There is an evident difference between the kind of work marketing practitioner involved in everyday managerial decision making does and the work a marketing scientist is involved in. Whereas the first one is concerned with the optimization of a set of variables that may maximize a performance measure, the second attempts to generate scientific knowledge in marketing related issues.
Most of the definitions of Science differ in depth or scope and none can be considered more valid than the other. Anyhow, all the definitions have some factors in common which can be summarized in the following: Science is concerned with a connected body of demonstrated truths or with observed facts. These truths and facts must be classified under general laws and principles that can lead to the discovery of new truths and principles within its domain through the experiment and observation.
Marketing Science involves building a body of knowledge that integrates relationships, principles and generalizations within the domain of marketing.
Using the scientific method, Marketing Science aims at extending the boundaries of knowledge in such a way that it produces agreement among a set of independent observers.
The methodological approach to Marketing Science is very diverse, involves different skills and draws from many other disciplines (statistics, economics, psychology, etc). This diversity can be illustrated by the following examples: ÃÂ· The construction of a theoretical model in which a Marketing Scientist proposes a set of assumptions, some of them mathematical and others empirically verifiable, in order to derive their logical implications. If these implications can be corroborated by observed phenomena, cause-effect relationships can be inferred.
ÃÂ· The description of phenomena (consumer behavior, for instance) by the analysis of aggregate patterns and trends that can be found to generalize across different contextual settings.
ÃÂ· A qualitative model aiming at explaining the cognitive and emotional attitudes of a customer.
ÃÂ· A quantitative empirical model that attempts to understand forecast the behavior of players in a particular type of market.
All these examples attempt to find general principles and patterns from particular phenomena. The process by which these general statements generated involves disentangling complex systems into parts and retaining the elements in common across the different contextual environments. The more general a statement the greater the scope and thus the more appealing to the scientist. of some generalizable elements from the particular contextual environment. The world, broadly speaking, is constituted by particularities that a scientist relates together.
. For that reason, it is not always possible (or at least easy) to apply general scientific findings to particular concrete situations.
How are these general statements applied to specific situation? There is not a straightforward answer to that question, especially in the social sciences. In particular, marketing practitioners are not concerned with generalities. They face particular problematic situations that require solutions specifically tailored for that particular firm, business unit, geographic region and period of time. Even though Marketing Science findings can be found useful in some situations, this is not always true. The complexity of marketing systems makes it difficult to bridge the gap between scientific generalizations and context-specific particular problems.
Managers usually have to look at the system from an engineering point of view. In other words, they need to find an optimum solution for a design problem. This design can take many forms but in general terms can be thought of as the set of decisions necessary to achieve a strategic objective. In the case of the marketing manager, the set of decisions may be for instance the marketing mix (pricing, distribution, advertising, etc.) and the objective may be an increase in sales, brand awareness or other performance measure. The inner environment of the design problem can be represented as the set of possible alternatives for action (Ai), whereas the outer environment is represented by a set of parameters (Sj), which may be known with certainty or only in terms of a probability distribution. The design problem involves the maximization of a performance measure that can be considered a function of those two sets: In this way, marketing decisions are taken so that U is maximized.
What is then the link between these two activities? The marketing engineering may rely on scientific findings in both, the definition and the optimization of the design process. For example, a study of the influence of the marketing mix on the adoption a new technology can help in the design of a new product launch strategy. On the other hand, marketing science is nourished by the problems that marketing engineering face since they are the particularities that are collected into purified generalizations. There is also a gray area in marketing academia that is equidistant from science and engineering. Many published studies in marketing journals are truly engineering problems. What distinguishes these studies from particular managerial problems is that they are evaluated more rigorously and with some generality.
Marketing Science and Marketing Engineering are two different areas that look at the same universe of marketing systems. Whereas the former is concerned with identifying the trends and principles that are common to several of these systems, the latter attempts to optimize particular issues in each specific context.