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2014 | OriginalPaper | Buchkapitel

1. Problem Setting

verfasst von : Vithala R. Rao

Erschienen in: Applied Conjoint Analysis

Verlag: Springer Berlin Heidelberg

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Abstract

Several interdependent decisions are involved in the formulation of a marketing strategy for a brand (of a product or service). These include not only decisions about the product’s characteristics but also its positioning, communication, distribution, and pricing to chosen sets of targeted customers. The decisions will need to be made in the wake of uncertain competitive reactions and a changing (and often unpredictable) environment. For a business to be successful, the decision process must include a clear understanding of how customers will choose among (and react to) various competing alternatives. It is well accepted in marketing that choice alternatives can be described as profiles on multiple attributes and that individuals consider various attributes while making a choice. While choosing, consumers typically make trade-offs among the attributes of a product or service. Conjoint analysis is a set of techniques ideally suited to studying customers’ choice processes and determining tradeoffs.

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Nächstes Kapitel Theory and Design of Conjoint Studies (Ratings Based Methods)

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See Corstjens and Gautschi (1983) for detailed methods for testing these axioms.

This method is quite similar to preference analysis in multidimensional scaling which focuses on estimating the ideal points for or weights on perceptual dimensions. These functions will be described in Chap. 2.

A computer software called Policy-PC offered by the Executive Decision Services, Albany, NY allows for a menu of utility functions.

See Wilkie and Pessemier (1973) for a comprehensive review.

Hybrid models involve a combination of several tasks aimed to increase the “efficiency” of data collection in conjoint studies usually for products with a large number of attributes. We will discuss these in Chaps. 2 and 3.

Three surveys were conducted among firms that provide marketing research services by Wittink and his colleagues on the commercial use of conjoint analysis in Europe (1986–91) and USA (1981–85 and 1971–80). While the estimates of actual numbers of projects varied greatly, the authors documented that 698 projects were conducted by 17 firms in the US during the 5 years 1976–80 as compared to 1,062 projects by 66 firms in the US during the 5 years 1981–85. In Europe, 956 projects were conducted by 59 firms during the 5 years, 1986–91. These numbers show extensive diffusion of the methodology on both sides of the Atlantic. Table 1.11 summarizes the results on the utilization of various methods of data collection, analysis, and specific purpose of the conjoint studies in the three surveys

While we are describing an example of product design here, the method of conjoint analysis is versatile in tackling various managerial problems such as product line decisions, competitive decisions, product/service pricing and the like. Several chapters in the book are devoted to these applications.

This high value of R-square is due to the hypothetical data. In general, the fits of the model to data at the individual level will not be this high (and average around 0.7 or so). The fits for some individuals will be poor for a variety of reasons such as unreliable responses and complexity of the task involved. We will discuss later incentive-compatible methods of data collection which ensure more reliable responses.

Other measures such as partial R-squared exist for this purpose; we will discuss them in Chap. 2

The SAS Optex Code for the Transportation Example is as follows:

data ab; n = 1; do time = 10 to 30 by 10; do fare = 55 to 115 by 30; output; n = n + 1; end; end; run;

proc optex data = ab seed = 73462 coding = orth; class time fare; model time fare; blocks structure = (42)3; run;

output out = try number = 1 blockname = blk; proc print data = try; run.

The number of observations was 126 (=3 × 42). The likelihood ratio for the model was 144.5 with 4 degrees of freedom.

Readers may also be interested in the classic paper, Green and Wind (1975), for a comprehensive application of the ratings-based conjoint method.

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Titel: Problem Setting
verfasst von: Vithala R. Rao
Verlag: Springer Berlin Heidelberg
Buch: Applied Conjoint Analysis
Print ISBN: 978-3-540-87752-3

Electronic ISBN: 978-3-540-87753-0

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-3-540-87753-0_1