Data processing for direct marketing through Big Data

Traditional marketing performs promotion through various channels such as news in newspapers, radio, etc., but those promotions are aimed at all people, whether or not interested in the product or service being promoted. This method usually leads to high expenses and a low response rate by potential customers. That is why, nowadays, because there is a very competitive market, mass marketing is not safe, hence specialists are focusing efforts on direct marketing. This method studies the characteristics, needs and also selects a group of customers as a target for the promotion. Direct marketing uses predictive modeling from customer data, with the aim of selecting the most likely to respond to promotions. This research proposes a platform for the processing of data flows for target customer selection processes and the construction of required predictive response models

Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda

Although social media use is gaining increasing importance as a component of firms’ portfolio of strategies, scant research has systematically consolidated and extended knowledge on social media marketing strategies (SMMSs). To fill this research gap, we first define SMMS, using social media and marketing strategy dimensions. This is followed by a conceptualization of the developmental process of SMMSs, which comprises four major components, namely drivers, inputs, throughputs, and outputs. Next, we propose a taxonomy that classifies SMMSs into four types according to their strategic maturity level: social commerce strategy, social content strategy, social monitoring strategy, and social CRM strategy. We subsequently validate this taxonomy of SMMSs using information derived from prior empirical studies, as well with data collected from in-depth interviews and a quantitive survey among social media marketing managers. Finally, we suggest fruitful directions for future research based on input received from scholars specializing in the field

Leveraging analytics to produce compelling and profitable film content

Producing compelling film content profitably is a top priority to the long-term prosperity of the film industry. Advances in digital technologies, increasing availabilities of granular big data, rapid diffusion of analytic techniques, and intensified competition from user generated content and original content produced by Subscription Video on Demand (SVOD) platforms have created unparalleled needs and opportunities for film producers to leverage analytics in content production. Built upon the theories of value creation and film production, this article proposes a conceptual framework of key analytic techniques that film producers may engage throughout the production process, such as script analytics, talent analytics, and audience analytics. The article further synthesizes the state-of-the-art research on and applications of these analytics, discuss the prospect of leveraging analytics in film production, and suggest fruitful avenues for future research with important managerial implications

How consumer digital signals are reshaping the customer journey

Marketers are adopting increasingly sophisticated ways to engage with customers throughout their journeys. We extend prior perspectives on the customer journey by introducing the role of digital signals that consumers emit throughout their activities. We argue that the ability to detect and act on consumer digital signals is a source of competitive advantage for firms. Technology enables firms to collect, interpret, and act on these signals to better manage the customer journey. While some consumers' desire for privacy can restrict the opportunities technology provides marketers, other consumers' desire for personalization can encourage the use of technology to inform marketing efforts. We posit that this difference in consumers' willingness to emit observable signals may hinge on the strength of their relationship with the firm. We next discuss factors that may shift consumer preferences and consequently affect the technology-enabled opportunities available to firms. We conclude with a research agenda that focuses on consumers, firms, and regulators

Why Generalized Structured Component Analysis Is Not Universally Preferable to Structural Equation Modeling

Contains fulltext : 111959.pdf (publisher's version ) (Open Access)27 maart 201212 p