See also: Conjoint analysis, Conjoint analysis (in healthcare) Conjoint analysis, also called multi-attribute compositional models, is a statistical technique that originated in mathematical psychology. ... See also: Conjoint analysis (in marketing) // Pharmaceutical manufacturers need deeper and deeper market information they can rely on to make the right decisions and to identify the most promising market opportunities. ...

Conjoint analysis is a statistical technique used in market research to determine how people value different features that make up an individual product or service. Market research is the process of systematic gathering, recording and analyzing of data about customers, competitors and the market. ...

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. Other prominent conjoint analysis pioneers include Richard Johnson (founder of Sawtooth Software) who developed the Adaptive Conjoint Analysis technique in the 1980s and Jordan Louviere who invented and developed Choice-based approaches to conjoint analysis and related techniques such as MaxDiff. For information regarding the parapsychology phenomenon of distance knowledge, see psychometry. ... Paul Green (17 March 1894 - 4 May 1981) American Pulitzer Prize-winning playwright. ... There are very few or no other articles that link to this one. ...

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). Wikibooks has more about this subject: Marketing Look up marketing in Wiktionary, the free dictionary. ... This article or section does not adequately cite its references or sources. ... It has been suggested that this article or section be merged with Operations management. ... In business and engineering, new product development is the complete process of bringing a new product to market. ... Commercialism redirects here. ... Service design can be both tangible and intangible. ... A products position is how potential buyers see the product. ...

Conjoint analysis techniques may also be referred to as multiattribute compositional modelling, discrete choice modelling, stated preference research and form part of a broader set trade-off analysis tools. These tools include Brand-Price Trade Off, Simalto and other more bespoke tools including new mathematical approaches (e.g. evolutionary algorithms) (for example, Affinnova's Interactive Discovery & Design by Evolutionary Algorithms (IDDEA) technology). An evolutionary algorithm (also EA, evolutionary computation, artificial evolution) is a generic term used to indicate any population-based optimization algorithm that uses mechanisms inspired by biological evolution, such as reproduction, mutation and recombination (see genetic operators). ... IDDEA (Interactive Discovery & Design by Evolutionary Algorithms) is a quantitative method used in market research. ...

Conjoint Design

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. Levels for screen format may be CRT, LCD or Plasma for instance.

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, whilst ensuring enough data is available for statistical analysis, resulting in a carefully controlled set of profiles for the respondent to consider. In statistics, fractional factorial designs are experimental designs consisting of a carefully chosen subset (fraction) of the experimental runs of a full factorial design. ...

Types of conjoint analysis

The earliest forms of conjoint analysis were what are known as Full Profile studies. Here a small set of attributes (typically 4-5) 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. In statistics, regression analysis examines the relation of a dependent variable (response variable) to specified independent variables (predictors). ...

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. 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. Adaptive Conjoint Analysis from Sawtooth is the best known of these techniques.

The second drawback was that the task itself was unrealistic. In real life situations the task would be some form of actual choice between alternatives. Jordan Louviere pioneered an approach that just used a choice task which became the basic of choice-based conjoint and discrete choice analysis. Discrete choice analysis is a statistical technique. ...

Information collection

Data for conjoint analysis is most commonly gathered through a market research survey, although conjoint analysis can also be applied to a carefully designed configurator or data from an appropriately design test market experiment. Mass customization, in marketing, manufacturing, and management, is the use of flexible computer-aided manufacturing systems to produce custom output. ... A test market, in the field of business and marketing, is a geographic region or demographic group used to gauge the viability of a product or service in the mass market prior to a wide scale roll-out. ...

Analysis

Any number of algorithms may be used to estimate utility functions. The original methods were monotonic analysis of variance or linear programming techniques, but these are largely obsolete in contemporary marketing research practice. Far more popular are Hierarchical Bayesian procedures that operate on choice data. These utility functions indicate the perceived value of the feature and how sensitive consumer perceptions and preferences are to changes in product features.

Advantages

• able to use physical objects
• measures preferences at the individual level
• estimates psychological tradeoffs that consumers make when evaluating several attributes together

Disadvantages

• only a limited set of features can be used because the number of combinations increases very quickly as more features are added.
• information gathering stage is complex
• difficult to use for product positioning research because there is no procedure for converting perceptions about actual features to perceptions about a reduced set of underlying features
• respondents are unable to articulate attitudes toward new categories

See also

• marketing
• new product development
• product positioning
• advertising
• marketing research
• quantitative marketing research

Wikibooks has more about this subject: Marketing Look up marketing in Wiktionary, the free dictionary. ... In business and engineering, new product development is the complete process of bringing a new product to market. ... A products position is how potential buyers see the product. ... Commercialism redirects here. ... Research is the search for and retrieval of existing, discovery or creation of new information or knowledge for a specific purpose. ... Quantitative marketing research is a social research method that utilizes statistical techniques. ...

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External sources

• Green, P. and Srinivasan, V. (1978) Conjoint analysis in consumer research: Issues and outlook, Journal of Consumer Research, vol 5, September 1978, pp 103-123.
• Green, P. Carroll, J. and Goldberg, S. (1981) A general approach to product design optimization via conjoint analysis, Journal of Marketing, vol 43, summer 1981, pp 17-35.
• Orme, B. (2005) Getting Started with Conjoint Analysis Madison, WI: Research Publishers LLC. ISBN 0-9727297-4-7
• Evolutionary Algorithms, a viable alternative to Conjoint Analysis
• "What is Conjoint Analysis?" From Sawtooth Software (includes interactive training module)
• Wharton: A century of Innovation
• Interactive demonstration of how conjoint works from dobney.com
• Knowledge at Wharton: Using Rule Developing Experimentation (RDE) to Determine Consumer Preferences ([1])