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ЭЛЕКТРОННАЯ БИБЛИОТЕКА ПО ТЕМЕ МАГИСТЕРСКОЙ ДИССЕРТАЦИИ:Экономико-математическое моделирование маркетинговых решений в управлении производственным процессомСтатья по теме: THE USE OF MODELS IN MARKETINGScott M.Smith and William R.Swinyard Introduction to marketing models,1999 Introduction The personal computer has affected the entire world of model building. Just a few years ago we were visiting with a mid-level marketing manager marketing models. He told us of his experience with marketing models, then added, "Models are used only by the megabuck consumer-products companies that can afford the high risk and costs of model development, data collection, and analysis." His was a widely-held view in those days before personal computers, and was supported in the history of mainframe-based marketing models. What are the reasons why? First, in some cases model development costs have been outrageous, with no payback whatever. In the early 1970's, LaButz of Canada spent over $50,000 to develop a model which would determine the effects of advertising and sales force expenses on market share, but it was never used. A few years earlier, Amstutz's Sprinter Mod II market simulation had cost over $200,000 to develop and it was never used. Today, many large corporations contract for large research projects that do not answer the right question, and are not even directed at the right problem. Second, the development time for a model was often too long for it to be useful. Many mainframe models have taken years of development to simulate market conditions which had changed long before the model was finished. Third, costs of collecting data to be used as input to a model have frequently been huge. In 1999, IBM will spend nearly $2,000,000 to model world wide sales potential on a country by country basis. And finally but probably most important, many of those mainframe models were almost incomprehensible to anyone but their developer. Amstutz's model was staggeringly complex, having more than 500 equations. Another well-publicized 1960's model was so large and complex that running it took more than $75,000 in computer time. The personal computer changed all this. It has encouraged the development of models that are smaller, cheaper to develop and cheaper to run. These models are more understandable, appeal to a larger audience and, if not always "friendly," are at least more companionable than the impersonal, and even intimidating, mainframe models of a decade ago. Model development software, such as Excel, is also becoming plentiful. These programs, or executable versions may ultimately standardize much model building. This standardization will help non-model-builders feel more comfortable interacting directly with the model themselves, instead of relying on their operations research people to do it for them. Undoubtedly, this interaction will increase management's comfort zone for models. This is likely to increase the use, confidence in, and understanding of the models. What is a Model? A model is simply a representation. A map, which represents the countryside, is a model, and so is a graph which represents a company's sales over time. In this sense, all our perceptions of reality are "models" since they are only perceptions and not reality itself. Our language is a model, since words are only representations of objects or ideas. Similarly, our numbering system is a model by which quantities of objects or ideas can be represented. These are trivial, but real, distinctions between models and reality. Models characterize either what currently exists in fact, or what might exist in the future. Marketing models might depict such operations as an existing product distribution system; a consumer's value structure, consumer preference modeling for product choices, or the effects of advertising on consumer awareness, knowledge, attitudes, or intention to purchase. The purpose of a model is typically to provide the manager with a guide for evaluating the effect of a set of input variables. For example, a design engineer for General Motors might model the aerodynamics of a different body design features to determine their impact on airflow and fuel economy. Or a civil or hydraulics engineer designing a dam to hold back a reservoir 200 feet deep might use a model to determine the concrete density and materials necessary to withstand the water pressure at each depth level. Model builders in the applied or physical sciences often use readily measurable and quantifiable input data. But marketing model builders typically often have only very loose and imprecise inputs. These inputs are often estimates of such unknowns as new product demand, advertising effectiveness, advertising efficiency, optimal sales force allocation, results of a product positioning option, or estimates of pricing strategy. Marketing Decision Making Modeling is simply a means of depicting, examining, specifying, or operationalizing relationships. This suggests, then, that management decision making is inherently a modeling process, in which the manager models the environment and competitive strategy. Companies, in their evolution toward becoming "model-building" firms, will usually include four processes or stages in marketing decision making. Most are stuck in the first or second stage. These processes are: 1. Making decisions through managerial experience, where the years of integrative executive experience guides managers to the proper course of action. 