Online social blogs have gained popularity recently. They provide an effective channel for word-of-mouth (WoM) marketing to promote products or service. In WoM marketing, an opinion leader, who is normally more interconnected and has a higher social standing, can deliver product information, provide recommendations, give personal comments, and supplement professional knowledge that help companies to promote their products. Many theories have been put forward about social networks, but few address the issue of opinion leader identification. This study proposes a framework to identify opinion leaders using the information retrieved from blog content, authors, readers, and their relationships, which we call BARR for short. We first build ontology for a marketing product and then collect parameters from BARR to identify ''hot topics'' related to the product. These hot topics are then associated with information disseminators, or opinion leaders. Marketers can use BARR to track blogs written by opinion leaders and identify their opinions to form effective marketing strategies.

Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs

Telecommunications demand forecasting—a review

Agent‐based modelling is a bottom‐up approach to understanding systems which provides a powerful tool for analysing complex, non‐linear markets. The method involves creating artificial agents designed to mimic the attributes and behaviours of their real‐world counterparts. The system’s macro‐observable properties emerge as a consequence of these attributes and behaviours and the interactions between them. The simulation output may be potentially used for explanatory, exploratory and predictive purposes. The aim of this paper is to introduce the reader to some of the basic concepts and methods behind agent‐based modelling and to present some recent business applications of these tools, including work in the telecoms and media markets.

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Agent‐based modelling of customer behaviour in the telecoms and media markets

The main objective of this paper is to study existing methodological frameworks on group modelling projects using system dynamics. Such projects are more and more applied in organizations in order to support their strategic decisions. In this research, key frameworks were first identified and then classified allowing for an in‑depth analysis. The results of this analysis indicate that existing frameworks proposing a global vision of projects are scarce. Moreover, few of them consider both aspects of structure and process simultaneously. In addition, three crucial issues are highlighted: the elicitation of participants knowledge, the establishment of a consensus among participants, and the aspects of facilitation.

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Group Model Building Using System Dynamics: An Analysis of Methodological Frameworks

Consumer markets have been studied in great depth, and many techniques have been used to represent them. These have included regression-based models, logit models, and theoretical market-level models, such as the NBD-Dirichlet approach. Although many important contributions and insights have resulted from studies that relied on these models, there is still a need for a model that could more holistically represent the interdependencies of the decisions made by consumers, retailers, and manufacturers. When the need is for a model that could be used repeatedly over time to support decisions in an industrial setting, it is particularly critical. Although some existing methods can, in principle, represent such complex interdependencies, their capabilities might be outstripped if they had to be used for industrial applications, because of the details this type of modeling requires. However, a complementary method—agent-based modeling—shows promise for addressing these issues. Agent-based models use business-driven rules for individuals (e.g., individual consumer rules for buying items, individual retailer rules for stocking items, or individual firm rules for advertizing items) to determine holistic, system-level outcomes (e.g., to determine if brand X's market share is increasing). We applied agent-based modeling to develop a multi-scale consumer market model. We then conducted calibration, verification, and validation tests of this model. The model was successfully applied by Procter & Gamble to several challenging business problems. In these situations, it directly influenced managerial decision making and produced substantial cost savings. © 2010 Wiley Periodicals, Inc. Complexity, 2010

Multiscale agent-based consumer market modeling

A number of simulation techniques may be applied to strategy and decision-making. In using such techniques it is important to understand the role of models, in the context of the wider decision-making process which is largely a communal and political process; the choice of a particular approach or technique depends not only on the type of decision being considered, but also on the stage of the decision-making process (which in turn affects the type of problem under consideration).

Models which take into account the dynamics of a system can give insight into possible outcomes and indications of unintended consequences. Such models may not necessarily reproduce all aspects of a complex adaptive system; there is a continuum of modelling techniques, ranging from static equilibrium models through to agent-based approaches which can incorporate evolution and learning. The trade-offs between alternative approaches is discussed.

Complex Systems Models for Strategic Decision Making

Understanding the rate of adoption of a telecommunications service in a population of customers is of prime importance to ensure that appropriate network capacity is provided to maintain quality of service. This problem goes beyond assessing the demand for a product based on usage and requires an understanding of how consumers learn about a service and evaluate its worth. Field studies have shown that word of mouth recommendations and knowledge of a service have a significant impact on adoption rates. Adopters of Internet can be influenced through communications at work or children learning at school. In this paper we present an agent based model of a population of customers, with rules based on field data, which is being used to understand how services are adopted. Of particular interest is how customers interact to learn about the service through their communications with other customers. We will show how the different structure, dynamics, and distribution of the social networks affect the diffusion of a service through a customer population. Our model shows that real world adoption rates are a combination of these mechanisms which interact in a non-linear and complex manner. This complex systems approach provides a useful way to decompose these interactions. The paper concludes by suggesting improvements to the model and directions for future work.

Agent Based Customer Modelling

Understanding the rate of adoption of a telecommunications service in a population of customers is of prime importance to ensure that appropriate network capacity is provided to maintain quality of service. This problem goes beyond assessing the demand for a product based on usage and requires an understanding of how consumers learn about a service and evaluate its worth. Field studies have shown that word of mouth recommendations and knowledge of a service have a significant impact on adoption rates. Adopters of the Internet can be influenced through communications at work or children learning at school. The authors present an agent based model of a population of customers, with rules based on field data, which is being used to understand how services are adopted. Of particular interest is how customers interact to learn about the service through their communications with other customers. We show how the different structure, dynamics and distribution of the social networks affect the diffusion of a service through a customer population. Our model shows that real world adoption rates are a combination of these mechanisms which interact in a non-linear and complex manner. This complex systems approach provides a useful way to decompose these interactions.

M. H. Lyons

Papers

Complex Systems Models for Strategic Decision Making

Agent Based Customer Modelling

Agent Based Customer Modelling

Agent based customer modelling: individuals who learn from their environment