Multivariate Data Analysis(1)

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Multivariate Data Analysis

Conjoint Analysis


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Conjoint analysis is a statistical techniqueused in market research to determine howpeople value different features that make upan individual product or service.

What Is Conjoint Analysis?


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Important Terminology

Factor

Level

ProfileUtility

Part-Worth


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Conjoint analysis take these attributesand level description of product/servicesby asking people to make a number ofchoices between different product

EXAMPLE:- Would you choose Phone A orPhone B

Understanding the ConjointAnalysis

Phone A Phone B

Weight 200g 120g

Battery life 21hours 10 hours

price Rs 5000

Rs 8000


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Researcher can work out numerically (fromthe responses)have valuable each of the

level is relative others around it- this valueis known as the utility of the level

We can also compare across attributes to

see which attribute make have thegreatest impact in making a choice.

Utilies value for each level of weightRelative importance of attribute

0

2

4

6

8

10

12

0

2

4

6

8

10

12


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Usages of Conjoint Analysis

Individual Consumer

Segment Level

Across Segment


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Designing a Conjoint Analysis

Stage 1

Formulation of the Problem

Stage 2Construct the stimuli

Stage 3

Decide on the form of input

Stage 4

Select the conjoint procedure


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A Practical Example ofConjoint Analysis

Q: Would you prefer an airline travel with Aor B?

A: regular seats, that costs $400 and

takes 5 hours.B: costs $700 has extra-wide seats

and takes 3hours.

Extending this, we see that if seat comfort,price and duration are the only relevantattributes, there are potentially eight travelchoices.

Choice

SeatComfort

Price

Duration

Choice

SeatComfort

Price

Duration

1 Extra widea-Wide

$700700

5hrHours

5 Regularlar

$70000

5hr5Hours

2Extra-ExtrawideWide

$7$70000

3hrHours

6 RRegularegular

$700$700

33hrHours

3 ExtrExtra widea-Wide

$$40000

5 5hrHours

7 ReRegulargular

$400$400

5hrHours

4 EExtra widextra-Wide

$4$$400000

33hrHours

8 RegRegularular

$4$40000

3H3hrours


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Given the above alternatives:

Product 4 is very likely the most preferred choice.

Product 5 is probably the least preferred product.

The preference for the other choices is determined bywhat is important to that individual.

Conjoint analysis can be used to determine:

Relative importance of each attribute, attribute level,and combinations of attributes.

If the most preferable product is not feasible for somereason, identify the next most preferred alternative.

Using other information, such as backgrounddemographics be able to identify market segments for


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Utility Value

A traveler may like the comfort and arrivaltime of a particular travel, but rejectpurchase due to the cost.

In this case, price has a high utility value.

Utility can be defined as a number whichrepresents the value that consumers place

on an attribute.

Utility represents the relative "worth" of theattribute.

Price

Utility

$4000

655

$$700700

5


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Types of Conjoint Analysis

Adaptive Conjoint Analysis (ACA)

Choice Based Conjoint (CBC)Full profile Conjoint Analysis


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Advantage of ConjointAnalysis

Estimates psychological tradeoffsthat consumers make when

evaluating several attributestogether

Measures preferences at the

individual levelUncovers real or hidden drivers

which may not be apparent to the

respondent themselves


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Realistic choice or shopping task

Able to use physical objects

If appropriately designed, theability to model interactionsbetween attributes can be used todevelop needs based segmentation


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Limitation of ConjointAnalysis

Designing conjoint studies can becomplex

With too many options, respondentsresort to simplification strategies

Respondents are unable to articulateattitudes toward new categories, ormay feel forced to think about issuesthey would otherwise not give muchthought to


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Poorly designed studies may over-value emotional/preferencevariables and undervalue concrete

variablesDoes not take into account the

number items per purchase so it

can give a poor reading of marketshare


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Documents

Multivariate Data Analysis(1)