Explore the vast range of titles available.

Despite the increasing popularity of pay-per-click (PPC) advertising and search engine optimization within the financial industry, there is a notable lack of research on the effectiveness of PPC on bank customers' continuous search intention and banking services intention. This study aims to fill this gap by investigating the use of PPC as a tool on customers' search intention and continuous services intention in a retail banking context. Utilizing a quantitative design, we collected data and employed maximum likelihood estimation (MLE) for path analysis to analyze the empirical data. Our findings reveal several substantial results. Firstly, continuous search intentions significantly influence continuous banking services intentions. Secondly, attitude toward PPC advertising significantly affects both continuous search and banking services intentions. Thirdly, satisfaction with PPC advertising is crucial in shaping attitudes toward PPC, continuous search intention, and continuous banking services intention. Fourthly, perceived usefulness directly influences attitudes toward PPC, satisfaction with PPC, and continuous search intention. Lastly, while PPC advertising's perceived ease of use and perceived confirmation are linked to perceived usefulness, they do not directly affect attitude toward PPC. By adopting a dual intentions approach, this study contributes to banking literature by highlighting the importance of understanding the distinct roles of PPC attributes in shaping short- and long-term customer behavioral intentions.

We study the bidding strategies of vertically differentiated firms that bid for sponsored search advertisement positions for a keyword at a search engine. We explicitly model how consumers navigate and click on sponsored links based on their knowledge and beliefs about firm qualities. Our model yields several interesting insights; a main counterintuitive result we focus on is the \"position paradox.\" The paradox is that a superior firm may bid lower than an inferior firm and obtain a position below it, yet it still obtains more clicks than the inferior firm. Under a pay-per-impression mechanism, the inferior firm wants to be at the top where more consumers click on its link, whereas the superior firm is better off by placing its link at a lower position because it pays a smaller advertising fee, but some consumers will still reach it in search of the higher-quality firm. Under a pay-per-click mechanism, the inferior firm has an even stronger incentive to be at the top because now it only has to pay for the consumers who do not know the firms' reputations and, therefore, can bid more aggressively. Interestingly, as the quality premium for the superior firm increases, and/or if more consumers know the identity of the superior firm, the incentive for the inferior firm to be at the top may increase. Contrary to conventional belief, we find that the search engine may have the incentive to overweight the inferior firm's bid and strategically create the position paradox to increase overall clicks by consumers. To validate our model, we analyze a data set from a popular Korean search engine firm and find that (i) a large proportion of auction outcomes in the data show the position paradox, and (ii) sharp predictions from our model are validated in the data.

We consider a multiround auction setting motivated by pay-per-click auctions for Internet advertising. In each round the auctioneer selects an advertiser and shows her ad, which is then either clicked or not. An advertiser derives value from clicks; the value of a click is her private information. Initially, neither the auctioneer nor the advertisers have any information about the likelihood of clicks on the advertisements. The auctioneer's goal is to design a (dominant strategies) truthful mechanism that (approximately) maximizes the social welfare. If the advertisers bid their true private values, our problem is equivalent to the multi-armed bandit problem, and thus can be viewed as a strategic version of the latter. In particular, for both problems the quality of an algorithm can be characterized by regret, the difference in social welfare between the algorithm and the benchmark which always selects the same \"best\" advertisement. We investigate how the design of multi-armed bandit algorithms is affected by the restriction that the resulting mechanism must be truthful. We find that deterministic truthful mechanisms have certain strong structural properties---essentially, they must separate exploration from exploitation---and they incur much higher regret than the optimal multi-armed bandit algorithms. Moreover, we provide a truthful mechanism which (essentially) matches our lower bound on regret. [PUBLICATION ABSTRACT]

Online paid advertising is a dynamic form of online marketing that requires precision and a quick response to market changes. Automated tools are used to greatly simplify the process of creating and managing Pay-Per-Click (PPC) campaigns. The purpose of this research is to evaluate the impact of implementing an automated PPC management tool (Dotidot) on campaign performance in a travel agency compared with standard Google Ads campaigns. A structured multi-criteria procedure is first applied to select the most suitable tool for the Czech and Slovak markets. The core contribution of the paper is the observational case study of Dotidot and its performance comparison. The subject of the research is a comparative analysis of three PPC automated tools: Conviu, Dotidot, and BlueWinston. The significance of this topic lies in highlighting the relatively new possibilities of digital marketing and choosing the right tool for using automation. This research can also stimulate other researchers in this field and expand knowledge. The analysis is carried out using the method of systematic comparison based on established criteria, the result of which is a recommendation of the preferred tool and a brief discussion of its implementation options. The analyzed tools are used mainly on the Czech and Slovak markets and are oriented towards e-commerce (electronic commerce). In addition to e-commerce, a case study of the use of digital promotion in sports organizations is also presented. The research results in a systematic comparison of Conviu, Dotidot, and BlueWinston tools according to predefined criteria. The research also includes a brief discussion on the possibilities of implementing the recommended tool in practice, as well as an assessment of its benefits and limitations.

PurposeAnalysis of the publisher's behavior plays a vital role in identifying fraudulent publishers in the pay-per-click model of online advertising. However, the vast amount of raw user click data with missing values pose a challenge in analyzing the conduct of publishers. The presence of high cardinality in categorical attributes with multiple possible values has further aggrieved the issue.Design/methodology/approachIn this paper, gradient tree boosting (GTB) learning is used to address the challenges encountered in learning the publishers' behavior from raw user click data and effectively classifying fraudulent publishers.FindingsThe results demonstrate that the GTB effectively classified fraudulent publishers and exhibited significantly improved performance as compared to other learning methods in terms of average precision (60.5 %), recall (57.8 %) and f-measure (59.1%).Originality/valueThe experiments were conducted using publicly available multiclass raw user click dataset and eight other imbalanced datasets to test the GTB's generalizing behavior, while training and testing were done using 10-fold cross-validation. The performance of GTB was evaluated using average precision, recall and f-measure. The performance of GTB learning was also compared with eleven other state-of-the-art individual and ensemble classification models.

