See also: Conjoint analysis, Conjoint analysis, IDDEA, Rule Developing Experimentation.
Conjoint analysis is a statistical technique used in market research to determine how people
value different attributes 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 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 procedure for rank ordered data as well as a self-explicated
approach, Richard Johnson 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. 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.
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, evolutionary algorithms
or Rule Developing Experimentation. 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. 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
The earliest forms of conjoint analysis were what are known as Full Profile studies, in
which a small set of attributes 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, 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 hierarchical
Bayesian analysis techniques, individual level utilities can be imputed back to provide individual
level data. Information collection
Data for conjoint analysis are 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. 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
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 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 with logistic 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.
Advantages estimates psychological tradeoffs that consumers
make when evaluating several attributes together measures preferences at the individual level
uncovers real or hidden drivers which may not be apparent to the respondent themselves
realistic choice or shopping task able to use physical objects
if appropriately designed, the ability to model interactions between attributes can
be used to develop needs based segmentation Disadvantages
designing conjoint studies can be complex with too many options, respondents resort
to simplification strategies 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, or may feel forced to think about issues they would otherwise
not give much thought to poorly designed studies may over-value emotional/preference
variables and undervalue concrete variables does not take into account the number items
per purchase so it can give a poor reading of market share
See also Advertising
Marketing Marketing research
New product development Product positioning
Quantitative marketing research TURF Analysis
References
External links Green, P. and Srinivasan, V. 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. A general
approach to product design optimization via conjoint analysis, Journal of Marketing, vol
43, summer 1981, pp 17–35. Srinivasan, V., A Conjunctive-Compensatory
Approach to the Self-Explication of Multiattributed Preferences, Decision Sciences, Vol. 19, Spring
1998, 295-305. Green, P. E. and Srinivasan V. Conjoint Analysis
in Marketing: New Developments with Implications for Research and Practice, Journal of Marketing,
Vol. 54, October 1990, 3-19. Marder, E. The Assumptions of Choice Modeling
Orme, B. Getting Started with Conjoint Analysis Madison, WI: Research Publishers LLC. ISBN
0-9727297-4-7 Interactive demonstration of how conjoint
works from dobney.com Wharton: A century of Innovation
Knowledge at Wharton: Using Rule Developing Experimentation to Determine Consumer Preferences
What is Conjoint Analysis Video