Optimal product design using conjoint analysis - Columbia ...
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Columbia Business School
by R KOHLI - 1989 - Cited by 98 - Related articles
Theory and Methodology. Optimal product design using conjoint analysis: Computational complexity and algorithms *. Rajeev KOHLI. Joseph M. Katz Graduate .
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Columbia Business School
by R KOHLI - 1989 - Cited by 98 - Related articles
Theory and Methodology. Optimal product design using conjoint analysis: Computational complexity and algorithms *. Rajeev KOHLI. Joseph M. Katz Graduate .The Algorithms for Constructing Efficient Experimental ...
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University of Bucharest
by M KUZMANOVIĆ - Cited by 3 - Related articles
Keywords: Conjoint analysis, experimental design, efficiency, optimality criteria ... favoured as well as the algorithms for choosing efficient designs. The paper is
Abstract
Conjoint analysis is a technique for measuring consumer preferences for
products or services. It is also a method for simulating consumersí possible reactions to changes in current products or newly introduced
products into an existing competitive market. One of the fundamental problems in performing Conjoint analysis is how
to generate experimental designs. The purpose of an experimental design
is to give a rough overall idea as to the shape of the experimental
response surface, while only requiring a relatively small number of runs. These designs are expected to be orthogonal and balanced in an ideal
case. In practice, though, it is hard to construct optimal designs and thus
constructing of near optimal and ef icient designs is carried out.
In this paper it will be present the basic criteria of the design ef iciency
and some algorithms which can be used for its construction. Special
attention will be paid to the algorithm we developed and implemented in
Visual Basic application as the procedure in MCON software.
Keywords: Conjoint analysis, experimental design, ef iciency, optimality
fmi.unibuc.ro/.../CD/.../Marija%20Kuzmanovic.p...
University of Bucharest
by M KUZMANOVIĆ - Cited by 3 - Related articles
Keywords: Conjoint analysis, experimental design, efficiency, optimality criteria ... favoured as well as the algorithms for choosing efficient designs. The paper is Abstract Conjoint analysis is a technique for measuring consumer preferences for products or services. It is also a method for simulating consumersí possible reactions to changes in current products or newly introduced products into an existing competitive market. One of the fundamental problems in performing Conjoint analysis is how to generate experimental designs. The purpose of an experimental design is to give a rough overall idea as to the shape of the experimental response surface, while only requiring a relatively small number of runs. These designs are expected to be orthogonal and balanced in an ideal case. In practice, though, it is hard to construct optimal designs and thus constructing of near optimal and ef icient designs is carried out. In this paper it will be present the basic criteria of the design ef iciency and some algorithms which can be used for its construction. Special attention will be paid to the algorithm we developed and implemented in Visual Basic application as the procedure in MCON software. Keywords: Conjoint analysis, experimental design, ef iciency, optimality
Conjoint analysis (marketing)
From Wikipedia, the free encyclopedia
- See also: Conjoint analysis, Conjoint analysis (in healthcare), IDDEA, Rule Developing Experimentation, Discrete choice models.
Conjoint analysis is a statistical technique used in market research to determine how people value different attributes (feature, function, benefits) that make up an individual product or service.
The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products, the implicit valuation of the individual elements making up the product or service can be determined. These implicit valuations (utilities or part-worths) can be used to create market models that estimate market share, revenue and even profitability of new designs.
Conjoint originated in mathematical psychology and was developed by marketing professor Paul Green at the Wharton School of the University of Pennsylvania and Data Chan. Other prominent conjoint analysis pioneers include professor V. “Seenu” Srinivasan of Stanford University who developed a linear programming (LINMAP) procedure for rank ordered data as well as a self-explicated approach, Richard Johnson (founder of Sawtooth Software) who developed the Adaptive Conjoint Analysis technique in the 1980s[1] and Jordan Louviere (University of Iowa) who invented and developed Choice-based approaches to conjoint analysis and related techniques such as Best-Worst Scaling.
Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations research. It is used frequently in testing customer acceptance of new product designs, in assessing the appeal of advertisements and in service design. It has been used in product positioning, but there are some who raise problems with this application of conjoint analysis (see disadvantages).
Conjoint analysis techniques may also be referred to as multiattribute compositional modelling, discrete choice modelling, or stated preference research, and is part of a broader set of trade-off analysis tools used for systematic analysis of decisions. These tools include Brand-Price Trade-Off, Simalto, and mathematical approaches such as AHP,[2] evolutionary algorithms or Rule Developing Experimentation.
