Conjoint Analysis

Sunny Bose

Definitions & Key Terms Conjoint Analysis- Is a term given to a multi variate analytical

tool that CONsiders JOINTly the effect of the individual attributes of a product or a brand. This helps the marketer to analyze the utility that each varied combinations of the attributes of the product is providing to the customer.

Utility- The subjective preference judgment of an individual that represent the total value or worth he is putting on the product having a combination of certain attributes.

Part- Worth- The values of the individual attributes that sum up or produce the total utility for the product.

Additive Model- Assumes that individuals just add up the individual Part- Worths to get to the overall utility.

Interaction Model- Unlike the additive model, here the individual also considers the interactions between two independent Part- Worth while valuing the overall utility of the product.

Definitions & Key Terms (Contd.) Factorial Design- Method of designing stimuli by

generating all possible combinations of levels. For example a three factor (attribute) conjoint analysis with three levels each will result in 3x3x3 = 27 combinations which will form the total stimuli in the analysis.

Full Profile Method- Analysis carries on based on the respondent’s evaluation of all the possible combinations in the stimuli.

Fractional Factorial Design- Method of designing a stimuli that is a subset of the full factorial design so as to estimate the results based on the assumed compositional rule.

Orthogonality- Joint occurrence of levels of different attributes will be equal or in proportional number of times.

Validation Stimuli- Set of stimuli that are not used for estimation of the Part- Worths. Estimated Part- Worths are then used to predict preference for the validation stimuli to assess validity and reliability of the original estimates.

Definitions & Key Terms (Contd.) Pair wise Comparison Method- Method of presenting a

pair of stimuli to the respondent for evaluation, with the respondent selecting one of the stimuli as preferred.

Self Explicated Model- Compositional technique where the respondent provides the Part- Worth estimates directly without making choices.

Adaptive (Hybrid) Conjoint Analysis (ACA)- ACA asks respondents to evaluate attribute levels directly, and then to assess the importance of each attribute, and finally to make paired comparisons between profile descriptions.

Choice Based Conjoint (CBC)- An alternative form of conjoint analysis where the respondent’s task is of choosing a preferred profile similar to what he would actually buy in the marketplace. CBC analysis lets the researcher include a "None" option for respondents, which might read "I wouldn't choose any of these."

Usages of Conjoint Analysis Breaking down customer’s overall utility from

the product into values put in by him on the products individual attributes.

Product planning and design Accommodating conflicting interests-

Buyers want all of the most desirable features at lowest possible price

Sellers want to maximize profits by: Minimizing the costs of features provided Providing products that offer greater overall value than

the competitors.

Market segmentation based on the utility structures

Conjoint Analysis- Process Flow

Stage 1Identify the research

problem

Stage 2Decide on the

attributes and their levels

Focused Group is the most practiced

Stage 3Chose the

methodologyTraditional, ACA or CBC

Stage 4Collect responses

Rating or rank order

Stage 5Run analysisIndividual or aggregative

Stage 6Interpret results

Stage 7Validate the resultsExternal or internal

validity tests

Stage 8Apply the Conjoint

resultsProduct designing,

market segmentation etc.

Types of Conjoint Analysis Traditional Conjoint

Full Profile Partial Profile / Fractional Factorial Design Paired Comparison Self Explicated

Adaptive Conjoint Analysis (ACA) Choice Based Conjoint (CBC)

How Conjoint Analysis works Decompose the overall utility into its

individual attribute’s part- worths Additive model- Overall utility = Sum total of all

part- worths Total worth/ Utility = Part- worth of level i for factor 1+

Part- worth of level j for factor 2 + …. Part- worth of level n for factor m

Interaction model- Overall utility > Sum total of all part-worths Total worth/ Utility = Part- worth of level i for factor 1+

Part- worth of level j for factor 2 + …. Part- worth of level n for factor m + I (Interaction effect between the attributes and their level)

Generally, the Traditional Conjoint analyses use additive models whereas ACA and CBC use interaction models

Traditional Conjoint Analysis Full Profile Partial Profile Paired Comparison Test Self Explicated Method

Full Profile Let us assume that a

cricket bat maker is planning to launch a new professional level cricket bat. Based on the inputs from focused group, salesman and experts, he finds the following attributes important for a professional bat.

From the table let us take a profile as an example that a respondent would require to rank.

