Using big data for customer centric marketing

This chapter deliberates on “big data” and provides a short overview of business intelligence and emerging analytics. It underlines the importance of data for customer-centricity in marketing. This contribution contends that businesses ought to engage in marketing automation tools and apply them to create relevant, targeted customer experiences. Today’s business increasingly rely on digital media and mobile technologies as on-demand, real-time marketing has become more personalised than ever. Therefore, companies and brands are striving to nurture fruitful and long lasting relationships with customers. In a nutshell, this chapter explains why companies should recognise the value of data analysis and mobile applications as tools that drive consumer insights and engagement. It suggests that a strategic approach to big data could drive consumer preferences and may also help to improve the organisational performance.peer-reviewe

Real-Time Push Mobile Marketing Strategy: To What Extent Do Time and Relevance Matter?

The ubiquity of the smartphone has proven disruptive. The relevance of this medium can be observed through time spent on mobile media, google mobile search numbers, and direct and indirect sales generated by mobile devices. Consumer expectations of firms have likewise increased, and there is now an anticipation of readily reliable, responsive, and personalized services to support consumers’ everyday activities whenever they need it. Prior research focused on the following themes: mobile marketing strategy, permission marketing, proximity marketing, topicality, and utility. Empirical gaps were identified in the real-time mobile and push mobile marketing domain. A quantitative engaged scholarship research method was utilized to investigate this phenomenon empirically. In partnership with an online information marketplace, an empirical investigation was undertaken via an experiment that used real mobile application users. The empirical findings from the study have several possible implications. First, prior research suggests that mobile marketing is time-sensitive, but consumers require some lead time to respond to the communication. However, this study provides evidence that push mobile communication is different. Unlike traditional mobile marketing, real-time communication, and content topicality work together to increase consumer engagement in push mobile communication. Second, mobile application users would like a guided experience that is both relevant and in real-time. Failing to engage users with any communication or provide a guided experience on the mobile application is as counterproductive as sending users a push communication that is neither relevant nor in real-time. Third, in certain business contexts, typicality takes priority over the timing of the communication. When the business context is ephemeral in nature, timing and topicality are of equal importance. The study contributes to the research by plugging the real-time and push mobile communication literature gap. The study contributes to practice by providing a push mobile marketing framework for firms seeking to orchestrate a sound push mobile communication strategy. Finally, the study acts as a catalyst to a call for research on the scarcely explored areas of real-time and push mobile marketing to move the field forward

Galileo and EGNOS as an asset for UTM safety and security

GAUSS (Galileo-EGNOS as an Asset for UTM Safety and Security) is a H2020 project1 that aims at designing and developing high performance positioning systems for drones within the U-Space framework focusing on UAS (Unmanned Aircraft System) VLL (Very Low Level) operations. The key element within GAUSS is the integration and exploitation of Galileo and EGNOS exceptional features in terms of accuracy, integrity and security, which will be key assets for the safety of current and future drone operations. More concretely, high accuracy, authentication, precise timing (among others) are key GNSS (Global Navigation Satellite System) enablers of future integrated drone operations under UTM (UAS Traffic Management) operations, which in Europe will be deployed under U-Space [1]. The U-Space concept helps control, manage and integrate all UAS in the VLL airspace to ensure the security and efficiency of UAS operations. GAUSS will enable not only safe, timely and efficient operations but also coordination among a higher number of RPAS (Remotely Piloted Aircraft System) in the air with the appropriate levels of security, as it will improve anti-jamming and anti-spoofing capabilities through a multi-frequency and multi-constellation approach and Galileo authentication operations. The GAUSS system will be validated with two field trials in two different UTM real scenarios (in-land and sea) with the operation of a minimum of four UTM coordinated UAS from different types (fixed and rotary wing), manoeuvrability and EASA (European Aviation Safety Agency) operational categories. The outcome of the project will consist of Galileo-EGNOS based technological solutions to enhance safety and security levels in both, current UAS and future UTM operations. Increased levels of efficiency, reliability, safety, and security in UAS operations are key enabling features to foster the EU UAS regulation, market development and full acceptance by the society.Peer ReviewedPostprint (author's final draft

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure