Search results

Online advertising such as Google AdWords gives small and medium-sized enterprises access to new markets at reduced costs. The purpose of this paper is to analyse the visibility and performance of a website and to test the effectiveness of online marketing using the data provided by Google Analytics.

The authors use a class of econometric time series models with unobservable components, Structural Time Series Models (STSM). The authors allow for time-varying trends to take into account the non-stationary behaviour displayed by time series. The authors illustrate the model using daily data from a local tourist website. Three specific questions are addressed: do paid keywords campaigns increase the volume and quality of search traffic? Do paid keywords affect the volume and quality of the unpaid traffic? How do paid and unpaid keywords perform?

The results for the case study show that: first, online campaigns affect traffic volume positively but their effectiveness on traffic quality is uncertain; second, paid keywords do not affect the volume and quality of unpaid traffic; third, the increase in traffic volume is not always due to the paid keywords and the lowest quality visits come from paid traffic.

This analysis may help webmasters to design successful online advertising strategies.

This study contributes to the development of user-friendly methodologies to monitor website performance. The analysis shows that STSM is a suitable methodology to test the effectiveness of online campaigns and to assess the changes over time in the performance of a website.

This study aims to determine the antecedents and consequences of information source choice to support a purchase decision for services high in experience attributes.

The authors conduct two studies to test their propositions. Study 1 is a single-category application using data from a national survey of 974 consumers who recently made a hotel-stay purchase/reservation. Correspondence analysis was used to identify search patterns, and regression analysis was used to identify their antecedents and influence on search outcomes. Study 2 is a cross-category study using data from a survey of 422 MTurk respondents reporting on search processes across six different services contexts, including hotel reservations. In this study, the authors seek generalization of their results to other services categories.

The authors identify four dimensions that characterize what information sources consumers, on average, use together when purchasing services. It is found that loyalty program membership and consistency in service delivery across a brand’s outlets for the brands in a consumer’s evoked set are important determinants of search patterns. Search patterns partially mediate the impact of consumer characteristics, choice context and choice set characteristics on search effort and, ultimately, on price paid.

An understanding of the factors that are associated with consumers’ choices of information sources and whether these choices are systematically related to search outcomes has implications for market segmentation and for marketers’ initiatives with respect to what information content to emphasize across sources.

The contribution is an understanding of the antecedents and consequences of consumer search patterns – and what information sources consumers tend to use together, considering the diversity of both internet and non-internet sources. There are limited insights in the services literature regarding how the internet impacts information search processes.

This study is to propose a more effective and efficient analytic methodology based on within-site clickstream associated with path visualization to explore the channel dependence of consumers' latent shopping intent and the related behaviors, with which in turn to gain insight concerning the interactivity between webpages.

The primary intention of the research is to design and develop a more effective and efficient approach for exploring the consumers' latent shopping intent and the related behaviors from the clickstream data. The proposed methodology is to use text-mining package, consisting of the combination of hierarchical recurrent neural networks and Hopfield-like neural network equipped with Laplacian-based graph visualization to visualize the consumers' browsing patterns. Based on the observed interactivity between webpages, consumers' latent shopping intent and the related behaviors can be understood.

The key finding is to evidence that consumers' latent shopping intent and related behaviors within website depend on channels the consumers click through. The accessing consumers through channels of paid search and display advertising are identified and categorized as goal-directed and exploratory modes, respectively. The results also indicate that the effect of the content of webpage on the consumer's purchase intent varies with channels. This implies that website optimization and attribution of online advertising should also be channel-dependent.

This is important for the managerial and theoretical implications: First, to uncover the channel dependence of consumer's latent shopping intent and browsing behaviors would be helpful to the attribution of the online advertising for the sales promotion. Second, in the past, webmasters did not understand users' preferences and make decisions of reorganization purely on the user's browsing path (sequential page view) without appraising psychological perspective, that is, user's latent shopping intent.

This study is the first to explore the channel dependences of consumer's latent shopping intent and the related browsing behaviors through within-site clickstream associated with path visualization. The findings are helpful to the attribution of the online advertising for the sales promotion and useful for webmasters to optimize the effectiveness and usability of their websites and in turn promote the purchase decision.

Historically, research shows that out-of-town buyers of real estate are informationally disadvantaged and therefore pay higher prices compared to in-town buyers. However, with the recent advent of online housing platforms, a plethora of information about the housing market is provided for free. The purpose of this paper is to examine whether out-of-town buyers do in fact pay a premium and why, and whether this premium has decreased because of better information availability.

A hedonic regression model over a ten-year window (2005, 2015) is developed to analyze condominium transactions in Miami-Dade County. The results are validated by various robustness checks and the propensity score matching algorithm to identify a comparable control sample for 2015 in terms of relevant housing characteristics.

The results support the hypothesis that out-of-town buyers pay higher prices for real estate, compared to their local counterparts, and that both search costs and anchoring cause a premium in both years, whereas wealth only plays a significant role in 2005. The premium because of search costs, and therefore, information availability has decreased slightly over time.

This is the first out-of-town paper that compares two points in time versus a single cross-section analysis. Besides the premium caused by information asymmetry/search costs measured by distance and the anchoring effect, the regression model is extended by the wealth effect.

Google commands approximately 70 per cent of search market share worldwide, resulting in businesses investing heavily in search engine advertising on Google to target potential customers. Recently, Google changed the way in which content and ads were displayed on the search engine results page. This reshuffling of content and ads is expected to affect the advertisers who advertise on Google and/or use it to drive traffic to their websites. The purpose of this study is to analyze the impact of these changes on various stakeholders.

Data have been collected from various sources on the internet including blogs and discussion forums. Netnography has been used as it allows a detailed evaluation of the consumers’ needs, wants and choices in a virtual space.

The average cost-per-click for ads on the top positions is expected to increase. Advertisers whose ads usually occupy the lower positions would be adversely affected. To counter this, more emphasis should be placed on ad extensions and on product listing ads. In addition, organizations would benefit from increased efforts on search engine optimization.

A variety of coping strategies have been developed that can help marketers to successfully navigate through the change, including the use of ad extensions and the use of product listing ads.

This practice-focused paper offers guidelines for digital marketers to use sponsored search more effectively as part of their arsenal in light of some important changes recently made by Google. The potential of netnography as a research methodology has also been expanded by using it in a novel setting and in drawing up actionable insights.

Librarians have traditionally helped users meet their information needs by providing access to vetted sources. This changed when Internet access to the Web was introduced into the library service equation. While users now have access to more information than ever before, they must rely on search engines to retrieve and rank the results of their searches before research can begin. A problem with ranked retrieval is that many search engines participate in the practice of pay‐for‐placement advertising, which can affect the order of ranking, and may not be apparent as advertising. This practice introduces a new variation in the relationship between user and advertiser that has not existed in libraries before.

We review the implications of the mobile technology for different stages of the consumer path to purchase including awareness, search, evaluation, store visit, and product choice. Real-time and location-specific access to information and products are identified as distinguishing characteristics of mobile devices. While the literature on digital marketing is well developed, knowledge of the effects on the consumer path to purchase in the presence of dynamic and location-specific information is still scarce. Path to purchase models need to recognize the central and powerful role of user-generated content. Better management of marketing resources would require models that connect investments in mobile marketing to sales, and also model the synergies among different digital and offline media. We conclude with a framework that connects mobile media impressions to product choice, in the presence of other marketing media, and consumer and firm feedback loops.