Research on Spillover Effect of Paid Search Advertising Channels

With the diversification of paid search advertising channels, e-commerce enterprises are paying more and more attention on how to evaluate the effectiveness of different paid search advertising channels correctly and accurately to choose the optimal advertising channel or channels. We develop a multivariate time series model to investigate the spillover effect of paid search advertising channels based on the ad click-through rate and conversion rate, and calibrate the model using an e-commerce site\u27s web log data. We determine the long-term equilibrium relationship between each channel\u27s advertisement clicks through the co-integration test and evaluate the effect of short-term fluctuations in the interaction between each channel advertisement clicks through the vector error correction model. Based on the empirical results, this paper puts forward suggestions on the advertising strategy of this e-commerce website

HOW CAN PRODUCT TEXT SNIPPETS BENEFIT FROM ONLINE CUSTOMER REVIEWS?

Product text snippets should highlight the product features that are appealing to customers. Nevertheless, the features in current product snippets mainly are often decided based on the understanding of vendors or advertisers, and may fail to contain the features appealing to customers. This paper investigates how product text snippets generation can benefit from online customer reviews. In doing so, an automated method is designed, in which features and the opinions are extracted from online reviews, and are further used for product text snippet generation. To verify the effectiveness of the proposed method, we conduct two experiments and the results show that the extracted features and the snippet are effective in inviting potential customers, compared with the baseline ones. Experimental results demonstrate that 1) the extracted features are more appealing to customers; and 2) the snippets generated based on the extracted features are more likely to be clicked

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

AdScope : intelligent scoping of paid search campaigns using relevance feedback

Tezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.In this thesis, we propose a semi-supervised online tool called AdScope for search engine marketing. AdScope can be used for filtering out unprofitable user queries from the search campaign while at the same time allowing profitable queries only. AdScope uses relevance feedback for classifying user queries broadly into two categories as relevant or non-relevant. All queries labeled as non-relevant are excluded from the search campaign; no ad is shown to a user posing an excluded query in the future. All queries labeled as relevant are included in the search campaign as regular campaign keywords. In order to label queries, two sources of relevance feedback are used: user feedback comes in the form of clicks and conversions which are available in the search query log provided by ad broker. Advertiser feedback is collected interactively. For this purpose, we designed an active learning step where advertiser is asked to label a selected set of unlabeled queries. The feedback received is incorporated into the classification model in real time using Bayesian update. In performance tests, we observed that AdScope had the highest classification accuracy of 89.25% for queries that contain at least two terms. Furthermore, three domain experts agreed substantially with a Fleiss’ agreement score of 0.79 on the selections made by our actively learning system.Contents Abstract ii Öz iii Acknowledgments v List of Figures viii List of Tables ix Abbreviations x 1 Introduction 1 1.1 Our contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Literature Review 5 3 Methodology 9 3.1 Computation of Relevance Status Value . . . . . . . . . . . . . . . . . . . 10 3.2 User feedback and bootstrapping in AdScope . . . . . . . . . . . . . . . . 11 3.3.1 Streaming algorithm for incorporating advertiser feedbak . . . . . 12 3 .3.2 Getting advertiser feedback . . . . . . . . . . . . . . . . . . . . . . 13 3.4 Complexity analysis of AdScope . . . . . . . . . . . . . . . . . . . . . . . . 15 4 Experimental Evaluation 18 4.1 Details of our datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2 Testing methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.3 Self comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.3.1 With pre-processing vs. without pre-processing . . . . . . . . . . . 19 4.3.2Active learning in AdScope . . . . . . . . . . . . . . . . . . . . . . 19 4.4 Comparison with the state of the art . . . . . . . . . . . . . . . . . . . . . 21 4.4.1 Multinomial Naive Bayes . . . . . . . . . . . . . . . . . . . . . . . 22 4.4.2 Binary classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.4.3 Markov chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.4.4 Comparative results . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.4.5 Performance on the second dataset . . . . . . . . . . . . . . . . . . 26 5 Phrase discovery in AdScope 27 6 Conclusions 30 A Computation of Jaccard score for trigrams 32 Bibliography 3