CONJOINT ANALYSIS
By
Gaurav Gupta
(14075)
Group C
Conjoint analysis is a methodology for the
measurement of psychological judgments, such as consumer preferences. Stimuli
(product configurations, advertisements, movie endings, etc.) are presented to
the respondent for evaluation.
Conjoint Analysis is a research technique used to
measure the trade-offs people make in choosing between products and service
providers. It is also used to predict their choices for future products and
services. Conjoint Analysis assumes that a product can be “broken down” into
its component attributes. For example, a car has attributes such as colour,
price, size, miles-per-gallon, and model style. Using Conjoint Analysis, the
value that individuals place on any product is equivalent to the sum of the
utility they derive from all the attributes making up a product. Further, it
assumes that the preference for a product and the likelihood to purchase it are
in proportion to the utility an individual gains from the product.
The
above points can be summarized as
Ø Identifies the attributes important in a choice
decision;
Ø Identifies the way the attributes are combined to make
the decision;
Ø Determines the utility value to each of the levels of
each of the attributes considered in the decision;
It
attempts to jointly identify the composition model for decision choices and at
the same time estimate the utility value of the attributes that are important
in the choice decision. This analysis helps to predict choice share for
different product configurations that may be introduced into the competitive
marketplace.
For example while buying a laptop it will help the
company to analyse what relative importance is given to features such as price,
battery life, weight, screen size, processor, size of graphics card, RAM and
Memory size by the consumers so that they can design their laptops as per the
analysis.
STAGES IN A CONJOINT ANALYSIS
1.
Identification of the problem, along
with dimensions of the product to be studied. How many attributes are considered
and what are the levels of each attribute.
2.
Develop the study protocol including
all contact, sampling and follow-up protocols. Also develop the survey and
associated visual aids, products, graphics, etc. that are to be used.
3.
Develop the questionnaire... and then
pretest the survey and data collection activity. Evaluate the process and
revise until you are satisfied with the approach, instrument and the
methodology.
4.
Using one of a variety of data
collection procedures described below, collect the data.
5.
Process the data to derive at the individual
respondent level estimates of the part-worths of each person’s utility
function.
6.
Segmentation Analysis: The matrix of
respondent by attribute-level part-worths may then be related to other subject
background data in an effort to identify possible market segments based on
similarities in part-worth functions.
7.
Build and Run the Choice Simulator
using a set of product configurations that represent feasible competitive offerings.
These product profiles are entered into a consumer choice simulator, along with
the earlier computed individual utility functions. Choice simulators differ, in
the simplest case each respondent’s individual part-worth function is used to
compute the utility for each of the competing profiles.
Managerial applications of conjoint analysis
1.
Segmentation: one of the most common
uses of individual level conjoint analysis results is to group respondents with
similar part-worths or importance values to identify segments.
2.
Profitability Analysis: A complement
to the product decision is a marginal profitability analysis of the proposed
product design. If the cost of each feature is known, the cost of each “product”
can be combined with the expected market share and sales volume to predict its
viability. This process might point to a combination of attributes with a
smaller share as the most profitable because of an increased profit margin
resulting from the low cost of particular components.
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