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This chapter integrates research that highlights and demonstrates the importance of the marketing mix and customer attitudinal metrics in influencing the customer’s path to purchase. A key objective of this chapter is the provision of an integrative conceptual framework that links marketing actions to customer mindset metrics along the consumer’s path to purchase and the identification of the mechanisms by which customer mindset metrics contribute to consumer purchase journey. Specifically, it delineates two routes for the effects to manifest on sales: the “mindset route” where marketing actions influence customer mindset metrics, which in turn influence brand performance, or the “transactions route” where marketing actions influence market performance directly without influencing the intermediate mindset metrics. A second objective is to identify empirical patterns on incorporating marketing mix and mindset metrics along the path to purchase by reviewing key papers in this domain. Finally, the chapter concludes with the formulation of a rich, forward-looking research agenda on the customer mindset metrics – path to purchase link.

This chapter presents a systematic review of over 30 types of online marketing methods. It describes different methods like email marketing, social network marketing, in-game marketing and augmented reality marketing, among other approaches. The researchers discuss that the rationale for using these online marketing strategies is to increase brand awareness, customer-centric marketing and consumer loyalty. They shed light on various personalization methods including recommendation systems and user-generated content in their taxonomy of online marketing terms. Hence, they explain how these online marketing methods are related to each other. The researchers contend that the boundaries between online marketing methods have not been clarified enough within the academic literature. Therefore, this chapter provides a better understanding of different online marketing methods. A review of the literature suggests that the “oldest” online marketing methods including the email and the websites are still very relevant for today’s corporate communication. In conclusion, the researchers put forward their recommendations for future research about contemporary online marketing methods.

The journalism profession has radically changed due to the digitisation and the development of new media. As content is moving online, rapidly evolving Internet technologies have affected basic journalistic work processes. In this context, changes in technology as well as audience engagement have greatly expanded the skills required to be a professional journalist nowadays. A number of studies have shown that search engines constitute an important source of the traffic to online news outlets around the world, identifying the significance of top rankings in search results. Concurrently, in the digital age, the interest in monitoring online activities as well as the significance of studying the traffic data has intensified. This chapter summarises the major findings of two studies regarding the use and impact of SEO and web analytics on news websites and journalism profession in Greece. Through examination of a sample of Greek journalists and several Greek news websites, it aims to provide new insights in the field of digital journalism.

Allocating budget optimally to marketing channels is an increasingly difficult venture. This difficulty is compounded by an increase in the number of marketing channels, a rise in siloed data between marketing technologies, and a decrease in individually identifiable data due to legislated privacy policies. The authors explore the rich attribution modeling literature and discuss the different model types and approaches previously used by practitioners and researchers. They also investigate the changing landscape of marketing attribution, discuss the advantages and disadvantages of different data handling approaches (i.e., aggregate vs. individualistic data), and present a research agenda for future attribution research.

Contemporary businesses face rapidly evolving changes and complexities that challenge their respective managerial responses and capabilities. The natures of information and communication systems, ways of doing business, knowledge-transfer methods, diffusion channels of innovation, and industrial habitus are shifting. Additionally, methods, concepts, and frameworks to study these challenges need to be in accordance.

Many of these features characterizing the new business environment influence not only the consumer business, but also the business-to-business (B2B) sectors and their ways of functioning. Interestingly, the influence also connects domestic with international business through the global connectedness. This is particularly visible in marketing communication, as the difference between domestic and international business communication has further diminished due to digital and virtual dimensions and applications. In this new age, it is assumed that new ventures and small- and medium-sized enterprises can turn their vulnerabilities and size constraints into competitive advantages by addressing these challenges with efficient social media usage. To address this technology-enabled dimension of B2B relations, the authors present a case study illustrating how a firm advances its relationship management and communication by introducing social media instruments. The study contributes to relationship management and international marketing communication and provides new insights into the workings of social media within the B2B context.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

While Big Data offer marketing managers information that is high in volume, variety, velocity, and veracity (the 4Vs), these features wouldn’t necessarily improve their decision-making. Managers would still be vulnerable to confirmation bias, control illusions, communication problems, and confidence issues (the 4Cs). The authors argue that traditional remedies for such biases don’t go far enough and propose a lean start-up approach to data-based learning in marketing management. Specifically, they focus on the marketing analytics component of Big Data and how adaptations of the lean start-up methodology can be used in some combination with such analytics to help marketing managers improve their decision-making and innovation process. Beyond the often discussed technical obstacles and operational costs associated with handling Big Data, this chapter contributes by analyzing the various learning and decision-making problems that can emerge once the 4Vs of Big Data have materialized.