2. Using market data or other facts derived from marketing research along with the above, where marketing research may describe the attitudes, perceptions, preferences, product usage by market segments, or product use scenarios. Often data is collected to examine relationships, such as the relationship between use situation and usage rate, but the underlying assumptions of a consumer behavior model are not readily recognized. 3. Developing accepted organizational standards for decision making, where the organization explicitly recognizes acceptable alternatives in planning and implementing marketing strategy. 4. Conducting explicit model building, where model building objectives, constructs (variables), and relationships between constructs are specified in detail. The relationships between the constructs are specified in detail using theory and management judgement, and the evaluation of the model is based on a conceptual framework. An obvious question to be asked is, "Why don't more managers do explicit model building?" MIT professor John Little helped answer this by identifying several reasons why management science models are not used more often in marketing decision making. The reasons why models are not used in marketing include: 1. Parameterization of variables is difficult 2. Managers do not have time or desire to spend the effort to become knowledgeable in the area 3. Models are technologically and mathematically too complex 4. Managers do not have the input data for the models 5. Most models are not well defined To overcome these problems, Little gave thought to what characterizes "good" models -- models that will be used by management. These characteristics provide a litmus test for evaluating the "goodness", or managerial acceptability of all model building efforts within the social sciences. These are: 1. The model must be simple to use and understand, 2. It must be robust -- its results must not vary wildly with small changes to the input data, 3. It must be easy to communicate with, 4. It must be easy to control, 5. It must be adaptable to other products or situations, and 6. It must be complete on important details. It is easy to appreciate the complexity of the marketing decision making process when the difficulties of creating and parameterizing the models are understood. Total market effort is the mixture of activities the firm undertakes to meet its objectives (sales, profitability, donations, or whatever). Marketing activities include, of course, marketing mix variables such as the types and levels of pricing, promotional activities, distribution strategies, and product development strategies. Interactions are known to exist between these functions, but the duration, relevant range, and extent is unknown. In practice, the mathematical, and even directional, relationships between the marketing mix variables and market response are not usually known. Consider the "Be a Pepper" advertisements aired by Dr. Pepper. Their upbeat music and image resulted in high advertising awareness, but Dr. Pepper market share steadily declined. Although it is generally thought that the campaign was unsuccessful because it provided no product benefit focus, little is actually known as to why (or even "whether") the campaign was unsuccessful in selling the product. Contributing to this complexity are many other factors that can influence the implementation or interpretation of the marketing effort. Specifically, these variables may include: Market segments which differ in their response to the marketing mix. Segments may be defined according to geographic areas, attitudes held, benefits sought, characteristic life styles, demographic characteristics and so on. Multiple products which exist or are offered to the consumer. The allocation of resources, such as sales effort, to one product may influence sales of other products within the line. Conflicting objectives which present difficulties when setting goals and developing implementations for product offerings. Functional Areas Interaction. For example, inventory management objectives of limited stock may conflict with sales management objectives of no waiting time and abundant stock. Pricing and advertising strategies may influence inventory, employee scheduling, cash flows, production scheduling, and financial management. Competitive Effects. The nature and impact of competitive actions is typically unknown. Delayed Response To Marketing Efforts. The effects of advertising campaigns or selling efforts may carry over substantial periods of time and even last for years. In spite of the complexities of model building, models offer significant benefits. Perhaps most important is the sensitization process they help develop. Managers must gather and enter the input data, then evaluate the model's outputs. They re-evaluate the input data, modify it, and watch the effect on the model's outputs. By using models in this way, managers become sensitized to recognize and evaluate the elements that are important to making an appropriate decision. Modeling activities also force both the manager and the researcher to be critical (and parsimonious) in