CONJOINT ANALYSIS

Introduction

Conjoint Analysis is a statistical technique used in market research to determine how people value different features that make up an individual product or service.

Why do we need conjoint analysis?

Buyers point of view

•Buyers want most desirable features at lowest possible price

Sellers point of view

•Minimizing cost by providing features

•Providing products that offer greater overall value than their competitors

Conjoint analysis -

• Identifies the attributes(factors) important in a choice decision.

• Identifies the way the attributes are combined to make the decision.

• Determines the utility value to each of the levels of each of the attributes considered in the decision.

Steps in conjoint analysis:

Attributes relevant to study are identified

Specification of attribute variation and levels

Data collection from respondents

Analysis of collected data

Describing products in attributes and levels:• An attribute is a general feature of a product or

service – say size, colour, speed, delivery time. Each attribute is then made up of specific levels. So for the attribute colour, levels might be red, green, blue and so on.

• To understand how conjoint analysis works, we need to be able to describe the products or services consistently in terms of attributes and levels in order to see what is being traded off.

• Conjoint analysis takes these attribute and level descriptions of product/services and uses them in interviews by asking people to make a number of choices between different products.

For instance would you choose phone A or phone

B? Phone A Phone B

Weight 200g 120g Battery life 21 hours 10 hours Price Rs.7000 Rs.9000

Attributes

The thought process might be: • “Phone A is bulkier, but has the battery life and

lower cost, but Phone B is smaller and neater yet more expensive and with lower battery life. Lighter weight is worth more than the loss of battery life, and it’s probably worth the extra 2000, so I’d choose B in this instance.”

• The researcher can work out numerically how

valuable each of the levels is relative to the others around it – this value is known as the utility of the level.

Methodologies:

• Stimulus Construction: Two attributes at a Time; Full-profile design; Fractional Factorial Design; Self Explicated; Choice Based;

• Model Estimation Method: Part Worth Function; Vector Model; Ideal Point Model;

• Simulation Analysis Models: Maximum Utility; Average Utility; LOGIT;

Two attributes at a Time:• Now let’s consider a new apartment based on safety of

the location and the walking time from the apartment to school. Please enter your preference by ranking each cell of this matrix from 1 to 9, where 1 is your most preferred choice & 9 is least preferred.

• Safety of apartment location:

WALKING TIME

TO CLASS VERY SAFE AVERAGE SAFETY VERY UNSAFE

10 MINUTES 1 2 7

20 MINUTES 3 4 8

30 MINUTES 5 6 9

Brand Name

Microprocessor Screen Size/Wt Hard drive Amount of RAM Price

Dell T2400 1.88 GHz 12.1in / 4lbs 80 GB 512 MB

$1009

Toshiba U1300 1.63 GHz 14.1in / 6lbs 60 GB 2 GB $1149

●

Acer T7200 2.00 GHz 15.4in / 8lbs 40 GB 1 GB $ 1299

Choice ●●

•Example Choice Set

Choice-based conjoint:

• “East coast method” after the full profile approach popularized by Paul E. Green of the Wharton School at the University of Pennsylvania.

• The following conjoint example focuses on the:

Evaluation of advertising appeal for allergy medication. The medication is described by 4 attributes:

• Efficacy Claim,

• Endorsement Source,

• Superiority Claim, and

• Relief Claim.

Each attribute has 4 levels

16 respondents

Cards for the 4x4x4x4 Allergy Advertising Design

Design Matrix Matrix of Dummy Variables Card Rank Matrix

Describes the profiles ranked The design matrix is converted into a binary matrix of dummy variables: (0 = 0 0; 1 = 1 0; 2 = 0 1)

The dependent variable is the rank order preference score of each card viewed.

