Author: Vivek Agarwal
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.
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 analysis techniques
may also be referred to as multi-attribute compositional modeling, discrete
choice modeling, 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 various mathematical approaches.
Consumers examine a range of
features or attributes and then make judgements or trade-offs to determine
their final purchase choice. Conjoint analysis examines these trade-offs to
determine the combination of attributes that will be most satisfying to the consumer.
By using conjoint analysis a company can determine the optimal features for
their product or service.
Using conjoint analysis, we
can calculate which factor has a high utility value. Utility can be defined as
a number which represents the value that consumers place on an attribute. In
other words, it represents the relative "worth" of the attribute.
The importance of an
attribute can be calculated by examining the range of utilities (that is, the
difference between the lowest and highest utilities) across all levels of the
attribute. These ranges tell us the relative importance of each attribute.a
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.
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.
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