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This study investigates the effect of keyword portfolio characteristics on sales in paid search advertising. The authors propose two keyword portfolio characteristics (variety and disparity) and examine the effects of portfolio variety and portfolio disparity on direct and indirect sales in both PC and mobile environment.

By conducting a field study at a large e-commerce platform, the authors use a negative binomial model to develop empirical findings that provide insights into paid search advertising strategies.

For main effect, (1) portfolio variety has a negative effect on direct sales. However, (2) portfolio disparity has positive effects on both direct and indirect sales. Advertising channels influence the contribution of keyword portfolio to sales. (3) On mobile devices, portfolio variety positively affects both direct and indirect sales. However, portfolio disparity negatively affects both direct and indirect sales. (4) On PCs, portfolio variety negatively affects both direct and indirect sales. However, portfolio disparity positively affects both direct and indirect sales on PC.

The findings provide advertisers with insights into how to manage keyword portfolio between mobile devices and PCs.

The current study shifts the attention from keyword to keywords (keyword portfolio), which extends the paid search literature. Moreover, it also contributes to the literature by comparing the relative effectiveness of mobile and PC search advertising.

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.

This chapter presents a summary of the literature on the influence of the Internet and other digital innovations on markets, consumers, and firms. The review leads to a list of topics in need of research in the general areas of big data, online and mobile advertising, consumer search, online privacy, online reviews, social networks, platforms for online transactions, and the impact of the Internet on retail markets, including multi-channel and omni-channel retailing. We discuss the big data approaches that have been applied to problems of targeting and positioning and suggest areas for further development of these approaches. We also discuss the emerging area of mobile advertising, which can further enhance targeting. On the consumer side, the evidence indicates that the Internet has greatly lowered the costs of search and access to retailers. Much of the consumer data are transmitted to sellers, and much of the online advertising is transmitted to consumers, through platforms, such as Google. We conclude that better models of competition among these platform firms are needed and that they need to be examined for anti-trust violations. While online retailing has grown rapidly, it still has a relatively small share of retail sales. Since sellers can combine the advantages of online and offline channels, it has been common for sellers to branch into multi-channel retailing. Given the increased availability of detailed consumer data, omni-channel selling, which emphasizes strategies for the various touchpoints that lead to a transaction, is an area for further development.

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.

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.

Despite the tremendous advancement of information technology worldwide, a few attempts have been made to uncover the effectiveness of paid internet advertising (PIA) on consumers buying decisions for retail enterprises in emerging markets. The purpose of this paper is to examine the structural relationships between consumer self-confidence, perceived intrusiveness, continuance search intention, attitude towards PIA and frequency of apps-buying decisions.

A total number of 515 online questionnaires were obtained from a sample of experienced apps shoppers who were disposed to PIA, in order to evaluate the theoretical model. VB-SEM, which is a variance-based structural equation modelling technique, was applied to assess the measurement scales and structural relationship between exogenous and endogenous constructs.

Overall, the structural results imply that the proposed model explains 0.738 per cent of variances in consumer’s continuance search intention and 0.756 per cent of the frequency of apps-buying decisions. The results support that consumer self-confidence positively influences attitude towards PIA, continuance search intention and frequency of apps-buying decisions; however, consumer self-confidence is negatively related to perceived intrusiveness. Similarly, attitude towards PIA is positively related to continuance search intention and frequency of apps-buying decision. Besides, perceived intrusiveness negatively influences attitude towards PIA, continuance search intention and frequency of apps-buying decisions. Finally, continuance search intention and the frequency of apps-buying decisions are positively related.

Despite the fact that PIA and its utilisation has been a recent growing trend in managing retail enterprises worldwide, a few studies have been conducted on possible end results of PIA, including consumer continuous apps search intention and purchase decisions.

With expenditures totaling $227 billion in 2007, prescription drug purchases are a growing portion of the total medical expenditure, and as this industry continues to grow, prescription drugs will continue to be a critical part of the larger health care industry. This chapter presents a survey on the economics of the US pharmaceutical industry, with a focus on the role of R&D and marketing, the determinants (and complications) of prescription drug pricing, and various aspects of consumer behavior specific to this industry, such as prescription drug regulation, the patient's interaction with the physician, and insurance coverage. This chapter also provides background in areas not often considered in the economics literature, such as the role of pharmacy benefit managers in prescription drug prices and the differentiation between alternative measures of prescription drug prices.

The internet has gained enormous importance as an advertising medium. At the same time, the question of marketing accountability has become one of the top areas of research for researchers and practitioners who aim at optimal marketing budget allocations. This paper aims to show that national culture has an impact on how consumer behavior is influenced by online advertising.

The paper compares the effectiveness of internet advertising in the USA and France by analyzing a real data sample with more than 1.2 million transactions using partial least squares and structural equation modeling.

It is found that on‐demand channels have a stronger effect on short‐term success than push‐channels and that this effect is strongly moderated by the culture. It is recommended that spending and efforts in the various advertising channels be adjusted to reflect the product offered and the customers to whom it is offered, as customers in both countries should be targeted by advertising in different ways. The channel effectiveness of affiliate price comparison sites and search engines has significantly different impacts on customer behavior in the USA and France.

Until now, most companies have utilized a suboptimal allocation of resources to advertising channels because of missing and misleading data. Focusing on this problem, the paper suggests that advertisers apply the methodology to identify the total effect of clicks in each advertising channel. Combining these total effects with the associated costs allows practitioners to optimize their online advertising spending.