We introduce a new model of competition on growing networks. This extends the preferential attachment model, with the key property that node choices evolve simultaneously with the network. When a new node joins the network, it chooses neighbours by preferential attachment, and selects its type based on the number of initial neighbours of each type. The model is analysed in detail, and in particular, we determine the possible proportions of the various types in the limit of large networks. An important qualitative feature we find is that, in contrast to many current theoretical models, often several competitors will coexist. This matches empirical observations in many real-world networks.
We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective. Marketing practitioners and theorists routinely cite the power of the personal referral on customer behaviour. However, relatively few companies have tried to harness the power of word of mouth (WOM). Scholars have been pondering WOM over 2400 years, although modern marketing research into WOM started only relatively recently, in the post-war 1940s. WOM can be characterized by valence, focus, timing, solicitation and degree of management intervention. Most recent WOM research has been conducted from a customer-to-customer perspective, even though WOM is found in other contexts such as influence, employee and recruitment markets. Marketing research into WOM has attempted to answer two questions. What are the antecedents of WOM? What are the consequences of WOM? This paper integrates that research into a contingency model and attempts to identify researchable gaps in our knowledge. The spread of new ideas, behaviors or technologies has been extensively studied using epidemic models. Here we consider a model of diffusion where the individualsʼ behavior is the result of a strategic choice. We study a simple coordination game with binary choice and give a condition for a new action to become widespread in a random network. We also analyze the possible equilibria of this game and identify conditions for the coexistence of both strategies in large connected sets. Finally we look at how can firms use social networks to promote their goals with limited information. Our results differ strongly from the one derived with epidemic models and show that connectivity plays an ambiguous role: while it allows the diffusion to spread, when the network is highly connected, the diffusion is also limited by high-degree nodes which are very stable. Online feedback mechanisms harness the bidirectional communication capabilities of the Internet to engineer large-scale, word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a much wider impact on organizations. Their growing popularity has potentially important implications for a wide range of management activities such as brand building, customer acquisition and retention, product development, and quality assurance. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based feedback mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to their design, evaluation, and use. It provides an overview of relevant work in game theory and economics on the topic of reputation. It discusses how this body of work is being extended and combined with insights from computer science, management science, sociology, and psychology to take into consideration the special properties of online environments. Finally, it identifies opportunities that this new area presents for operations research management science (OR MS) research.  For this segment…Aim to… Example: Telecommunications CompanyPayoﬀ : Telecommunications CompanyAﬄ uents Make them Champi-ons by encouraging them to refer more new customers while maintaining their highly valuable pur-chasing behavior.Sent Aﬄ uents direct-mail promotions oﬀ ering a @math 190, a 388 increase.Advocates Turn them into Champions by increasing their CLV without compromis-ing their CRV.Focused on cross-selling and up-selling company’s products; for example, by oﬀ ering bundled products and giving discounts to customers signing one-year contracts.Average CLV increased approximately $110, a 61 improvement.Misers Move them to any other segment by persuading them to buy more products The effects of word-of-mouth (WOM) communications and specific attribute information on product evaluations were investigated. A face-to-face WOM communication was more persuasive than a printed format (experiment 1). Although a strong WOM effect was found, this effect was reduced or eliminated when a prior impression of the target brand was available from memory or when extremely negative attribute information was presented (experiment 2). The results suggest that diverse, seemingly unrelated judgmental phenomena--such as the vividness effect, the perseverance effect, and the negativity effect--can be explained through the accessibility-diagnosticity model. Copyright 1991 by the University of Chicago. This paper analyzes a model of rational word-of-mouth learning, in which successive generations of agents make once-and-for-all choices between two alternatives. Before making a decision, each new agent samples N old ones and asks them which choice they used and how satisfied they were with it. If (a) the sampling rule is “unbiased” in the sense that the samples are representative of the overall population, (b) each player samples two or more others, and (c) there is any information at all in the payoff observations, then in the long run every agent will choose the same thing. If in addition the payoff observation is sufficiently informative, the long-run outcome is efficient. We also investigate a range of biased sampling rules, such as those that over-represent popular or successful choices, and determine which ones favor global convergence towards efficiency. Though word-of-mouth (w-o-m) communications is a pervasive and intriguing phenomenon, little is known on its underlying process of personal communications. Moreover as marketers are getting more interested in harnessing the power of w-o-m, for e-business and other net related activities, the effects of the different communications types on macro level marketing is becoming critical. In particular we are interested in the breakdown of the personal communication between closer and stronger communications that are within an individual's own personal group (strong ties) and weaker and less personal communications that an individual makes with a wide set of other acquaintances and colleagues (weak ties).