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2007 | Buch

Conjoint Measurement

Methods and Applications

herausgegeben von: Professor Dr. Anders Gustafsson, Professor Dr. Andreas Herrmann, Professor Dr. Frank Huber

Verlag: Springer Berlin Heidelberg

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The book covers all recent developments in Conjoint Analysis. Leading scientists present theory and applications of this technique. In short, the following models, techniques, and applications are discussed: normative models that maximize return, extension of choice-based conjoint simulations, latent class, hierarchical Bayes modelling, new choice simulators, normative models for representing competitive actions and reactions (based on game theory), applications in diverse areas, computation of monetary equivalents of part worth, share/return optimisation (including Pareto frontier analysis), coupling of conjoint analysis with the perceptual and preference mapping of choice simulator results.

Inhaltsverzeichnis

Frontmatter
1. Conjoint Analysis as an Instrument of Market Research Practice
Abstract
The essay by the psychologist Luce and the statistician Tukey (1964) can be viewed as the origin of conjoint analysis (Green and Srinivasan 1978; Carroll and Green 1995). Since its introduction into marketing literature by Green and Rao (1971) as well as by Johnson (1974) in the beginning of the 1970s, conjoint analysis has developed into a method of preference studies that receives much attention from both theoreticians and those who carry out field studies. For example, Cattin and Wittink (1982) report 698 conjoint projects that were carried out by 17 companies in their survey of the period from 1971 to 1980. For the period from 1981 to 1985, Wittink and Cattin (1989) found 66 companies in the United States that were in charge of a total of 1062 conjoint projects. Wittink, Vriens, and Burhenne counted a total of 956 projects in Europe carried out by 59 companies in the period from 1986 to 1991 (Wittink, Vriens, and Burhenne 1994; Baier and Gaul 1999). Based on a 2004 Sawtooth Software customer survey, the leading company in Conjoint Software, between 5,000 and 8,000 conjoint analysis projects were conducted by Sawtooth Software users during 2003. The validation of the conjoint method can be measured not only by the companies today that utilize conjoint methods for decision-making, but also by the 989,000 hits on www.google.com. The increasing acceptance of conjoint applications in market research relates to the many possible uses of this method in various fields of application such as the following:
  • new product planning for determining the preference effect of innovations (for example Bauer, Huber, and Keller 1997; DeSarbo, Huff, Rolandelli, and Choi 1994; Green and Krieger 1987; 1992; 1993; Herrmann, Huber, and Braunstein 1997; Johnson, Herrmann, and Huber 1998; Kohli and Sukumar 1990; Page and Rosenbaum 1987; Sands and Warwick 1981; Yoo and Ohta 1995; Zufryden 1988) or to
  • improve existing achievements (Green and Wind 1975; Green and Srinivasan 1978; Dellaert et al., 1995), the method can also be applied in the field of
  • pricing policies (Bauer, Huber, and Adam 1998; Currim, Weinberg, and Wittink 1981; DeSarbo, Ramaswamy, and Cohen 1995; Goldberg, Green, and Wind 1984; Green and Krieger 1990; Kohli and Mahajan 1991; Mahajan, Green, and Goldberg 1982; Moore, Gray-Lee, and Louviere 1994; Pinnell 1994; Simon 1992; Wuebker and Mahajan 1998; Wyner, Benedetti, and Trapp 1984),
  • advertising (Bekmeier 1989; Levy, Webster, and Kerin 1983; Darmon 1979; Louviere 1984; Perreault and Russ 1977; Stanton and Reese 1983; Neale and Bath 1997; Tscheulin and Helmig 1998; Huber and Fischer 1999), and
  • distribution (Green and Savitz 1994; Herrmann and Huber 1997; Oppewal and Timmermans 1991; Oppewal 1995; Verhallen and DeNooij 1982).
Anders Gustafsson, Andreas Herrmann, Frank Huber
2. Measurement of Price Effects with Conjoint Analysis: Separating Informational and Allocative Effects of Price
Abstract
One of the most frequent purpose of conjoint analysis is the measurement of price effects (Wittink and Cattin 1989; Wittink, Vriens, and Burhenne 1994). Usually this is be done by describing a number of product alternatives on a small number of attributes, including price, and collecting some kind of preference data for these product alternatives. From the estimated part-worth function for price one can infer price effects (Srinivasan 1979).
Vithala R. Rao, Henrik Sattler
3. Market Simulation Using a Probabilistic Ideal Vector Model for Conjoint Data
Abstract
