The observation that many models are built but few are used has almost become a commonplace in the management science and operations research literature. Nevertheless, the statement remains to a large extent true today, also and perhaps even more so where marketing models are concerned. This led Philippe Naert, now about four years ago, to write a concept text of a few hundred pages on the subject of how to build imple men table marketing models, that is, models that can and will be used. One of the readers of that early manuscript was Peter Leefiang. He made suggestions leading to a more consistent ordering of the material and pro posed the addition of some topics and the expansion of others to make the book more self-contained. This resulted in a co-authorship and a revised version, which was written by Peter Leefiang and consisted of a reshuffling and an expansion of the original material by about fifty per cent. Several meetings between the co-authors produced further refinements in the text and the sequence of chapters and sections, after which Philippe Naert again totally reworked the whole text. This led to a new expansion, again by fifty per cent, of the second iteration. The third iteration also required the inclusion of a great deal of new literature indicating that the field is making fast progress and that implementation has become a major concern to marketing model builders.
This book is about marketing models and the process of model building. Our primary focus is on models that can be used by managers to support marketing decisions. It has long been known that simple models usually outperform judgments in predicting outcomes in a wide variety of contexts. For example, models of judgments tend to provide better forecasts of the outcomes than the judgments themselves (because the model eliminates the noise in judgments). And since judgments never fully reflect the complexities of the many forces that influence outcomes, it is easy to see why models of actual outcomes should be very attractive to (marketing) decision makers. Thus, appropriately constructed models can provide insights about structural relations between marketing variables. Since models explicate the relations, both the process of model building and the model that ultimately results can improve the quality of marketing decisions. Managers often use rules of thumb for decisions. For example, a brand manager will have defined a specific set of alternative brands as the competitive set within a product category. Usually this set is based on perceived similarities in brand characteristics, advertising messages, etc. If a new marketing initiative occurs for one of the other brands, the brand manager will have a strong inclination to react. The reaction is partly based on the manager's desire to maintain some competitive parity in the mar keting variables.
This book is about how models can be developed to represent demand and supply on markets, where the emphasis is on demand models. Its primary focus is on models that can be used by managers to support marketing decisions. Modeling Markets presents a comprehensive overview of the tools and methodologies that managers can use in decision making. It has long been known that even simple models outperform judgments in predicting outcomes in a wide variety of contexts. More complex models potentially provide insights about structural relations not available from casual observations. In this book, the authors present a wealth of insights developed at the forefront of the field, covering all key aspects of specification, estimation, validation and use of models. The most current insights and innovations in quantitative marketing are presented, including in-depth discussion of Bayesian estimation methods. Throughout the book, the authors provide examples and illustrations. This book will be of interest to researchers, analysts, managers and students who want to understand, develop or use models of marketing phenomena.
This book is about marketing models and the process of model building. Our primary focus is on models that can be used by managers to support marketing decisions. It has long been known that simple models usually outperform judgments in predicting outcomes in a wide variety of contexts. For example, models of judgments tend to provide better forecasts of the outcomes than the judgments themselves (because the model eliminates the noise in judgments). And since judgments never fully reflect the complexities of the many forces that influence outcomes, it is easy to see why models of actual outcomes should be very attractive to (marketing) decision makers. Thus, appropriately constructed models can provide insights about structural relations between marketing variables. Since models explicate the relations, both the process of model building and the model that ultimately results can improve the quality of marketing decisions. Managers often use rules of thumb for decisions. For example, a brand manager will have defined a specific set of alternative brands as the competitive set within a product category. Usually this set is based on perceived similarities in brand characteristics, advertising messages, etc. If a new marketing initiative occurs for one of the other brands, the brand manager will have a strong inclination to react. The reaction is partly based on the manager's desire to maintain some competitive parity in the mar keting variables.
The observation that many models are built but few are used has almost become a commonplace in the management science and operations research literature. Nevertheless, the statement remains to a large extent true today, also and perhaps even more so where marketing models are concerned. This led Philippe Naert, now about four years ago, to write a concept text of a few hundred pages on the subject of how to build imple men table marketing models, that is, models that can and will be used. One of the readers of that early manuscript was Peter Leefiang. He made suggestions leading to a more consistent ordering of the material and pro posed the addition of some topics and the expansion of others to make the book more self-contained. This resulted in a co-authorship and a revised version, which was written by Peter Leefiang and consisted of a reshuffling and an expansion of the original material by about fifty per cent. Several meetings between the co-authors produced further refinements in the text and the sequence of chapters and sections, after which Philippe Naert again totally reworked the whole text. This led to a new expansion, again by fifty per cent, of the second iteration. The third iteration also required the inclusion of a great deal of new literature indicating that the field is making fast progress and that implementation has become a major concern to marketing model builders.
This book is about how models can be developed to represent demand and supply on markets, where the emphasis is on demand models. Its primary focus is on models that can be used by managers to support marketing decisions. Modeling Markets presents a comprehensive overview of the tools and methodologies that managers can use in decision making. It has long been known that even simple models outperform judgments in predicting outcomes in a wide variety of contexts. More complex models potentially provide insights about structural relations not available from casual observations. In this book, the authors present a wealth of insights developed at the forefront of the field, covering all key aspects of specification, estimation, validation and use of models. The most current insights and innovations in quantitative marketing are presented, including in-depth discussion of Bayesian estimation methods. Throughout the book, the authors provide examples and illustrations. This book will be of interest to researchers, analysts, managers and students who want to understand, develop or use models of marketing phenomena.
This ground-breaking Research Handbook provides a state-of-the-art discussion of the international law of Indigenous rights and how it has developed in recent decades. Drawing from their extensive knowledge of the topic, leading scholars provide strong general coverage and highlight the challenges and cutting-edge issues arising in international Indigenous rights law.
Our newly digital world is generating an almost unimaginable amount of data about all of us. Such a vast amount of data is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organisations to leverage the information to create value. This book is a refreshingly practical, yet theoretically sound roadmap to leveraging big data and analytics. Creating Value with Big Data Analytics provides a nuanced view of big data development, arguing that big data in itself is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. By tying data and analytics to specific goals and processes for implementation, this is a much-needed book that will be essential reading for students and specialists of data analytics, marketing research, and customer relationship management.
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