Conjoint Design[edit]
A product or service area is described in terms of a number of attributes. For example, a television may have attributes of screen size, screen format, brand, price and so on. Each attribute can then be broken down into a number of levels. For instance, levels for screen format may be LED, LCD, or Plasma.
Respondents would be shown a set of products, prototypes, mock-ups, or pictures created from a combination of levels from all or some of the constituent attributes and asked to choose from, rank or rate the products they are shown. Each example is similar enough that consumers will see them as close substitutes, but dissimilar enough that respondents can clearly determine a preference. Each example is composed of a unique combination of product features. The data may consist of individual ratings, rank orders, or preferences among alternative combinations.
As the number of combinations of attributes and levels increases the number of potential profiles increases exponentially. Consequently, fractional factorial design is commonly used to reduce the number of profiles that have to be evaluated, while ensuring enough data are available for statistical analysis, resulting in a carefully controlled set of "profiles" for the respondent to consider
Types of conjoint analysis[edit]
The earliest forms of conjoint analysis were what are known as Full Profile studies, in which a small set of attributes (typically 4 to 5) are used to create profiles that are shown to respondents, often on individual cards. Respondents then rank or rate these profiles. Using relatively simple dummy variable regression analysis the implicit utilities for the levels can be calculated.
Two drawbacks were seen in these early designs. Firstly, the number of attributes in use was heavily restricted. With large numbers of attributes, the consideration task for respondents becomes too large and even with fractional factorial designs the number of profiles for evaluation can increase rapidly.
In order to use more attributes (up to 30), hybrid conjoint techniques were developed. The main alternative was to do some form of self-explication before the conjoint tasks and some form of adaptive computer-aided choice over the profiles to be shown.
The second drawback was that the task itself was unrealistic and did not link directly to behavioural theory. In real-life situations, the task would be some form of actual choice between alternatives rather than the more artificial ranking and rating originally used. Jordan Louviere pioneered an approach that used only a choice task which became the basis of choice-based conjoint analysis and discrete choice analysis. This stated preference research is linked to econometric modeling and can be linked revealed preference where choice models are calibrated on the basis of real rather than survey data. Originally choice-based conjoint analysis was unable to provide individual level utilities as it aggregated choices across a market. This made it unsuitable for market segmentation studies. With newer hierarchicalBayesian analysis techniques, individual level utilities can be imputed back to provide individual level data.
Information collection[edit]
Data for conjoint analysis are most commonly gathered through a market research survey, although conjoint analysis can also be applied to a carefully designedconfigurator or data from an appropriately design test market experiment. Market research rules of thumb apply with regard to statistical sample size and accuracy when designing conjoint analysis interviews.
The length of the research questionnaire depends on the number of attributes to be assessed and the method of conjoint analysis in use. A typical Adaptive Conjoint questionnaire with 20-25 attributes may take more than 30 minutes to complete. Choice based conjoint, by using a smaller profile set distributed across the sample as a whole may be completed in less than 15 minutes. Choice exercises may be displayed as a store front type layout or in some other simulated shopping environment.
Analysis[edit]
Depending on the type of model, different econometric and statistical methods can be used to estimate utility functions. These utility functions indicate the perceived value of the feature and how sensitive consumer perceptions and preferences are to changes in product features. The actual estimation procedure will depend on the design of the task and profiles for respondents, in the type of specification, and the scale of measure for preferences (it can be ratio, ranking, choice) which can have a limited range or not. For rated full profile tasks, linear regression may be appropriate, for choice based tasks, maximum likelihood estimation, usually withlogistic regression are typically used. The original methods were monotonic analysis of variance or linear programming techniques, but contemporary marketing research practice has shifted towards choice-based models using multinomial logit, mixed versions of this model, and other refinements. Bayesians estimators are also very popular. Hierarchical Bayesian procedures are nowadays relatively popular as well.
[PDF]determining customer value dimensions: a conjoint analysis ...
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University of Bucharest
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marija.kuzmanovic@fon.bg.ac.rs, ... analysis. Conjoint analysis is a research technique for measuring customers' ... Key words: Customer value, conjoint analysis, preferences, product development. ... is created by any product or service attribute, which motivates the customer to ..... consumer perception of Service Quality.Innovative Management and Firm Performance: An ...
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by M Kuzmanovic - 2013 - Cited by 8 - Related articles
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