In commercial applications of conjoint analysis to product design and product pricing it has become quite popular to further evaluate the estimated individual part-worth functions by predicting shares of choices for alternatives in hypothetical market scenarios (Wittink, Vriens and Burhenne 1994 and Baier 1999 for surveys on commercial applications). Wide-spread software packages for conjoint analysis (Sawtooth Software’s 1994 ACA system) already include specific modules to handle this so-called market simulation situation for which, typically, a threefold input is required: (I) The (estimated) individual part-worth functions have to be provided. (II) A definition of a hypothetical market scenario is needed that allows to calculate individual utility values for each available alternative. (III) A so-called choice rule has to be selected, which relates individual utility values to expected individual choice probabilities and, consequently, to market shares for the alternatives. In this context, the determination of an adequate choice rule seems to be the most cumbersome task. Well-known traditional choice rules are, e.g., the 1ST CHOICE rule (where the individuals are assumed to always select the choice alternative with the highest utility value), the BTL (Bradley, Terry, Luce) rule (where individual choice probabilities are related to corresponding shares of utility values), and the LOGIT rule (where exponentiated utility values are used). Furthermore, in newer choice rules implemented by various software developers, the similarity of an alternative to other alternatives is taken into account as a corrective when choice probabilities are calculated (Sawtooth Software 1994).
Daniel Baier, Wolfgang Gaul
4. A Comparison of Conjoint Measurement with Self-Explicated Approaches
Abstract
Over the past two decades conjoint measurement has been a popular method for measuring customers’ preference structures. Wittink and Cattin (1989) estimate that about 400 commercial applications were carried out per year during the early 1980s. In the 1990s this number probably exceeds 1000. The popularity of conjoint measurement appears to derive, at least in part, from its presumed superiority in validity over simpler, less expensive techniques such as self-explication approaches (Leigh, MacKay and Summers 1984). However, when considered in empirical studies, this superiority frequently has not been found (e.g. Green and Srinivasan 1990; Srinivasan and Park 1997). This issue is of major practical relevance. If, at least in certain situations, conjoint measurement is not clearly superior in validity to self-explicated approaches, it becomes highly questionable whether future applications for measuring customers’ preferences should be done by conjoint measurement, as self-explicated approaches are clear advantageous in terms of time and money effort.
Henrik Sattler, Susanne Hensel-Börner
5. Non-geometric Plackett-Burman Designs in Conjoint Analysis
Abstract
Design of experiments is an established technique for product and process improvement that has its origin in the 1920s and the work of Sir Ronald Fisher. Conjoint analysis shares the same theoretical basis as traditional design of experiments, but was originally used within the field of psychology and it was not until the early 1970s that the methodology was introduced into marketing research to form what is called conjoint analysis (Luce and Tukey 1964; Green and Rao 1971; Johnson 1974). Today, conjoint analysis is an established technique for investigating customer preferences.
Ola Blomkvist, Fredrik Ekdahl, Anders Gustafsson
6. On the Influence of the Evaluation Methods in Conjoint Design - Some Empirical Results
Abstract
It is the goal of conjoint analysis to explain and predict preferences of customers (Schweikl 1985). Variants of predefined manifestations of attributes of various product concepts (both real and hypothetical) are created, and these are presented to test persons for evaluation. The contributions (partial benefits) the various attributes make to overall preference (overall benefit) are estimated on the basis of overall preference judgments (Green and Srinivasan 1978).
Frank Huber, Andreas Herrmann, Anders Gustafsson
7. Evolutionary Conjoint
Abstract
Preference analysis and utility measurement remain central topics in consumer research. Although the concept of utility and its measurement was investigated in a large number of studies, it still remains ambiguous due to its unobservability and lack of an absolute scale unit (Teichert 2001a: 26): Whereas utility is praised as a quantitative indicator of consumer behavior, only preference judgments can be observed. These judgments contain error terms stemming from different sources which cannot be separated. This inherent methodological problem of utility measurement has not been handled consistently over years of empirical application.
Thorsten Teichert, Edlira Shehu
8. The Value of Extent-of-Preference Information in Choice-Based Conjoint Analysis
Abstract
