Conjoint analysis involves showing respondents multiple product or service concepts that vary across key attributes and levels, and analyzing their overall preferences to determine the relative importance or "part-worths" of each attribute and level in driving customer choice. It allows companies to understand customer trade-offs and preferences to help design optimal product offerings. Various methods are used to collect conjoint data and estimate importance weights and part-worth utilities for application in product design, pricing, and market simulations.

1. CONJOINT ANALYSIS

2. Customer Value Assessment
Procedures
Customer
Value
Attitude-Based
Direct Questions
Unconstrained Constrained/Compositional Methods
Multiattribute value analysis
Indirect/(Decompositional Methods)
Conjoint analysis
Behavior-Based Inferential/Value-Based

3. Conjoint Analysis and Concept
Testing
 Concept testing
 Show one product concept and get overall “Purchase
Intent” feedback
 Also get product diagnostics
 Conjoint Analysis
 Show multiple concepts and ask for overall preference
 Concepts differ on Attributes and levels within an
attribute
 Based on overall preference get “part-worths” for
attributes and levels within an attribute

4. A Survey
 Familiarity & usage of value assessment methods
 58 industrial firms in the top 125 of the Fortune 500
list
 16 market research firms from the top 40

5. Survey Results
Method Industrial Market Research
Familiarity % Usage % Familiarity % Usage %
Internal Engg.
Assessment
61.3 42.5 - -
Field value-in-use 63.8 36.3 25 5
Focus group 92.5 60 90 60
Direct survey 91.3 48.8 85 55
Benchmarks 83.8 27.5 80 25
Conjoint 75 28.8 90 60
Compositional
methods
45 10 40 5

6. Conjoint Analysis in Product
Design
Should we offer our business travelers more room space or a
fax machine in their room?
Given a target cost for a product, should we enhance product
reliability or its performance?
Should we use a steel or aluminum casing to increase
customer preference for the new equipment?

7. P&G and Disposable Diapers
 Question: What value do consumers associate with two
improved features in disposable diapers:
 Improved absorbency
 Elastic waistband

8. Conjoint Analysis Assumption
 Products can be defined by their individual
attributes and levels within the attribute
 Consumer responses to the overall preference
can be then partitioned to attributes

9. Eg. Packaged Soup

10. Eg. Packaged Soup individual
concept

11. Eg. Packaged Soup Conjoint
INPUT
Cards and Ratings

12. Eg. Packaged Soup Conjoint
OUPUT 1
Part Worths

13. Eg. Packaged Soup Conjoint
OUPUT 1
Part Worths

14.  Flavor 45%
 Calories 25%
 Salt Freeness 22%
 Price 8%
Eg. Packaged Soup Conjoint
OUPUT 2
Importance
 Weights of Attributes

15. How were the part-worths calculated and
how was the importance determined?
How does one use
Part Worths?
Importance?
The Black box

16. The Conjoint Model
utilities
attributes
U u(a1) u(a2) ...

17. Notebook computer example
Processing speed:
2) Hard drive:
3) Memory:
1.5 GHz or 2.5 GHz
120 GB or 160 GB
1 GB or 2 GB RAM
There are 8 different combinations of notebook - defined as product profiles:

18. One respondent’s preference

19. Input to computer system – dummy variable
regression

20. Part Worth Estimation
Regression of ranks vs the attributes
U = a + b1*Processor + b2*Hard Drive + b3*Memory
The intution
U 1 a b1 0 b2 0 b3 0 a
U 2 a b1 1 b2 0 b3 0 a b1
U 3 a b1 0 b2 1 b3 0 a b2
U 4 a b1 0 b2 0 b 3 1 a b3
a U 1 1
b1 U 2 U 1 5 1 4
b2 U 3 U 1 3 1 2
b3 U 4 U 1 2 1 1

21. Forecast preferences to check
accuracy
U5 a b1 1 b2 1 b3 0 7
U6 a b1 0 b2 1 b3 1 4
U7 a b1 1 b2 0 b3 1 6
U8 a b1 1 b2 1 b3 1 8

22. Weightage and Relative Importance of Each Attribute
b1 4
b1 b2 b3 7
b2 2
b1 b2 b3 7
b3 1
b1 b2 b3 7
Processo
r Speed
Hard
Drive
Memory
= 57%
= 29%
= 14%

23. Segment consumers based on
preferences
Are there segments in terms of preferences?
Here preference is the “basis” and “age” could be
the descriptor

24. Eg. Packaged Soup
Which is the most important attribute & which is the best product to
introduce?

25. Conjoint Simulation - The Motivation
1. What share can the new brand obtain?
2. Where does this share will come from?

26. Conjoint Simulation - The Principle
Before introduction share: A=40%, B=60%.
After introduction share: A=20%,B=50%, and New=30%.

27. Other ways of getting responses

28. Stage 1—Design the conjoint study:
Step 1.1:
Step 1.2:
Step 1.3:
Select attributes relevant to the product or service category,
Select levels for each attribute, and
Develop the product bundles to be evaluated.
Stage 2—Obtain data from a sample of respondents:
Step 2.1:
Step 2.2:
Design a data-collection procedure, and
Select a computation method for obtaining part-worth
functions.
Stage 3—Evaluate product design options:
Step 3.1:
Step 3.2:
Step 3.3:
Segment customers based on their part-worth functions,
Design market simulations, and
Select choice rule.
Conjoint Study Process

29. 29
Attributes Should Be…
 Determinant
 Easily measured and communicated
 Controllable by the company
 Realistic
 Such that there will be preferences for some
levels over others
 Compensatory
 As a set, sufficient to define the choice
situation
 Without built-in redundancies

30. 30
How Many Levels per Attribute?
 Levels and range should be
meaningful, informative, and realistic to
consumers and producers
 Avoiding absurd configurations
 Marginal increases in levels can greatly
increase respondent’s task

31. 31
Which Data Collection Method?
 Full profile: Show complete list of attributes
 Limited to 6-7 attributes
 Pair-wise: Show pairs of attributes in matrix; each cell
rated from most to least preferred
 Lacks realism
 Inconsistent responses likely

32. Designing a Frozen Pizza – Paired
Comparison Approach
1. Crust 2. Type of Cheese 3. Price
Pan Romano $ 9.99
Thin Mixed cheese $ 8.99
Thick Mozzeralla $ 7.99
4. Topping
Pineapple
Veggie
Sausage
Pepperoni
5. Amount of Cheese
2 oz.
4 oz.
6 oz.
A total of 324 (3 * 4 * 3 * 3 * 3) different pizzas can be developed from these options!