121,916 research outputs found
Online advertising: analysis of privacy threats and protection approaches
Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft
Faulty Metrics and the Future of Digital Journalism
This report explores the industry of Internet measurement and its impact on news organizations working online. It investigates this landscape through a combination of documentary research and interviews with measurement companies, trade groups, advertising agencies, media scholars, and journalists from national newspapers, regional papers, and online-only news ventures
Marketing management of a successful e-business
Marketing management occupies an increasingly important position in the business world, as well as in the sphere of electronic commerce. Some participants, however, underestimate the importance of this marketing support, which may be one of the major causes of the failure and inability of some companies operating on the Internet to grow. The aim of this paper is to develop an effective marketing management process
model, which can significantly contribute to the increased competitiveness of companies operating on the Internet. The validity of this model is then applied on a Czech e-shop, which has long been one of the leaders of the Czech Internet market. To achieve the objective of this paper the current situation will be analysed, and synthesis of the findings from research literature as well as modelling using the methods of abstraction and specification will be performed. This article is focused on Czech Internet market. Results of the survey (case study) will be used for further research in the field of e-business
Pioneers of Influence Propagation in Social Networks
With the growing importance of corporate viral marketing campaigns on online
social networks, the interest in studies of influence propagation through
networks is higher than ever. In a viral marketing campaign, a firm initially
targets a small set of pioneers and hopes that they would influence a sizeable
fraction of the population by diffusion of influence through the network. In
general, any marketing campaign might fail to go viral in the first try. As
such, it would be useful to have some guide to evaluate the effectiveness of
the campaign and judge whether it is worthy of further resources, and in case
the campaign has potential, how to hit upon a good pioneer who can make the
campaign go viral. In this paper, we present a diffusion model developed by
enriching the generalized random graph (a.k.a. configuration model) to provide
insight into these questions. We offer the intuition behind the results on this
model, rigorously proved in Blaszczyszyn & Gaurav(2013), and illustrate them
here by taking examples of random networks having prototypical degree
distributions - Poisson degree distribution, which is commonly used as a kind
of benchmark, and Power Law degree distribution, which is normally used to
approximate the real-world networks. On these networks, the members are assumed
to have varying attitudes towards propagating the information. We analyze three
cases, in particular - (1) Bernoulli transmissions, when a member influences
each of its friend with probability p; (2) Node percolation, when a member
influences all its friends with probability p and none with probability 1-p;
(3) Coupon-collector transmissions, when a member randomly selects one of his
friends K times with replacement. We assume that the configuration model is the
closest approximation of a large online social network, when the information
available about the network is very limited. The key insight offered by this
study from a firm's perspective is regarding how to evaluate the effectiveness
of a marketing campaign and do cost-benefit analysis by collecting relevant
statistical data from the pioneers it selects. The campaign evaluation
criterion is informed by the observation that if the parameters of the
underlying network and the campaign effectiveness are such that the campaign
can indeed reach a significant fraction of the population, then the set of good
pioneers also forms a significant fraction of the population. Therefore, in
such a case, the firms can even adopt the naive strategy of repeatedly picking
and targeting some number of pioneers at random from the population. With this
strategy, the probability of them picking a good pioneer will increase
geometrically fast with the number of tries
Interactive Food and Beverage Marketing: Targeting Children and Youth in the Digital Age
Looks at the practices of food and beverage industry marketers in reaching youth via digital videos, cell phones, interactive games and social networking sites. Recommends imposing governmental regulations on marketing to children and adolescents
Twitter’s big hitters
We describe the results of a new computational experiment on Twitter data. By listening to Tweets on a selected topic, we generate a dynamic social interaction network. We then apply a recently proposed dynamic network analysis algorithm that ranks Tweeters according to their ability to broadcast information. In particular, we study the evolution of importance rankings over time. Our presentation will also describe the outcome of an experiment where results from automated ranking algorithms are compared with the views of social media experts
Predicting mobile advertising response using consumer colocation networks
Building on results from economics and consumer behavior, the authors theorize that consumers' movement patterns are informative of their product preferences, and this study proposes that marketers monetize this information using dynamic networks that capture colocation events (when consumers appear at the same place at approximately the same time). To support this theory, the authors study mobile advertising response in a panel of 217 subscribers. The data set spans three months during which participants were sent mobile coupons from retailers in various product categories through a smartphone application. The data contain coupon conversions, demographic and psychographic information, and information on the hourly GPS location of participants and on their social ties in the form of referrals. The authors find a significant positive relationship between colocated consumers' response to coupons in the same product category. In addition, they show that incorporating consumers' location information can increase the accuracy of predicting the most likely conversions by 19%. These findings have important practical implications for marketers engaging in the fast-growing location-based mobile advertising industry
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