Knowledge that Transforms

Based upon a recently developed multiattribute generalization of prospect theory's value function (Tversky and Kahneman 1991), we argue that consumer choice is influenced by the position of brands relative to multiattribute reference points, and that consumers weigh losses from a reference point more than equivalent sized gains (loss aversion). We sketch implications of this model for understanding brand choice. We develop a multinomial logit formulation of a reference-dependent choice model, calibrating it using scanner data. In addition to providing better fit in both estimation and forecast periods than a standard multinomial logit model, the model's coefficients demonstrate significant loss aversion, as hypothesized. We also discuss the implications of a reference-dependent view of consumer choice for modeling brand choice, demonstrate that loss aversion can account for asymmetric responses to changes in product characteristics, and examine other implications for competitive strategy.

In recent years, many U.S. and Japanese firms have adopted Quality Function Deployment (QFD). QFD is a total-quality-management process in which the “voice of the customer” is deployed throughout the R&D, engineering, and manufacturing stages of product development. For example, in the first “house” of QFD, customer needs are linked to design attributes thus encouraging the joint consideration of marketing issues and engineering issues. This paper focuses on the “Voice-of-the-Customer” component of QFD, that is, the tasks of identifying customer needs, structuring customer needs, and providing priorities for customer needs. In the identification stage, we address the questions of (1) how many customers need be interviewed, (2) how many analysts need to read the transcripts, (3) how many customer needs do we miss, and (4) are focus groups or one-on-one interviews superior? In the structuring stage the customer needs are arrayed into a hierarchy of primary, secondary, and tertiary needs. We compare group consensus (affinity) charts, a technique which accounts for most industry applications, with a technique based on customer-sort data. In the stage which provides priorities we present new data in which product concepts were created by product-development experts such that each concept stressed the fulfillment of one primary customer need. Customer interest in and preference for these concepts are compared to measured and estimated importances. We also address the question of whether frequency of mention can be used as a surrogate for importance. Finally, we examine the stated goal of QFD, customer satisfaction. Our data demonstrate a self-selection bias in satisfaction measures that are used commonly for QFD and for corporate incentive programs. We close with a brief application to illustrate how a product-development team used the voice of the customer to create a successful new product.

This research investigates the antecedents and consequences of customer satisfaction. We develop a model to link explicitly the antecedents and consequences of satisfaction in a utility-oriented framework. We estimate and test the model against alternative hypotheses from the satisfaction literature. In the process, a unique database is analyzed: a nationally representative survey of 22,300 customers of a variety of major products and services in Sweden in 1989–1990. Several well-known experimental findings of satisfaction research are tested in a field setting of national scope. For example, we find that satisfaction is best specified as a function of perceived quality and “disconfirmation”—the extent to which perceived quality fails to match prepurchase expectations. Surprisingly, expectations do not directly affect satisfaction, as is often suggested in the satisfaction literature. In addition, we find quality which falls short of expectations has a greater impact on satisfaction and repurchase intentions than quality which exceeds expectations. Moreover, we find that disconfirmation is more likely to occur when quality is easy to evaluate. Finally, in terms of systematic variation across firms, we find the elasticity of repurchase intentions with respect to satisfaction to be lower for firms that provide high satisfaction. This implies a long-run reputation effect insulating firms which consistently provide high satisfaction.

The PIMS (Profit Impact of Marketing Strategies) data entail sparse time-series observations for a large number of strategic business units (SBUs), In order to estimate disaggregate marketing mix elasticities of demand, a natural solution is to pool different SBUs. The traditional, a priori approach is to pool together those SBUs which one believes in advance to be very similar with respect to their marketing mix elasticities. We propose an alternative maximum likelihood, latent-pooling method for simultaneously pooling, estimating, and testing linear regression models empirically. This method enables the determination of a “fuzzy” pooling scheme, while directly estimating a set of marketing mix elasticities and intertemporal covariances for each pool of SBUs. Our analyses reveal different magnitudes and patterns of marketing mix elasticities for the derived pools. Pool membership is influenced by demand characteristics, business scope, and order of market entry.