It is clear that conjoint analysis has had a substantial impact upon research practice (Wittink and Cattin 1989; Wittink, Vriens and Burhenne 1994). Conjoint analysis has evolved, and along with that evolution has been a gradual shift in the types of responses collected, from rankings to ratings to choices.
Terry Elrod, Keith Chrzan
9. A Multi-trait Multi-method Validity Test of Partworth Estimates
Abstract
Conjoint analysis has already been widely accepted by marketing researchers as a popular instrument for the measurement of consumer preferences. Typical applications of conjoint analysis include new product design based on the relationship between product features and predicted choice behavior, benefit segmentation based on attribute preferences, etc. The popularity of conjoint analysis among marketing researchers hinges on the belief that it produces valid measurements of consumer preferences for the features of a product or service, and that it provides accurate predictions of choice behavior.
Wagner Kamakura, Muammer Ozer
10. Conjoint Preference Elicitation Methods in the Broader Context of Random Utility Theory Preference Elicitation Methods
Abstract
The purpose of this chapter is to place conjoint analysis techniques within the broader framework of preference elicitation techniques that are consistent with the Random Utility Theory (RUT) paradigm. This allows us to accomplish the following objectives: explain how random utility theory provides a level playing field on which to compare preference elicitation methods, and why virtually all conjoint methods can be treated as a special case of a much broader theoretical framework. We achieve this by:
  • discussing wider issues in modelling preferences in the RUT paradigm, the implications for understanding consumer decision processes and practical prediction, and how conjoint analysis methods fit into the bigger picture.
  • discussing how a level playing field allows meaningful comparisons of a variety of preference elicitation methods and sources of preference data (conjoint methods are only one of many types), which in turn allows us to unify many disparate research streams;
  • discussing how a level playing field allows sources of preference data from various elicitation methods to be combined, including the important case of relating sources of preference elicitation data to actual market behaviour;
  • discussing the pros and cons of relaxing the simple error assumptions in basic choice models, and how these allow one to capture individual differences without needing individual-level effects;
  • using three cases studies to illustrate the themes of the chapter.
Jordan Louviere, David Hensher, Joffre Swait
11. Conjoint Choice Experiments: General Characteristics and Alternative Model Specifications
Abstract
Conjoint choice experimentation involves the design of product profiles on the basis of product attributes specified at certain levels, and requires respondents to repeatedly choose one alternative from different sets of profiles offered to them, instead of ranking or rating all profiles, as is usually done in various forms of classic metric conjoint studies. The Multinomial Logit (MNL) model has been the most frequently used model to analyze the 0/1 choice data arising from such conjoint choice experiments (e.g., Louviere and Woodworth 1983; Elrod, Louviere and Davey 1992). One of the first articles describing the potential advantages of a choice approach for conjoint analysis was by Madanski (1980). His conclusion was that conjoint analysts could adopt the random utility model approach to explain gross trends or predilections in decisions instead of each person's specific decision in each choice presented. The real breakthrough for conjoint choice came with the Louviere and Woodworth (1983) article in which they integrated the conjoint and discrete choice approaches.
Rinus Haaijer, Michel Wedel
12. Optimization-Based and Machine-Learning Methods for Conjoint Analysis: Estimation and Question Design
Abstract
Soon after the introduction of conjoint analysis into marketing by Green and Rao (1972), Srinivasan and Shocker (1973a, 1973b) introduced a conjoint analysis estimation method, Linmap, based on linear programming. Linmap has been applied successfully in many situations and has proven to be a viable alternative to statistical estimation (Jain, et. al. 1979, Wittink and Cattin 1981). Recent modification to deal with “strict pairs” has improved the estimation accuracy with the result that, on occasion, the modified Linmap predicts holdout data better than statistical estimation based on hierarchical Bayes methods (Srinivasan 1998, Hauser, et. al. 2006).
Olivier Toubia, Theodoros Evgeniou, John Hauser
13. The Combinatorial Structure of Polyhedral Choice Based Conjoint Analysis
Abstract
In abstract terms conjoint analysis can be seen as fitting a model to preference information elicited from a group of respondents. That is, conjoint analysis comprises two tasks,
(1)
preference data elicitation, and
 
