D. B. Learner

Information theory as a unifying statistical approach for use in marketing research

Abstract Information theory is shown to provide a unified approach to a wide range of problems in marketing research. For instance, this approach can be used to obtain characterizations parallel to those of the Hendry system and other entropic approaches with great economy of assumptions and with the added flexibility that constraints can be easily identified for explicit consideration and implemented as needed. Goodness-of-fit tests and decision modelling structures are supplied from these same stochastic models with a range of applications that include market segmentation and brand shifting choices. A basic approach to these and other procedures is therefore obtainable from information theoretic methods. These methods can be used to address stochastic model selection problems and other probabilistic models of marketing choice — for example, Minimum Discrimination Information (MDI) estimation, Logit, Multiplicative Competitive Interaction (MCI), and other important choice models are also shown in this paper to arise naturally from information theoretic formulations with duality relations developed by Charnes and Cooper providing additional simplifications and interpretations.

A Multiperiod Analysis of Market Segments and Brand Efficiency in the Competitive Carbonated Beverage Industry

Measurement and evaluation of sales response, in a multiattribute sense, for a product in the usual marketing environment of competing brands has been and continues to be an exceedingly complex and difficult task. It is made more so by the inability to obtain either comprehensive data or sample data that are free from noise factors, not all of which are recognized a priori or a posteriori. For example, even a casual review of the marketing literature would lead one to conclude that even in a heavily researched area such as the advertising—sales response curve there is little conclusive evidence as to the shape of these curves, and that all such investigations are limited by vitrue of ignoring interactions of marketing mix variables. These studies also, of course, treat only one response variable at a time.

Managing service productivity: The data envelopment analysis perspective

Abstract This paper outlines a new productivity assessment based on Data Envelopment Analysis (DEA) methodology. DEA offers the first satisfactory multi-input, multi-output measure of productivity, and allows for productivity management at the intrafirm and interfirm levels with particular application in marketing. Efficiency and effectiveness are distinguished as productivity components, and means for managing each one is discussed and illustrated by an example from a marketing perspective.

Method and progress in management science

Abstract A model of the management sciences is presented, detailing the roles of theory, methodology, data, and problems in scientific advance. The purpose of the model is to describe the scientific process and clarify its terminology, so that research may be supported and performed in a way that fosters more rapid advances in worthwhile directions. A framework of “problem-driven research” is offered as a preferred alternative to theory-driven research as a basis for progress in the management sciences. The presentation draws on the insights of other writers, and uses a number of examples from the history of technology and management to illustrate and support the model.

Contributions to Marketing

Marketing is a field involving many dimensions. Activities range from marketing one-of-a-kind industrial products to marketing consumer goods that are repetitively purchased in high volumes. Here we will deal only with the latter case, even though doing so passes over many of Abraham Charnes’s contributions, in order to focus on his contributions to 1) advertising and media selection, 2) new product introductions and analyses, 3) brand switching and market-structure analyses, and 4) new ways of modeling and measuring relative efficiency and effectiveness in marketing efforts. We will show how the mathematical modeling Dr. Charnes brought to specific tasks not only addressed these problems with creative new approaches but also structured previously unstructured collections of marketing activities. We will also examine how modeling and structuring methods like these can continue to produce benefits for marketing practice and can uncover new problems and possibilities for scientific research.