The article aims to investigate the role and features of the content and test proposed indicators for evaluating its effectiveness within the marketing enterprise communications with the client using Internet technologies. The differences between traditional content communications and content communications using unifying content have been presented. Based on review of scientific works and statistic data and results of the studies, a system of indicators for evaluating the level of content supply cost-effectiveness is proposed: average cost of attracting one user of website, coefficient of lead generation of the Internet website, average cost of attracting one lead, content effectiveness ratio, average purchase in consequence of content supply, profit from the sale of goods in consequence of content supply, profitability of using the Internet marketing tools. This system of indicators allows monitoring of content effectiveness at key stages of enterprise-customer interaction. The proposed indicators of content effectiveness were tested on two leading enterprises in the field of water purification and water supply in Ukraine: “ZIKO Company” and “BWT Ukraine”. It was concluded that only a high quality of content supply could provide an enterprise with a high level of customer conversion and significant visibility of its website in different search engines.

In this paper, we analyze the relationship between performance and use of brand terms in the key phrases that link advertisements to searcher queries. We use data that consist of more than 2.5 million daily records from a key word advertising campaign of a major U.S. retailer. The campaign spanned nearly four years, involved approximately $8 million in advertising cost, and generated more than $23 million in sales. We categorize key phrases and advertisements as either brand focused or non-brand focused. Using analysis of variance in a 2 × 2 design, we analyze use of branded terms on the critical key word advertising metrics of number of clicks, cost per click, sales revenue generated, number of orders, number of items ordered, and return on advertising cost, as well as number of impressions triggered by these key phrases. Therefore, we investigate a significant spectrum of user actions and consumer behaviors in a sponsored search campaign. Our findings show that there is a significant advantage to matching branding terms in key phrases and advertisements relative to any other combination of key phrase or advertisement for all the metrics examined. A combination of a branded phrase and a branded advertisement generated 15 times more sales revenue than any other combination of phrase and advertisement. Therefore, a focus by key word advertisers on branded terms for search engine ads could be quite beneficial for both the effectiveness and efficiency of key word advertising. The implication for online advertising and key word search in the e-commerce domain, especially for large retailers, is that brand mentions in both key phrase and advertisements correlate with higher conversions.

Advertisement in dedicated webpage spaces or in search engines sponsored slots is usually sold using auctions, with a payment rule that is either per impression or per click. But advertisers can be both sensitive to being viewed (brand awareness effect) and being clicked (conversion into sales). In this paper, we generalize the auction mechanism by including both pricing components: the advertisers are charged when their ad is displayed, and pay an additional price if the ad is clicked. Applying the results for Vickrey–Clarke–Groves auctions, we show how to compute payments to ensure incentive compatibility from advertisers as well as maximize the total value extracted from the advertisement slot(s). We provide tight upper bounds for the loss of efficiency due to applying only pay-per-click (or pay-per-view) pricing instead of our scheme. Those bounds depend on the joint distribution of advertisement visibility and population likelihood to click on ads, and can help identify situations where our mechanism yields significant improvements. We also describe how the commonly used generalized second price auction can be extended to this context.

In this research, we evaluate the effectiveness of the buying funnel as a model for understanding consumer interaction with keyword advertising campaigns on web search engines. We analyze data of nearly 7 million records from a 33 month, $56 million (US) search engine marketing campaign of a major US retailer. We classify key phrases used in this campaign into stages of the buying funnel (i.e., Awareness, Research, Decision, and Purchase) and then compare the consumer behaviors associated with each stage of the buying funnel using the critical keyword advertising metrics of impressions, clicks, cost-per-click, sales revenue, orders, and items sold. Findings from our analysis show that the stages from the buying funnel are effective for classifying types of queries, with statistically different consumer behaviors for all attributes among all stages. However, results also indicate that the buying funnel model does not represent the actual process that consumer engage in when contemplating a potential purchase, as the stages do not seem to be associated with expected consumer actions as predicted by the model. Results show that Awareness key phrases cost less and generate more sales revenue than Purchase queries, indicating that these broader phases can be a lucrative advertising segment for sponsored search campaigns. The results reported in this paper are important to researchers interested in understanding online consumers interaction with search engines and beneficial to search engine marketers striving to design successful advertising campaigns. Insights from this research could produce keyword advertising efforts being more effectively targeted to consumers in order to achieve campaign goals. [PUBLICATION ABSTRACT]

Background: It is imperative that commercial websites should rank highly in search engine result pages because these provide the main entry point to paying customers. There are two main methods to achieve high rankings: search engine optimisation (SEO) and pay-per-click (PPC) systems. Both require a financial investment - SEO mainly at the beginning, and PPC spread over time in regular amounts. If marketing budgets are applied in the wrong area, this could lead to losses and possibly financial ruin. Objectives: The objective of this research was to investigate, using three real-world case studies, the actual expenditure on and income from both SEO and PPC systems. These figures were then compared, and specifically, the cost per acquisition (CPA) was used to decide which system yielded the best results. Methodology: Three diverse websites were chosen, and analytics data for all three were compared over a 3-month period. Calculations were performed to reduce the figures to single ratios, to make comparisons between them possible. Results: Some of the resultant ratios varied widely between websites. However, the CPA was shown to be on average 52.1 times lower for SEO than for PPC systems. Conclusion: It was concluded that SEO should be the marketing system of preference for e-commerce-based websites. However, there are cases where PPC would yield better results – when instant traffic is required, and when a large initial expenditure is not possible.