The method being introduced here was developed by Infratest, Munich. The first section is devoted to the requisites of applications with which marketing models have to comply if they are to meet the requirements of the user. The second and third sections deal with the development of the model approach. The model is strictly speaking a dynamic and open one. Both environmental influences and the competitive conditions are simultaneously taken into account. The market is essentially described by three moduli of elasticity. Whereas the alpha values describe the competitive conditions, the beta values measure the success of the marketing strategy. Finally, the factor of inertia describes the sensitivity of the market to marketing within a lapse of time. The fourth section is devoted to a case study taken from the consumer market. In addition to a brief interpretation of the results, forecasts and possibilities for simulation are presented with the aid of the model. The reliability of the model parameter was tested here. Moreover, the findings were not computed for all of the eight periods investigated, but only for a shorter period of time. The marketing criterion of the periods not included was then "forecast" on the basis of the Model 369 parameter. The values forecast could now be compared with the values actually measured. When the Model 369 runs, including six periods, and the two periods were forecast, the result was throughout one of a high degree of reliability. On the basis of the forecasted values, it was also possible to compute various simulations for different marketing strategies and to examine their effects on the marketing success of all products.
The method being introduced here was developed by Infratest, Munich. The first section is devoted to the requisites of applications with which marketing models have to comply if they are to meet the requirements of the user. The second and third sections deal with the development of the model approach. The model is strictly speaking a dynamic and open one. Both environmental influences and the competitive conditions are simultaneously taken into account. The market is essentially described by three moduli of elasticity. Whereas the alpha values describe the competitive conditions, the beta values measure the success of the marketing strategy. Finally, the factor of inertia describes the sensitivity of the market to marketing within a lapse of time. The fourth section is devoted to a case study taken from the consumer market. In addition to a brief interpretation of the results, forecasts and possibilities for simulation are presented with the aid of the model. The reliability of the model parameter was tested here. Moreover, the findings were not computed for all of the eight periods investigated, but only for a shorter period of time. The marketing criterion of the periods not included was then "forecast" on the basis of the Model 369 parameter. The values forecast could now be compared with the values actually measured. When the Model 369 runs, including six periods, and the two periods were forecast, the result was throughout one of a high degree of reliability. On the basis of the forecasted values, it was also possible to compute various simulations for different marketing strategies and to examine their effects on the marketing success of all products.
The distinction is often made at firm level between strategic decisions implying productive or commercial investment on a large scale and based on medium or long-term forecasts (3, 5 or 10 years), and tactical decisions relating to day-to-day management which refer to short-term forecasts (1 to 18 months). The reference data available, overall demand expressed in time series or more detailed information in cross-section, and the nature of the products, new or not new, have led to the parallel development of two separate forecasting techniques: econometric models and market segmentation models respectively. The paper gives a rapid description of the way in which these techniques have evolved over the last fifteen years and of the current trend, placing special emphasis on the advisability of integrating forecasting models with medium-term planning so as to produce veritable simulation models covering the possible growth prospects of the firm.
The distinction is often made at firm level between strategic decisions implying productive or commercial investment on a large scale and based on medium or long-term forecasts (3, 5 or 10 years), and tactical decisions relating to day-to-day management which refer to short-term forecasts (1 to 18 months). The reference data available, overall demand expressed in time series or more detailed information in cross-section, and the nature of the products, new or not new, have led to the parallel development of two separate forecasting techniques: econometric models and market segmentation models respectively. The paper gives a rapid description of the way in which these techniques have evolved over the last fifteen years and of the current trend, placing special emphasis on the advisability of integrating forecasting models with medium-term planning so as to produce veritable simulation models covering the possible growth prospects of the firm.
At a time where Market Testing is criticized for its costs and mathematical modelling is developed, sometimes imprudently, one has not to forget about the potential of Physical Micro Modelling, as well as Physical Simulation. Reason why we have pushed a raid beyond the previous developments, in order to master promotional activities which were up to now quite empirical, even anarchical.
Our objective is to expose and evaluate a particular class of simulation models, on which we have been working for some years and with which we have arrived at operational applications. To keep within the conceptual framework we have drawn, we shall define this class of models by the criteria: - micro-analytic; - behavioural. The objective of these models being to forecast the effects of marketing strategies elaborated by the user, these models are predictive and not normative. Then we shall try to evaluate the specific contribution of the micro-analytic and behavioural models to the practice of marketing.
Our objective is to expose and evaluate a particular class of simulation models, on which we have been working for some years and with which we have arrived at operational applications. To keep within the conceptual framework we have drawn, we shall define this class of models by the criteria: - micro-analytic; - behavioural. The objective of these models being to forecast the effects of marketing strategies elaborated by the user, these models are predictive and not normative. Then we shall try to evaluate the specific contribution of the micro-analytic and behavioural models to the practice of marketing.
Predicting consumer behavior with attitude measures has not always been successful. It is proposed to deal directly with the attitude components. In this article one such approach is discussed. Consumer decisions are studied in simulated choice experiments. It is hypothesized that prediction of choice should be based upon the cognitive elements which are aroused in the choice process, rather than upon single preference or attitude ratings. To illustrate that such predictions can be made, measurements are made, prior to the choices, of the subjective importance of the aroused values, and of the perception of the alternatives to these values. From these scores the attractiveness of alternatives is computed using a multidimensional subjective expected utility-type of model. In the experiments, different choices are studied and predictions based upon different kinds of values are compared. In all the choices highly significant predictions can be made, and the findings suggest that for this a relatively limited number of values are sufficient, so that the procedure is applicable in most consumer choice situations. Major problems with the model relate to the identification of relevant values which are not interdependent, and to the rating techniques applied.
In recent years there has been growing interest among marketing management in the possibilities of using quantitative techniques as aids to decision making. This interest is reaching epidemic proportions in many industries not least the pharmaceutical industry, and considerable confusion about the roles of these models has been generated. My paper describes a quantitative model of a pharmaceutical market and will, I hope, shed some light on the pitfalls of using such techniques as well as the very real benefits which can be derived.