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Contributing Authors Include Robert S. Weinberg, Paul Stillson, E. Leonard Arnoff, And Many Others.
Mathematical models can be classified in a number of ways, e.g., static and dynamic; deterministic and stochastic; linear and nonlinear; individual and aggregate; descriptive, predictive, and normative; according to the mathematical technique applied or according to the problem area in which they are used. In marketing, the level of sophistication of the mathe matical models varies considerably, so that a nurnber of models will be meaningful to a marketing specialist without an extensive mathematical background. To make it easier for the nontechnical user we have chosen to classify the models included in this collection according to the major marketing problem areas in which they are applied. Since the emphasis lies on mathematical models, we shall not as a rule present statistical models, flow chart models, computer models, or the empirical testing aspects of these theories. We have also excluded competitive bidding, inventory and transportation models since these areas do not form the core of ·the marketing field.
Marketing models is a core component of the marketing discipline. The recent developments in marketing models have been incredibly fast with information technology (e.g., the Internet), online marketing (e-commerce) and customer relationship management (CRM) creating radical changes in the way companies interact with their customers. This has created completely new breeds of marketing models, but major progress has also taken place in existing types of marketing models. The HANDBOOK OF MARKETING DECISION MODELS presents the state of the art in marketing decision models, dealing with new modeling areas such as customer relationship management, customer value and online marketing, but also describes recent developments in other areas. In the category of marketing mix models, the latest models for advertising, sales promotions, sales management, and competition are dealt with. New developments are presented in consumer decision models, models for return on marketing, marketing management support systems, and in special techniques such as time series and neural nets. Not only are the most recent models discussed, but the book also pays attention to the implementation of marketing models in companies and to applications in specific industries.
Mathematical Models of Distribution Channels identifies eight "Channel Myths" that characterize almost all analytical research on distribution channels. The authors prove that models that incorporate one or more Channel Myths generate distorted conclusions; they also develop a methodology that will enable researchers to avoid falling under the influence of any Channel Myth.
In the introduction to his book Dr. Harder has very clearly described its purpose and organization. I only want to add for the English-speaking reader a few words on the place the present text is likely to have in the cur rent literature. At first Dr. Harder's undertaking might come as a surprise. Only a few years ago, Zeisel's Say it with Figures gave the market research practi tioner some ideas of how simple figures and tables could be successfully employed; Langhoff's publication for the American Marketing Associa tion presented some pertinent mathematical models in the most elemen tary form; why should a German author believe he can already introduce us to serious mathematical procedures for use in product management and advertising? After reading the book, incredulity turns into pleasure because of the skill with which the author has pursued his task. As a matter of fact, the book can serve two audiences who at first glance might appear to have quite opposing interests. For the mathematically trained market re searcher, the book has the marked advantage of combining a variety of ap proaches not ordinarily mixed in one volume. If the market researcher be gan as an economist he is already familiar with difference equations and time series analysis; if he moved in from psychology, he is already ac quainted with factor analysis. But as he reads this book, he finds the two worlds well integrated.
The field of marketing and management has undergone immense changes over the past decade. These dynamic changes are driving an increasing need for data analysis using quantitative modelling. Problem solving using the quantitative approach and other models has always been a hot topic in the fields of marketing and management. Quantitative modelling seems admirably suited to help managers in their strategic decision making on operations management issues. In social sciences, quantitative research refers to the systematic empirical investigation of social phenomena via statistical, mathematical or computational techniques.The first edition of 'Quantitative Modelling in Marketing and Management' focused on the description and applications of many quantitative modelling approaches applied to marketing and management. The topics ranged from fuzzy logic and logical discriminant models to growth models and k-clique models.The second edition follows the thread of the first one by covering a myriad of techniques and applications in the areas of statistical, computer, mathematical as well as other novel nomothetic methods. It greatly reinforces the areas of computer, mathematical and other modeling tools that are designed to bring a level of awareness and knowledge among academics and researchers in marketing and management, so that there is an increase in the application of these new approaches that will be embedded in future scholarly output.
Mathematical Models of Distribution Channels identifies eight "Channel Myths" that characterize almost all analytical research on distribution channels. The authors prove that models that incorporate one or more Channel Myths generate distorted conclusions; they also develop a methodology that will enable researchers to avoid falling under the influence of any Channel Myth.
From the perspective of partial differential equations (PDE), this book introduces the Black-Scholes-Merton's option pricing theory. A unified approach is used to model various types of option pricing as PDE problems, to derive pricing formulas as their solutions, and to design efficient algorithms from the numerical calculation of PDEs.