Social and attitudinal determinants of viral marketing dynamics

At this time of uncertainty, viral marketing is emerging as one of the most intriguing communication strategies, due to low cost and the results it obtains. However, the success of this kind of practice depends on a range of factors including what we explore and refer to in the present research as the individual’s “viral dynamics”. We thus propose a causal model in which viral dynamics is determined by the individual’s social capital and prior attitudes. Based on a survey of young adults, the authors test the effects of structural and relational capital as well as attitudes on viral dynamics. The results evidence that the individual’s connectedness in the email network does not impact viral dynamics, whereas the individual’s integration and relationship with the network and the attitudes towards viral messages prove critical to the individual involved in the receiving-forwarding process.     

Discovering influencers for marketing in the blogosphere

Discovering influential bloggers will not only allow us to understand better the social activities taking place in the blogosphere, but will also provide unique opportunities for sales and advertising. In this paper, we develop an MIV (marketing influential value) model to evaluate the influential strength and identify the influential bloggers in the blogosphere. We analyze three dimensions of blog characteristics (network-based, content-based, and activeness-based factors) and utilize an artificial neural network (ANN) to discover potential bloggers. Based on peer and official evaluations, the experimental results show that the proposed framework outperforms two social-network-based methods (out-degree and betweenness centrality algorithms) and two content-based mechanisms (review rating and popular author approaches). The proposed framework can be effectively applied to support marketers or advertisers in promoting their products or services.     

Viral marketing for dedicated customers

Viral marketing has attracted considerable concerns in recent years due to its novel idea of leveraging the social network to propagate the awareness of products. Specifically, viral marketing first targets a limited number of users (seeds) in the social network by providing incentives, and these targeted users would then initiate the process of awareness spread by propagating the information to their friends via their social relationships. Extensive studies have been conducted for maximizing the awareness spread given the number of seeds (the Influence Maximization problem). However, all of them fail to consider the common scenario of viral marketing where companies hope to use as few seeds as possible yet influencing at least a certain number of users. In this paper, we propose a new problem, called J-MIN-Seed, whose objective is to minimize the number of seeds while at least J users are influenced. J-MIN-Seed, unfortunately, is NP-hard. Therefore, we develop an approximate algorithm which can provide error guarantees for J-MIN-Seed. We also observe that all existing studies on viral marketing assume that all users in the social network are of interest for the product being promoted (i.e., all users are potential consumers of the product), which, however, is not always true. Motivated by this phenomenon, we propose a new paradigm of viral marketing where the company can specify which types of users in the social network are of interest when promoting a specific product. Under this new paradigm, we re-define our J-MIN-Seed problem as well as the Influence Maximization problem and design some algorithms with provable error guarantees for the new problems. We conducted extensive experiments on real social networks which verified the effectiveness of our algorithms.     

“With a little help from new friends”: Boosting information cascades in social networks based on link injection

We investigate information cascades in the context of viral marketing applications. Recent research has identified that communities in social networks may hinder cascades. To overcome this problem, we propose a novel method for injecting social links in a social network, aiming at boosting the spread of information cascades. Unlike the proposed approach, existing link prediction methods do not consider the optimization of information cascades as an explicit objective. In our proposed method, the injected links are being predicted in a collaborative-filtering fashion, based on factorizing the adjacency matrix that represents the structure of the social network. Our method controls the number of injected links to avoid an “aggressive” injection scheme that may compromise the experience of users. We evaluate the performance of the proposed method by examining real data sets from social networks and several additional factors. Our results indicate that the proposed scheme can boost information cascades in social networks and can operate as a “people recommendations” strategy complementary to currently applied methods that are based on the number of common neighbors (e.g., “friend of friend”) or on the similarity of user profiles.     

Automatic detection of cohesive subgroups within social hypertext: A heuristic approach

The problem of identifying cohesive subgroups in social hypertext is reviewed. A computationally efficient three-step framework for identifying cohesive subgroups is proposed, referred to as the Social Cohesion Analysis of Networks (SCAN) method. In the first step of this method (Select), people within a social network are screened using a level of network centrality to select possible subgroup members. In the second step (Collect), the people selected in the first step are collected into subgroups identified at each point in time using hierarchical cluster analysis. In the third step (Choose), similarity modeling is used to choose cohesive subgroups based on the similarity of subgroups when compared across different points in time. The application of this SCAN method is then demonstrated in a case study where a subgroup is automatically extracted from a social network formed based on the online interactions of a group of about 150 people that occurred over a two-year period. In addition, this paper also demonstrates that similarity-based cohesion can provide a different, and in this case more compelling, subgroup representation than a method based on splitting a hierarchical clustering dendrogram using an optimality criterion.     

