain competitive and increase customer loyalty should adopt them