Redesigning Product Lines with Conjoint Analysis: A Comment

In a recent article, Page and Rosenbaum [ Journal of Product Innovation Management 4; 120–137 (June 1987)] discuss the use of conjoint analysis at Sunbeam Corporation. Their article provides a nice illustration of a conjoint application to food processor appliance redesign decisions. In this note, Dick Wittink compares characteristics of Sunbeam's approach to both established and more recent results reported in the conjoint literature.     

Decomposing the sales promotion bump accounting for cross-category effects

Extant research on the decomposition of unit sales bumps due to price promotions considers these effects only within a single product category. This article introduces a framework that accommodates specific cross-category effects. Empirical results based on daily data measured at the item/SKU level show that the effects of promotions on sales in other categories are modest. Between-category complementary effects (20%) are, on average, substantially larger than between-category substitution effects (11%). Hence, a promotion of an item has an average net spin-off effect of (20 − 11 =) 9% of its own effect. The number of significant cross-category effects is low, which means that we expect that, most of the time, it is sufficient to look at within-category effects only. We also find within-category complementary effects, which implies that competitive items within the category may benefit from a promotion. We find small stockpiling effects (6%), modest cross-item effects (22%), and substantial category-expansion effects (72%). The cross-item effects are the result of cross-item substitution effects within the category (26%) and within-category complementary effects (4%). Approximately 15% (= 11% / 72%) of the category-expansion effect is due to between-category substitution effects of dependent categories.     

Combining self-explicated priors with conjoint data using Bayesian regression

A Bayesian regression procedure (RBAYES) is proposed for the optimal combination of self-explicated data (priors) and conjoint judgments. The procedure does not require the design matrix for the conjoint judgments to be of full rank. The Bayesian regression procedure is similar to weighted least square in that it uses an information ratio to weight the priors. We provide empirical comparisons for the proposed method against (1) a Stein-type estimator (SBAYES) using one data set and (2) OLS applied to the data from an adaptive conjoint analysis using a second data set. In the second application we also use an alternating least squares procedure by itself and in combination with Bayesian regression (RBAYES+) to accommodate scale incompatibility as well as heteroscedasticity. In both applications we obtain superior results for the Bayesian regression procedure.     

Range and Number-of-Levels Effects in Derived and Stated Measures of Attribute Importance

We study how the range of variation and the number of attribute levels affect five measures of attribute importance: full profile conjoint estimates, ranges in attribute level attractiveness ratings, regression coefficients, graded paired comparisons, and self-reported ratings. We find that all importance measures are affected by the range manipulation. The number of attribute levels affects only two measures. The results allow us to benchmark the magnitude of the number-of-levels effect against the range effect: conjoint importance estimates were approximately equally affected by a threefold increase in the range of attribute variation and by the insertion of two intermediate attribute levels. Our findings show that the number-of-levels effect is most likely due to respondents' tendencies to distribute their mental stimulus representations and their responses uniformly over the corresponding continua.     

The Customer Concept: The Basis for a New Marketing Paradigm

Recent developments in both marketing theory and marketing practice make it necessary to formulate a new marketing paradigm. This paradigm consists of three elements: (1) a concept, which is the core of the paradigm, (2) a set of activities, and (3) a domain. The customer concept is the new marketing concept. It is a management orientation which maintains that firms establish relationships with selected individual target customers with whom superior customer values are designed, offered, redefined and realized in close cooperation with other partners in the marketing system such as suppliers and intermediaries, in order to realize long-term profits through customer satisfaction, partner- and employee satisfaction. The new marketing activities include decisions with regard to the firm's stated vision, objective(s), strategy, organizational structure, culture, information system, marketing instruments, business processes and human resource management. The new marketing domain encompasses the broader interpretation of marketing as the central concept in the behavior of the firm. The customer concept implies a reorientation of marketing to one that places the customer in a pivotal role.     

Understanding Firm, Physician and Consumer Choice Behavior in the Pharmaceutical Industry

This paper argues that the pharmaceutical industry represents an exciting opportunity to carry out academic research. The nature of the industry allows researchers to answer new questions, develop new methodologies for answering these questions as well as to apply existing methodology to new data. The paper opens with some industry background, then provides a brief overview of some important research areas and discusses the open questions in each area. Issues of data type and availability are also discussed. This paper is based on a session (with the same title and participants) that was part of the Sixth Invitational Choice Symposium hosted by the University of Colorado and held at Estes Park, Colorado during June 4–8, 2004.     

Competitive reaction- and feedback effects based on VARX models of pooled store data

We apply a model that accommodates dynamic phenomena in demand- and reaction functions. The latter functions capture reactions to actions as well as to consequences of actions. We estimate a fixed effects VARX model with dynamic and interactive effects for multiple brands based on pooled time series and cross-sectional data for two product categories. The Impulse Response Analysis (IRA) results for one category (tuna) under different scenarios show that the inclusion/exclusion of competitive reaction- and feedback effects matters a lot, consistent with a high degree of competitive interaction in this market. We find that the role of cross-brand feedback effects is greater than the role of traditional competitive reaction effects. Intrafirm effects (internal decisions) also play an important role. In a decomposition study we show that the exclusion of these effects may either increase (up to 12%) or decrease (by as much as 50%) the net unit sales effect of a 20% price reduction. By contrast, in the second category (shampoo), where brands have distinct positions, the exclusion of these effects matters very little.