Cognitive predictors of consumers’ intention to comply with social marketing email appeals

Email is used increasingly by social marketers to appeal to consumers, however, relatively little is known regarding the cognitive processes which lead consumers to comply with actions that marketers request in email messages. This exploratory study tests the direct effects of five cognitive factors that characterize the message receiver on intention to comply with an email appeal. These cognitive factors are benefit goals and cost goals related to the message, trusting beliefs in the message sender, involvement with the message, and perceived effort of complying with the appeal. We find four of the five factors are significant predictors of intention to comply, jointly explaining 70% of variance in this measure.     

A fast LDL-factorization approach for large sparse positive definite system and its application to one-to-one marketing optimization computation

LDL-factorization is an efficient way of solving Ax=b for a large symmetric positive definite sparse matrix A.This paper presents a new method that further improves the efficiency of LDL-factorization.It is based on the theory of elimination trees for the factorization factor.It breaks the computations involved in LDL-factorization down into two stages:1) the pattern of nonzero entries of the factor is predicted,and 2) the numerical values of the nonzero entries of the factor are computed.The factor is stored using the form of an elimination tree so as to reduce memory usage and avoid unnecessary numerical operations.The calculation results for some typical numerical examples demonstrate that this method provides a significantly higher calculation efficiency for the one-to-one marketing optimization algorithm.     

A new knowledge-based constrained clustering approach: Theory and application in direct marketing

Clustering has always been an exploratory but critical step in the knowledge discovery process. Often unsupervised, the clustering task received a huge interest when reinforced by different kinds of inputs provided by the user. This paper presents an approach giving the possibility to incorporate business knowledge in order to guide the clustering algorithm. A formalization of the fact that an intuitive a priori prioritization of the variables might exist, is presented in this paper and applied in a direct marketing context using recent data. By providing the analyst with a new approach offering different clustering perspectives, this paper proposes a straightforward way to apply constrained clustering with soft attribute-level constraints based on feature order preferences.     

Web病毒式营销核心群体挖掘与推荐策略

Web病毒式营销已经成为电子商务领域中的重要营销策略, 核心群体在其中发挥着重要的作用。为了挖掘核心群体并对其进行商品推荐, 在Web客户信任网络(customer trust network, CTN)的基础上考虑了信任度、评价分数以及推荐次数等因素定义了影响度的概念, 提出了以影响度为基础的节点网络影响集的构建方法以及基于网络影响集的核心群体挖掘算法MCGNIS（mining core group based on network-influence set）, 并以挖掘出的核心群体为对象建立了基于网络影响集的推荐模型RCGNIS(recommending model for core group based on network-influence set）, 设计了相应的推荐算法来计算商品对核心群体的可推荐度。实验证明, 以节点网络影响集为基础挖掘出的核心群体在Web客户信任网络中具有较高的网络覆盖率（network-coverage, NC）, 推荐模型RCGNIS具有很好的推荐准确性, 同时又保持了推荐的多样性。     

Entropy based evaluation of net structures – deployed in Social Network Analysis

The analysis of Social Networks widely is based on their respective graphical structures. Centrality measures of actors usually consider their position in a graph. Sometimes graphs are an insufficient medium to represent social structures. In this paper the authors propose a new framework with the purpose to analyze social fabric correctly: information theory. A set of conditionals forms the knowledge base about the network’s structure. Once the knowledge base is adapted under maximum entropy or minimum cross-entropy a new form of analysis is available. The construction of knowledge bases and their analyses are realized in the expert system shell SPIRIT.     

Measure oriented training: a targeted approach to imbalanced classification problems

Since the overall prediction error of a classifier on imbalanced problems can be potentially misleading and biased, alternative performance measures such as G-mean and F-measure have been widely adopted. Various techniques including sampling and cost sensitive learning are often employed to improve the performance of classifiers in such situations. However, the training process of classifiers is still largely driven by traditional error based objective functions. As a result, there is clearly a gap between themeasure according to which the classifier is evaluated and how the classifier is trained. This paper investigates the prospect of explicitly using the appropriate measure itself to search the hypothesis space to bridge this gap. In the case studies, a standard threelayer neural network is used as the classifier, which is evolved by genetic algorithms (GAs) with G-mean as the objective function. Experimental results on eight benchmark problems show that the proposed method can achieve consistently favorable outcomes in comparison with a commonly used sampling technique. The effectiveness of multi-objective optimization in handling imbalanced problems is also demonstrated.     