0, 0, 0, 0 1, 0, 1, 2 2, 0, 2, 3 3, 0, 3, 1 0, 1, 1, 1 1, 1, 0, 3 2, 1, 3, 2 3, 1, 2, 0 0, 2, 2, 2 1, 2, 3, 0 2, 2, 0, 1 3, 2, 1, 3 0, 3, 3, 3 1, 3, 2, 1 2, 3, 1, 0 3, 3, 0, 2

1 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 1 0 0 0 1 0 3 0 1 0 0 0 0 0 1 0 0 0 1 4 0 0 1 0 0 0 0 0 1 1 0 0 5 0 0 0 1 0 0 1 0 0 1 0 0 6 1 0 0 1 0 0 0 0 0 0 0 1 7 0 1 0 1 0 0 0 0 1 0 1 0 8 0 0 1 1 0 0 0 1 0 0 0 0 9 0 0 0 0 1 0 0 1 0 0 1 0 10 1 0 0 0 1 0 0 0 1 0 0 0 11 0 1 0 0 1 0 0 0 0 1 0 0 12 0 0 1 0 1 0 1 0 0 0 0 1 13 0 0 0 0 0 1 0 0 1 0 0 1 14 1 0 0 0 0 1 0 1 0 1 0 0 15 0 1 0 0 0 1 1 0 0 0 0 0 16 0 0 1 0 0 1 0 0 0 0 1 0

Ranks are shown for 3 respondents.

Respondent 1:5 16 9 4 2 3 13 14 8 6 1 7 11 10 15 12

Respondent 2 :3 8 7 11 4 1 12 9 13 5 10 14 16 2 15 6

Respondent 3: 13 14 15 6 11 4 5 1 10 2 7 9 12

The conjoint analysis conducts a regression analysis using the matrix of dummy variables and the data matrix of card ranks for the respondent.

The regression is repeated for each respondent’s data using the same matrix of dummy variables.

• The part-worth function is defined as:

m k

• U(x) = Σ Σ α x

i=1 j=1

where:

α = part wort contribution associated with jth level ( j=1,2,3…k) of ith attribute (i=1,2 3…m)

k =No of levels of attribute

m=No. of attributes

x=1

ij ij

ij

i

i

ij

R1 .00 2.25 3.00 2.75 3.00 2.50 .00 6.50 .00 4.75 5.00 3.25 5.75 .00 8.00 3.25 R2 5.00 .00 7.00 6.00 .75 .00 4.00 3.25 .00 5.25 2.75 6.00 1.25 .00 3.00 2.75 R3 4.50 .00 .50 1.00 6.75 .00 1.75 4.50 3.75 3.00 2.25 .00 1.25 4.50 7.75 6.50

For respondent 1 ,

Attribute 1: Levels 1-4 .00 2.25 3.00 2.75 Attribute 2: Levels 1-4 3.00 2.50 .00 6.50 Attribute 3: Levels 1-4 00 4.75 5.00 3.25 Attribute 4: Levels 1-4 5.75 .00 8.00 3.25

Utility values

• Average Utility Scores :

Efficacy |No med |No med |Relief |Right F|

IMPORT.%:14.63 | 3.48| 2.17| 4.10| 1.18|

Endorsements |Most re|Most re|Nat. Ga|Prof. G| IMPORT.%:54.24 | 3.13| 3.88| 2.57| 1.41|

Superiority |Less se|Rec. 2:|Relief |Leading| IMPORT.%:17.76 | 2.78| 2.63| 2.71| 2.31|

Gardening |Won |Enjoy r|Brand u|Relieve| IMPORT.%:13.37 | 2.39| 2.74| 2.58| 2.54|

Figure 1: Graphing of Average Utility Scores

Conclusion:

• Conjoint Analysis is concerned with understanding how people make choices between products or services, so that businesses can design new products or services that better meet customers underlying needs.

• A key benefit of conjoint analysis is the ability to produce dynamic market models that enable companies to test out what steps they would need to take to improve their market share, or how competitors behaviour will affect their customers.

• Conjoint analysis, when applied to product, service, and communications projects identifies which product or service attributes are most preferred and are best combined to produce maximum effect.