(2)
model fitting to the elicited data.
The model fitting phase is necessary since in general the elicited data tends to be very sparse and can be interpreted meaningfully only in the context of some model, which already encodes general assumptions on the structure of the preferences.
 
Joachim Giesen, Eva Schuberth
14. Using Conjoint Choice Experiments to Model Consumer Choices of Product Component Packages
Abstract
Recent advances in flexibility and automation allow a growing number of manufacturers and service providers to ‘mass-customize’ their products and offer modules from which consumers can create their own individualized products (e.g., Gilmore and Pine 1997). Traditional production processes limit consumer choices to fixed products defined by suppliers, but new mass-customization processes allow consumers to create their own optimal combination of product components. Although mass-customization offers consumers increased flexibility and consumption utility, little is known about how consumer choices to package or bundle separate components differ (if at all) from choices among traditional fixed product options, much less what the impact of packaging product components will be on the market shares of such products or a producer’s overall share in the category.
Benedict G. C. Dellaert, Aloys W. J. Borgers, Jordan J. Louviere, Harry J. P. Timmermans
15. Latent Class Models for Conjoint Analysis
Abstract
Conjoint analysis was introduced to market researchers in the early 1970s as a means to understand the importance of product and service attributes and price as predictors of consumer preference (e.g., Green and Rao 1971; Green and Wind 1973). Since then it has received considerable attention in academic research (see Green and Srinivasan 1978, 1990 for exhaustive reviews; and Louviere 1994 for a review of the behavioral foundations of conjoint analysis). By systematically manipulating the product or service descriptions shown to a respondent with an experimental design, conjoint analysis allows decision-makers to understand consumer preferences in an enormous range of potential market situations (see Cattin and Wittink 1982; Wittink and Cattin 1989; and Wittink, Vriens, and Burhenne 1994 for surveys of industry usage of conjoint analysis).
Venkatram Ramaswamy, Steven H. Cohen
16. A Generalized Normative Segmentation Methodology Employing Conjoint Analysis
Abstract
Since the pioneering research of Wendell Smith (1956), the concept of market segmentation has been one of the most pervasive activities in both the marketing academic literature and practice. In addition to being one of the major ways of operationalizing the marketing concept, marketing segmentation provides guidelines for a firm’s marketing strategy and resource allocation among markets and products. Facing heterogeneous markets, a firm employing a market segmentation strategy can typically increase expected profitability as suggested by the classic price discrimination model which provides the major theoretical rationale for market segmentation (cf. Frank, Massey and Wind 1972).
Wayne S. DeSarbo, Christian F. DeSarbo
17. Dealing with Product Similarity in Conjoint Simulations
Abstract
One of the reasons conjoint analysis has been so popular as a management decision tool has been the availability of a choice simulator. These simulators often arrive in the form of a software or spreadsheet program accompanying the output of a conjoint study. These simulators enable managers to perform ‘what if’ questions about their market - estimating market shares under various assumptions about competition and their own offerings. As examples, simulators can predict the market share of a new offering; they can estimate the direct and cross elasticity of price changes within a market, or they can form the logical guide to strategic simulations that anticipate short- and long-term competitive responses (Green and Krieger 1988).
Joel Huber, Bryan Orme, Richard Miller
18. Sales Forecasting with Conjoint Analysis by Addressing Its Key Assumptions with Sequential Game Theory and Macro-Flow Modeling
Abstract
Conjoint analysis is a research tool for assessing market potential, predicting market share and forecasting sales of new or improved products and services. In general, conjoint analysis follows a two-step process, i.e., (1) estimating utilities for varying levels of product features and (2) simulating marketplace preferences for established, improved, and/or new products. Conjoint analysis was introduced in the 1970s (Green and Rao 1971) and by 1980 had logged more than 1000 commercial applications (Cattin and Wittink 1982). During the 1980s usage increased tenfold (Wittink and Cattin 1989). Today it may be the most widely used quantitative product development tool in the U.S. and Europe (Wittink, Vriens, and Burhenne 1994).
David B. Whitlark, Scott M. Smith
Backmatter
Metadaten
Titel
Conjoint Measurement
herausgegeben von
Professor Dr. Anders Gustafsson
Professor Dr. Andreas Herrmann
Professor Dr. Frank Huber
Copyright-Jahr
2007
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-540-71404-0
Print ISBN
978-3-540-71403-3
DOI
https://doi.org/10.1007/978-3-540-71404-0