Identifying key players in large social networks by using a multi-objective artificial bee colony optimization approach

Identifying a set of individuals that have an influential relevance and act as key players is a matter of interest in many real world situations, especially in those related to the Internet. Although several approaches have been proposed in order to identify key players sets, they mainly focus just on the optimization of a single objective. This may lead to a poor performance since the sets identified are not usually able to perform well in real life applications where more objectives of interest are taken into account. Multi-objective optimization seems the natural extension for this task, but there is a lack of this type of methodologies in the scientific literature. An efficient Multi-Objective Artificial Bee Colony (MOABC) algorithm is proposed to address the key players identification problem and is applied in the context of six networks of different dimensions and characteristics. The proposed approach is able to best identify the key players than the ones previously proposed, especially in the context of large social networks. The model performance of the proposed approach has been evaluated according to different quality metrics. The results from the MOABC execution show important improvements with respect to the best multi-objective results in the scientific literature, specifically, in average, 13.20% of improvement in Hypervolume, 120.39% in Coverage Relation and 125.52% in number of non-dominated solutions. Even more, the proposed algorithm is also more robust when repeating executions.     

What makes good games go viral? The role of technology use,efficacy, emotion and enjoyment in players’ decision to share a prosocial digital game

Serious digital games may be an effective tool for prosocial message dissemination because they offer technology and experiences that encourage players to share them with others, and spread virally. But little is known about the factors that predict players’ willingness to share games with others in their social network. This panel study explores how several factors, including sharing technology use, emotional responses, and game enjoyment, contribute to players’ decision to share the game Darfur is Dying, with others. College students played the game and completed questionnaires that assessed whether they had shared the games at two different time points: during game play and after game play. Positive emotions predicted sharing while students played the game, but negative emotions predicted whether the game was shared after initial game play. Game enjoyment predicted players’ intentions to share the game, but it did not predict actual sharing behavior. Neither players’ general use of sharing technologies nor their satisfaction related to sharing digital content predicted sharing intentions or behavior. These findings have implications for the study of viral social marketing campaigns, and serious game design and theory.     

沈阳市奶业产销问题初探

随着我国经济不断向前发展和城乡人民生活水平的日益提高,人们的生活消费结构也将发生明显变化,其中食物消费中牛奶消费会呈现增长趋势.根据沈阳市近年来奶业发展现状,分析了奶业发展过程中存在的一些问题,并在产销方面有针对性地提出了促进沈阳市奶业发展的基本思路.     

Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign

Cross-selling campaigns seek to offer the right products to the set of customers with the goal of maximizing expected profit, while, at the same time, respecting the purchasing constraints set by investors. In this context, a bi-objective version of this NP-Hard problem is approached in this paper, aiming at maximizing both the promotion campaign total profit and the risk-adjusted return, which is estimated with the reward-to-variability ratio known as Sharpe ratio. Given the combinatorial nature of the problem and the large volume of data, heuristic methods are the most common used techniques. A Greedy Randomized Neighborhood Structure is also designed, including the characteristics of a neighborhood exploration strategy together with a Greedy Randomized Constructive technique, which is embedded in a multi-objective local search metaheuristic. The latter combines the power of neighborhood exploration by using a Pareto Local Search with Variable Neighborhood Search. Sets of non-dominated solutions obtained by the proposed method are described and analyzed for a number of problem instances.     

网络营销浅析

随着互联网的飞速发展,网络营销已经成为市场营销的重要组成部分,该文对网络营销的方式和特点逐一分析,探索其对营销内涵和方式的影响。     

一种基于资源分配策略的复杂网络中心性测度

针对标准中心性测度不适用于非联通网络这一问题,提出了基于资源分配策略的复杂网络中心性测度。节点的资源分配中心性测度定义为节点从其它节点接受的资源量,如果一个节点从其他节点接受的资源量越多,则该节点越重要。通过人工网络和现实网络实验表明,该度量不仅适用于联通网络,也适用于非联通网络,相较于标准测度,可以检测桥节点,而且具有良好的稳定性。     

A Web marketing system with automatic pricing

We propose a new scheme of ‘automatic pricing' for digital contents, and describe an implemented system as well as concrete pricing algorithms for it. Automatic pricing refers to a methodology of automatically setting sales prices to optimal prices, based on past prices and sales. In particular, we consider the case in which automatic pricing is done in order to maximize the profit of an on-line marketing site. We describe a demo site for on-line marketing with automatic pricing, which we call ‘digiprice'. We will also describe the concrete pricing algorithms we employ in digiprice, and report on preliminary performance evaluation experiments we conducted using simulated data. The results of experimentation verify that our methods are practical in terms of both the speed of convergence to the optimal price and computational efficiency.     

Marketing on the Internet — who can benefit from an online marketing approach?

The research builds upon the literature in electronic commerce and past research in marketing with the objective of understanding factors that impact a product's adaptability to online marketing. A review of marketing channel choice literature reveals a set of factors and channel choice functions that are considered important in making channel decisions. Using this as a basis, four major channel functions, namely, product customization, availability, logistics, and transaction complexity are considered relevant in understanding the implications for Internet marketing. By building upon previous research in the area of channel selection, we provide a means of classifying Internet marketing initiatives based on product characteristics. The classification scheme based on product characteristics can help analyze the significance of each factor on the success of a firm's online marketing approach. Further, the classification scheme is used to discuss decision support implications.