Conjoint Analysis
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
Types of conjoint analysis
Most conjoint
analysis studies carried out professionally use either Adaptive
Conjoint Analysis (ACA) which relies on computer based interviewing, or
Choice-based Conjoint. However, there are actually many "flavours" of
conjoint analysis depending on the task at hand.
Choice-Based Conjoint (CBC) is favoured academically and
widely used for pricing and brand value studies, ACA for larger more marketing
focused work. Other forms of conjoint incorporate Buy-your-own tasks or take
the basic principles and extend them to create tailored conjoint designs for
specific markets.
There are three key design
elements for conjoint analysis. Firstly, breaking down a product or
service into component attributes and levels. Then, the choice of how to
present a series of descriptions (product profiles) and what method should be
used to find out which are most preferred. And finally the method for the
calculation of the value of each attribute and level to the market - the
importance and utility scores, or part-worths.
The design decision of which to use depends on the number
of attributes you have, the contact method and time available for the interview
- for instance, on-line, face-to-face CAPI, or postal or paper-based
interviewing. In addition, the different flavours of conjoint analysis each
have differing characteristics in analysis. If data is available at an
individual level as with Adaptive Conjoint Analysis, then it is simpler to
develop market segmentation schemes. If the data needs to be more robust,
perhaps because of the inclusion of attributes that may interact with each
other, then Choice Based Conjoint analysis may be more relevant. If you run
into conjoint analysis as a student, then it is most probably through the use
of full-profile conjoint analysis. There are other options available depending
on the task at hand.
Adaptive Conjoint Analysis -
ACA
Adaptive Conjoint Analysis (ACA) is one of two most
common methods for carrying out conjoint analysis. The benefits of ACA are that
it allows for a large number of attributes (up to 30) and levels (up to 7 per
attribute) to be used. However, ACA does require a computer-based interview and
the large number of attributes means that it is common for an ACA interview to
last 45 minutes or more. In addition, some of the methods it uses to simplify
the task of working out utilities mean that some care is needed in choosing and
designing the attributes in order to get reliable results.
Technically ACA is known as a hybrid technique as it
contains elements of 'self-explication' followed by the trade-off tasks
themselves. ACA itself is produced by Sawtooth Software and can be conducted
face-to-face or on-line. Telephone use of ACA is difficult and paper-base
questionnaires are not possible.
Choice Based Conjoint
Analysis- CBC
The most common alternative to ACA is Choice-based
conjoint (CBC). Although this uses the same over-arching principles as ACA, in
design, implementation and calculation it is completely different.
Whereas ACA has respondents selecting from products
described with two or three attributes, CBC shows full descriptions using all
the attributes available. In addition, CBC can show more than just two
"products" at the same time, together with a none-of-these option
enabling more realistic choice decisions to be evaluated.
The limitation on the amount a respondent can absorb at a
time, combined with the rapidly increasing number of "full-profile"
combinations that are possible means that choice-based conjoint is typically
limited to 5-7 attributes (in contrast to 25-30 for ACA).
An additional twist is that utilities and importances in
CBC are calculated across a sample as a whole, whereas for ACA you get
utilities and importances for each individual in the sample. To get at
individual level estimations relies on techniques such as Hierarchical Bayes
analysis to infer back individual values. However, combined with the lower
number of attributes, choice-based studies require far shorter questionnaires
(15-20 mins) and can be designed to be purely paper-based.
The advantages choice-based conjoint gives you are
greater robustness of results - particularly for pricing work (although there
are ways of getting around ACA's pricing limitations), combined with shorter
and therefore less costly fieldwork. It is also favoured for its rigour
academically. In applications such as pharmaceutical work it also enables
comparisons with fixed products or fixed tasks enabling you to test new
formulations against an existing gold standard.
The disadvantages are the lower number of attributes that
are possible unless you move to more complex bespoke designs using partial
profiles, and the lack of directly valued individual level utilities - although
techniques such as hierarchical bayesian analysis seeks to remedy this by
post-hoc simulation of individual level values. However, if you are looking to
use conjoint analysis for clustering or segmentation you will need to be aware
of the trade-offs needed to get individual level utility scores.
Discrete Choice Analysis
A more advanced form of choice-based conjoint is Discrete
Choice Analysis (also known as "stated preference research"). DCA
studies are particularly popular for transportation studies looking at modal
choice - the preference between a train, car and airline for instance. The main
difference from CBC is the inclusion of continuous variables such as price and
time. This allows the ability to examine the varying costs of the ticket with
varying times taken to travel and so to establish the value of time for the
journey. This enables transport economists to make statements like "2cm
extra leg room is worth 10 minutes longer journey time or £40 extra fare"
or "an extra train every 15 minutes would encourage x% of car drivers to
switch to the train".
Full profile Conjoint
Analysis
An additional option that dates back a long time but that
is still used is full profile conjoint analysis. Full-profile is the original
form of conjoint and is still in use, though predominantly in the US it would
appear. Like choice-based conjoint this uses a more limited number of
attributes to describe the product or service, but sufficient cards or
treatments are shown to one respondent to enable individual level utilities to
be calculated. A fractional factorial design is used to specify a fixed set of
profiles that need to be shown for analysis. The difficulty is that this does
limit the number of attributes quite severely. However these old school studies
are still popular for simple, non-computer-based conjoint projects and are most
common for students learning about conjoint for the first time.
Other forms and formats
Recent developments in conjoint include the Adaptive
Choice Based Conjoint method from Sawtooth. Which combines elements of a
configurator, an adaptive element and choices. In addition we have our own
dobney.com conjoint designer that allows for a range of other more bespoke
research areas where traditional forms of conjoint analysis are lacking or
where current designs can seem too difficult from a respondent point-of-view
These include emotional association tasks, and repertoire purchasing (where
someone is buying a bundle of products across a range of uses), volumetric
measurement and improved choice displays such as using sliders or more
interactive elements to encourage a fuller participation in the decision making
process. Similarly, for international research the presentation of prices in
different formats and currencies requires careful consideration.
Deciding which format of
conjoint analysis to use
We often find that the choice of the type of conjoint
analysis to use depends on a number factors and it's often difficult for
someone new to conjoint analysis to visualise the options and in cases where
there are 6-8 attributes, there can be several options and approaches. We often
produce several versions and presentations for illustration to help clients see
and understand which form and presentation version of conjoint to use, without
compromising the final statistical quality of the survey.
Author
Pulkit Kabra
Team I
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