Page 267 - DMGT209_QUANTITATIVE_TECHNIQUES_II

Quantitative Techniques-II

Notes
Example: An airline would like to know, which is the most desirable combination of
attributes to a frequent traveller: (a) Punctuality (b) Air fare (c) Quality of food served on the
flight and (d) Hospitality and empathy shown.
Conjoint Analysis is a multivariate technique that captures the exact levels of utility that an
individual customer places on various attributes of the product offering. Conjoint Analysis
enables a direct comparison.

Example: A comparison between the utility of a price level of 400 versus 500, a
delivery period of 1 week versus 2 weeks, or an after-sales response of 24 hours versus 48 hours.
Once we know the utility levels for each attribute (and at individual levels as well), we can
combine these to find the best combination of attributes that gives the customer the highest
utility, the second best combination that gives the second highest utility, and so on. This
information is then used to design a product or service offering.

Application

Conjoint Analysis is extremely versatile and the range of applications includes virtually in any
industry. New product or service design, including the concepts in the pre-prototyping stage
can specifically benefit from the conjoint applications.
Some examples of other areas where this technique can be used are:
 Designing an automobile loan or insurance plan in the insurance industry,
 Designing a complex machine for business customers.

Process

Design attributes for a product are first identified. For a shirt manufacturer, these could be
design such as designer shirts Vs plain shirts, this price of 400 versus 800. The outlets can have
exclusive distribution or mass distribution. All possible combinations of these attribute levels
are then listed out. Each design combination will be ranked by customers and used as input data
for Conjoint Analysis. Then the utility of the products relative to price can be measured.
The output is a part-worth or utility for each level of each attribute. For example, the design may
get a utility level of 5 and plain, 7.5. Similarly, the exclusive distribution may have a part utility
of 2, and mass distribution, 5.8. We then put together the part utilities and come up with a total
utility for any product combination we want to offer, and compare that with the maximum
utility combination for this customer segment.
This process clarifies to the marketer about the product or service regarding the attributes that
they should focus on in the design.
If a retail store finds that the height of a shelf is an important attribute for selling at a particular
level, a well-designed shelf may result from this knowledge. Similarly, a designer of clocks will
benefit from knowing the utility attached by customers to the dial size, background colours, and
price range of the clocks.

Approach

From a discussion with the client, identify the design attributes to be studied and the levels at
which they can be offered. Then build a list of product concepts on offer. These product concepts
are then ranked by customers. Once this data is available, use Conjoint Analysis to derive the
part utilities of each attribute level. This is then used to predict the best product design for the
given customer segment. Use the SPSS Conjoint procedure to analyse the data.

262 LOVELY PROFESSIONAL UNIVERSITY