lecture9 conjoint analysis

32
CONJOINT ANALYSIS Prof Narayan Janakiraman

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Page 1: Lecture9 conjoint analysis

CONJOINT ANALYSIS

Prof Narayan Janakiraman

Page 2: Lecture9 conjoint analysis

Customer Value Assessment Procedures

CustomerValue

Attitude-Based

Direct Questions

Unconstrained Constrained/Compositional Methods Multiattribute value analysis

Indirect/(Decompositional Methods) Conjoint analysis

Behavior-Based Inferential/Value-Based

Page 3: Lecture9 conjoint analysis

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

Page 4: Lecture9 conjoint analysis

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

Page 5: Lecture9 conjoint analysis

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

Compositionalmethods

45 10 40 5

Page 6: Lecture9 conjoint analysis

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?

Page 7: Lecture9 conjoint analysis

P&G and Disposable Diapers

Question: What value do consumers associate with two improved features in disposable diapers:

Improved absorbency

Elastic waistband

Page 8: Lecture9 conjoint analysis

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

Page 9: Lecture9 conjoint analysis

Eg. Packaged Soup

Page 10: Lecture9 conjoint analysis

Eg. Packaged Soup individual concept

Page 11: Lecture9 conjoint analysis

Eg. Packaged Soup Conjoint INPUTCards and Ratings

Page 12: Lecture9 conjoint analysis

Eg. Packaged Soup Conjoint OUPUT 1Part Worths

Page 13: Lecture9 conjoint analysis

Eg. Packaged Soup Conjoint OUPUT 1Part Worths

Page 14: Lecture9 conjoint analysis

Weights of Attributes

Flavor 45%

Calories 25%

Salt Freeness 22%

Price 8%

Eg. Packaged Soup Conjoint OUPUT 2Importance

Page 15: Lecture9 conjoint analysis

How were the part-worths calculated and how was the importance determined?

How does one usePart Worths?Importance?

The Black box

Page 16: Lecture9 conjoint analysis

The Conjoint Model

attributes

utilitiesauauU ...)()( 21

Page 17: Lecture9 conjoint analysis

Notebook computer example

1) Processing speed: 1.5 GHz or 2.5 GHz

2) Hard drive: 120 GB or 160 GB

3) Memory: 1 GB or 2 GB RAM

There are 8 different combinations of notebook - defined as product profiles:

Page 18: Lecture9 conjoint analysis

One respondent’s preference

Page 19: Lecture9 conjoint analysis

Input to computer system – dummy variable regression

Page 20: Lecture9 conjoint analysis

Part Worth Estimation

Regression of ranks vs the attributes

U = a + b1*Processor + b2*Hard Drive + b3*Memory

U1 a b1 0 b2 0 b3 0 a

U2 a b1 1 b2 0 b3 0 a b1

U3 a b1 0 b2 1 b3 0 a b2

U4 a b1 0 b2 0 b3 1a b3

a U1 1

b1 U2 U1 5 14

b2 U3 U1 3 12

b3 U4 U1 2 11

The intution

Page 21: Lecture9 conjoint analysis

Forecast preferences to check accuracy

8111

6101

4110

7011

3218

3217

3216

3215

bbbaU

bbbaU

bbbaU

bbbaU

Page 22: Lecture9 conjoint analysis

Weightage and Relative Importance of Each Attribute

7

4

321

1 bbb

b

b2

b1 b2 b3

2

7

b3

b1 b2 b3

1

7

Processor Speed

Hard Drive

Memory

= 57%

= 29%

= 14%

Page 23: Lecture9 conjoint analysis

Segment consumers based on preferences

Are there segments in terms of preferences?Here preference is the “basis” and “age” could be the descriptor

Page 24: Lecture9 conjoint analysis

Eg. Packaged SoupWhich is the most important attribute & which is the best product to introduce?

Page 25: Lecture9 conjoint analysis

Conjoint Simulation - The Motivation

1. What share can the new brand obtain?

2. Where does this share will come from?

Page 26: Lecture9 conjoint analysis

Conjoint Simulation - The Principle

Before introduction share: A=40%, B=60%.

After introduction share: A=20%,B=50%, and New=30%.

Page 27: Lecture9 conjoint analysis

Other ways of getting responses

Page 28: Lecture9 conjoint analysis

Stage 1—Design the conjoint study:

Step 1.1: Select attributes relevant to the product or service category,Step 1.2: Select levels for each attribute, andStep 1.3: Develop the product bundles to be evaluated.

Stage 2—Obtain data from a sample of respondents:

Step 2.1: Design a data-collection procedure, andStep 2.2: Select a computation method for obtaining part-worth functions.

Stage 3—Evaluate product design options:

Step 3.1: Segment customers based on their part-worth functions,Step 3.2: Design market simulations, andStep 3.3: Select choice rule.

Conjoint Study Process

Page 29: Lecture9 conjoint analysis

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

Page 30: Lecture9 conjoint analysis

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

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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

Page 32: Lecture9 conjoint analysis

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 5. Amount of Cheese Pineapple 2 oz. Veggie 4 oz. Sausage 6 oz. Pepperoni

A total of 324 (3 * 4 * 3 * 3 * 3) different pizzas can be developed from these options!