A micro-behavioral marketing model provides the conceptual framework to explain free choice decision making among consumers who purchase our products or services. Since it is a micro model, it portrays the decision making of each individual respondent, one at a time. The underlying structure of the model is a highly simplified representation of the extremely complicated cognitive processes which actually take place when a consumer decides to choose or not choose a particular product or service. The simplifying assumption is that we are "creatures of satisfaction." We tend to make decisions, within acceptable economic and social constraints, which favour the things we like the most and derive the most satisfaction from possessing.
The paper argues the flexibility and versatility of this approach to modelling in terms of scale, scope, type of marketing problem and type of product field. Six case histories are cited, ranging from a simple use of Trade Off as an input to a quantitative exploratory project, to a comprehensive project incorporating a full repertoire of modelling technology. Emphasis is placed upon the cost effectiveness of micro- behavioural modelling, in that, irrespective of the size or complexity of the project in question, maximum use is made of the data collected, which can be re-worked time and again on computer simulation runs.
Philips pursue a policy of systematically evaluating new methodology for improving the Company's marketing decisions. Marketing models of several types have been developed, especially using regression techniques on time series data. However, ranch methods are limited by the availability of data and do not always give marketing management sufficient flexibility to test out the consequences of alternative marketing strategies. The development of methodology for the micro-behavioural, simulation of consumer choice-making seemed to provide the potential for improving the Company's ability to understand its marketing effort.
In this paper the authors propose as framework, or spine, a modular behavioural model structure that imbeds the various sequential development tests, sets the guidelines for data collection and processing and provides the logic for various evaluative models designed for the specific elements under development. After exposing the principles of this framework, the authors describe its practical application through a case study before drawing general conclusions.
This paper describes the background of a longitudinal study aimed at the development of a predictive model for the individual household's purchase behaviour concerning specific durable goods. Firstly, the setting of the problem is explained. Next, methodological, empirical, and theoretical contributions from a variety of studies which range from economic analyses of aggregated market demand to psychological studies of attitude-behaviour relationships, are discussed and are integrated into a conceptual model in which the household's purchase is a function of the household's situation, the household's anticipations, and changes in either of these two groups of variables. Then, a review of the selected variables is presented. Lastly, some results of preliminary surveys concerning the choice of a representative of the household to be interviewed, the choice of an interview-technique, and the response pattern through time of repeatedly interviewed panel members, are shown.
In this paper, traditional methods used by market researchers to predict consumer behaviour are criticised and the argument put forward that a formal modelling approach is the only way that reliable predictions can be made. Such an approach may involve several types and categories of models, and a typology of models arising out of discussion in the British Market Research Society Study Group on Modelling is described. It is argued that models most likely to be of value in consumer behaviour prediction problems will be Information Processing models and be concerned with the decision making process. They will be microanalytical behaviour models and work via a disaggregated data base rather than 'average' consumers. With the application of the technique of simulation it is argued that they represent a powerful research tool that can solve many of the problems market researchers are currently grappling with unsuccessfully, using traditional methods.