Knowledge that Transforms

“An Audience Flow Model of Television Viewing Choice” by Rust and Alpert (this issue, pp. 113–124) does indeed represent a contribution to the marketing literature. Furthermore, it represents an important step in the process of developing a better understanding of a significant and practical marketing issue: the selection—in advance—of specific advertising vehicles.

Response to the commentary Pringle, L. G. 1984. A Comment. Marketing Sci. 3 125–126 on author's paper Rust, R. T., M. I. Alpert. 1984. An audience flow model of television viewing choice. Marketing Sci. 3 113–124.

Comments and authors' rejoinder to Mahajan, V., E. Muller, S. Sharma. 1984. An empirical comparison of awareness forecasting models of new product introduction. Marketing Sci. 3 179–197.

A mixed binomial error model is proposed which permits the evaluation of validity and test-retest reliability when individuals are asked to pick K of N objects. The approach explicitly incorporates the effect of guessing as well as the respondent's true discrimination ability, and recognizes that that ability will vary across respondents. So, given the choices made by a set of individuals, we are interested in estimating the distribution of true ability over the population. To evaluate the criterion-related validity each person's K choices (of N objects) are compared to an objective standard—the K “correct” choices. For the primary model considered here these data can be summarized by the distribution, over individuals, of the number of “matches” or agreements between a person's choices and the objective criterion. A similar procedure is used for evaluating test-retest reliability. The distribution for the number of matches is again used; but here the matches will refer to agreement between the individual's K choices on this occasion, and the K choices made by that individual on a previous occasion. The use of these reliability and validity results to address specific marketing questions, such as the segmentability of a market or the effect of advertising on perception and attitudes, is also discussed.

New product planning models attempt to predict the market consequences of product line and product design decisions. One output of such models, especially those driven by subjective or market research data, is usually theoretical market shares based upon consumer preferences under idealized conditions. This paper describes a class of models that bridge the gap between such theoretical market shares and dynamic sales forecasts. This model accounts for differences in customer awareness of different products, for differences in product announcement dates, for differences in product availability, for differences in marketing efforts, for customer inertia and for customer purchasing delays. The paper describes specific details of such a model used in a system developed for market analysis of high speed nonimpact computer printers.

This paper indicates how an information theoretic approach via the MDI (minimum discrimination information) statistic can be used to help provide a uniform approach to both statistical testing and estimation in various kinds of marketing analyses. Extensions to constrained versions of the MDI statistic also make it possible to test the consistency of market information with management plans or policies that can be represented in “external” constraints, i.e., constraints formulated without reference to the data base, and to estimate their impact on the market. Composite hypotheses, which are difficult to deal with by the more customary methods used in market research, can be dealt with naturally and easily via these MDI approaches. Basically MDI is more efficient than classical approaches because distribution estimation and hyopthesis testing are done simultaneously and the resulting estimates obtain regardless of the conclusion of the test. Numerical illustrations are supplied and discussed. Recent developments in mathematical programming (duality) theory and methods, which are also pertinent, are briefly examined for their bearing on still further possibilities for constrained MDI modeling.

A model is proposed to estimate the impact of advertising copies on the relationship between the frequency of exposure to an advertisement and the criterion measures of attitude or behavioral intentions. The model is used in the context of copy testing. It is different from existing copy testing methods on several aspects, the most important of which are that various constructs known to be important are integrated, and the model offers diagnostic information as to areas where the copy can be improved.

Patterns of customer purchases are often modeled as discrete behavioral events following some probability law. These stochastic models have been applied separately to the phenomena of product class purchase incidence and brand choice (conditional on the products being purchased). Most of these models are probability mixture models where the parameters of an individual customer model vary across the population according to some distribution. This paper proposes a simple model of purchase behavior at the individual level, Poisson (λ) product purchases and Bernoulli (p) brand choices, so that λ and p characterise each customer. However, λ and p may vary jointly across the population in an arbitrary fashion. Therefore, purchase incidence and brand choice are treated together making no assumptions about the dependence between them. This generality is obtained at the cost of efficiency but enables us to focus on the individual behavior. Estimators (with known asymptotic properties) are developed for predicting future purchasing behavior of individuals conditional on their past purchasing behavior. These predictions of future purchases, at brand and product levels, are of interest to marketing managers. For example, what is the expected number of brand (or product) purchases in period 2 for the group of customers that made x brand and n product purchases in period 1? Apart from the basic insights provided by such predictions, they are particularly useful in evaluating the impact of marketing efforts. The model and the estimation results are applied to a frequently purchased product and compared with other stochastic approaches. While this paper focuses on buying behavior, the model is applicable to accidents, direct mail purchases and other random events.

In conjoint analysis, a consumer's utility function for a continuous attribute is usually estimated using a part worth function. However, one may also use continuous functions. The purpose of the paper is to investigate through simulation the combined influence of the number of degrees of freedom, the nature of the “true” utility function and the amount of error in the data on the selection of a utility function. The focus is on functions that are known or expected to be monotone within the range of attribute levels of interest. One can then choose among linear, quadratic and part worth functions. The results show (a) that estimation procedures with monotonicity constraints should be used, and (b) that it is best to use quadratic or part worth functions rather than linear functions to minimize potential losses in predictive validity.