evaluating the impact of variables that could explain the process. They begin to question assumptions about supposedly influential variables, and begin to discover important new variables. Finally, the manager is forced, as part of the variable selection process, to consider the relationships between variables. He or she begins to be aware of interactions between them, and they may begin to recognize that a symbiotic, synergistic, relationship exists between many marketing activities and effects. AN INTRODUCTION TO MODELING CONCEPTS Model Purpose and Form Models are usually developed to increase our understanding, prediction, and control of real world events. They can further be described as being either descriptive, predictive, or normative. This distinction between model types is important since it delineates the purpose or use of the model in management decision making. Descriptive Models. Some models are intended merely to describe a real-world process. These descriptive models provide a characterization of the nature and workings of the modeled process. For example, a simple accounting model of, Profit = Revenue - Costs, describes a large number of events, including sales, purchases of materials, expenditures for labor and overhead, and so on. In marketing, descriptive models show market information in a way that identifies the components of a market or system. In consumer research, this system might be the consumer information search process that occurs within the overall process of consumer behavior. Descriptive models make statements that certain phenomena are produced by other factors. For example, we might predict sales with the factors, Factor 1 = size of the customer's business Descriptive models are often used to depict large systems, because the large number of variables and interactions make other types of models impractical. The Engel, Blackwell, and Kollat model provides one example (Figure 1-1). In addition to providing graphic detail, descriptive models also serve to develop descriptive structural hypothesis that specify the causal relationships between variables. This specification may provide for better measurement of variable relationships. Descriptive models provide a structured basis for discussion, analysis and understanding of a problem. Predictive Models. Predictive models are usually more complex than descriptive ones. Besides describing objectives and events, they are designed to predict future events. A sales forecasting model, for example, is designed to predict the result of the purchase decisions by a firm's customers. As another example, we might develop a time-series regression model to predict the impact of advertising reach and frequency on advertising effectiveness. Predictive models make inferences about the underlying structure of variables that govern a phenomenon. Understanding the predictive relationships between sets of variables is beneficial because these relationships can be used to forecast future events, making them invaluable in planning and control. Predictive models can be useful in validating descriptive models and in determining sensitivity of predictions to variables in the model. Extending our sales example from above, predictive models provide a more precise explanation by specifying how factors 1...k interact in manner x1...xk to predict sales. Normative Models. Normative (or, control) models are the most difficult models to construct since these models not only describe and predict, but provide direction about the proper course of action. If our sales forecasting model includes the prices we can charge for our products, we might be able to make a decision about which price we should charge in the future. The normative models tell us what should be done. They assess the implications of decisions and provide solutions to problems. Once again using our sales example, normative models extend to provide the reasons for the predictive relationship, and may be expressed as: "Factors 1...k interact in manner x1...xk for reasons w1...wk to predict sales." Iconic and Symbolic Models. Management science models may assume a variety of forms, as shown by Zaltman, Pinson and Anglemar (1970), who identify two basic types of models: iconic and symbolic. Iconic (which means "image") models are like reality in the sense that they look like reality. Photographs, maps, architectural miniatures, and rough layouts of advertisements are all iconic models. In contrast to iconic models, symbolic models do not look like reality, but emulate reality in other ways. They include either (a) verbal, (b) schematic, or (c) mathematical forms that describe a specific process. For example, consider several simple models of these types which describe the relationship between consumer attitudes and intention to purchase a brand: Verbal: To discover consumers' purchase intention for a brand, measure their evaluation of each of its attributes and then add these evaluations together. Schematic: Purchase Intention = Evaluation of Attitude 1 + Evaluation of Attitude 2 + Evaluation of Attitude 3 Mathematical: PI = b0 + b1A1 + b2A2 + b3A3 where, b0, b1, b2 and b3 are importance weights, and A1, A2 and A3 are measured attitudes evaluating brand attributes 1, 2 and 3, and PI is purchase intention. 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