Like the profile in example, a full profile would provide 2x2x2 = 8 combinations

Attribute

Level 1 Level 2

Type Heavy Long handle

Wood Kashmir willow

English willow

Grip Single Multi

Attribute Level

Wood English Willow

Grip Single

Type Long handle

Full Profile (Contd.) Now, let us assume a respondent ranks all

these profiles based on his utility from these profiles (1- Highest and 8- Lowest)

Profile Type Wood Grip Rank

1 Heavy English willow

Multi 1

2 Heavy English willow

Single 2

3 Heavy Kashmir willow

Multi 4

4 Heavy Kashmir willow

Single 5

5 Long handle English willow

Multi 3

6 Long handle English willow

Single 6

7 Long handle Kashmir willow

Multi 7

8 Long handle Kashmir willow

Single 8

Full Profile (Contd.) To estimate the Part- Worth of each attribute,

average ranks or ratings for each attribute level is measuredAttribute Levels Ranks Across Stimuli Average Rank (AR) Deviation from Overall

Rank (DOR)

Type

Heavy 1,2,4,5 3.0 -1.5

Long handle 3,6,7,8 6.0 +1.5

Wood

English willow 1,2,3,6 3.0 -1.5

Kashmir willow 4,5,7,8 6.0 +1.5

Grip

Multi 1,3,4,7 3.75 -0.75

Single 2,5,6,8 5.25 +0.75

Full Profile (Contd.) These deviations of ranks from the overall

average rank is used to compute the individual Part- Worths StD= SDxSV, where SV= No. of levels/ SD= 6/10.125=

0.592Attribute Levels Reversed

Deviations (RD)Squared Deviation (SD)

Standardized Deviation(StD)

Estimated Part- Worth

Type

Heavy +1.5 2.25 +1.332 +1.154

Long handle -1.5 2.25 -1.332 -1.154

Wood

English willow

+1.5 2.25 +1.332 +1.154

Kashmir willow

-1.5 2.25 -1.332 -1.154

Grip

Multi +0.75 0.5625 +0.333 +0.577

Single -0.75 0.5625 -0.333 -0.577

Full Profile (Contd.) Let us check whether the Part- worths are

reliableProfile

Type Wood

Grip P-W Type

P- W Wood

P-W Grip

Total P-W

Estimate Rank

Rank

1 Heavy

EW Multi 1.154 1.332 0.333 2.819 1 1

2 Heavy

EW Single 1.154 1.332 -0.333 2.153 2 2

3 Heavy

KW Multi 1.154 -1.332 0.333 0.155 4 4

4 Heavy

KW Single 1.154 -1.332 -0.333 -0.511 6 5

5 LH EW Multi -1.154 1.332 0.333 0.511 3 3

6 LH EW Single -1.154 1.332 -0.333 -0.155 5 6

7 LH KW Multi -1.154 -1.332 0.333 -2.153 7 7

8 LH KW Single -1.154 -1.332 -0.333 -2.819 8 8

Partial Profile Partial profile is a necessity when the number of

attributes and the levels within the attributes are large. In such a case, it becomes almost impossible for the

respondent to evaluate the full profile 4 attributes having 4 levels each will result in 4x4x4x4 = 256

profiles

Partial profile considers a subset of the entire which would be representative of the full profile This is done through an orthogonal process so that the

profiles contain the levels equally or in proportion. Partial profile eases the pressure of evaluation for the

respondent Out of 256 profiles, a partial profile might contain only 16

representative profiles

Paired Comparison Test Also known as Trade off Approach as the

respondent is forced to make a trade- offs between the attribute levels.

Instead of full profiles or partial profiles, trade off matrices are created considering all the levels of two attributes taken at a time.

Incase of more than two attributes sequential trade off matrices are given to be ranked or rated in an order such that there is at least one attribute from the previous matrix is present. In Paired Comparison Tests, the value of the

individual attributes come out from the different ratings its levels receive in a paired combination with the other attributes.

Paired Comparison Test (Contd.) Let us consider that a realtor is considering to

build a multi storied residential apartment. From his prior knowledge he knows that other than price, the important considerations for purchasing a flat are: proximity of schools, markets, hospitals and other

utilities, availability of transportation to various locations of

the city Provision of elevator and garage

On these attributes he can give the following options:

Attributes Level 1 Level 2

Proximity Yes No

Transportation Yes No

Provision Yes No

Paired Comparison Test (Contd.) Unlike Full Profile which would generate 2x2x2

= 8 combinations, the Paired Comparison Test in this case would generate

Attributes Proximity (Yes) Proximity (No)

Transportation (Yes)

9 6

Transportation (No)

5 3

Attributes Proximity (Yes) Proximity (No)

Provision (Yes) 9 6

Provision (No) 4 2

Attributes Provision (Yes) Provision (No)

Transportation (Yes)

10 4

Transportation (No)

4 2

Paired Comparison Test (Contd.) From the matrices it is evident that when considering the combinations

between transportation - provision and transportation – proximity, the respondent has rated the provision (Yes) higher than proximity (Yes) and again provision (No)lower than proximity (No)(transportation is constant). Value of Provision > Value of Proximity

Similarly, between provision- transportation and provision- proximity, the combinations of transportation (Yes) got higher rating than proximity (Yes) whereas, transportation (No) got lower ratings than proximity (No). Value of Transportation > Value of Proximity

Finally, taking proximity constant in proximity- provision and proximity- transportation, the combinations with provisions (Yes) have either got equal or higher rating than combinations with transportation (Yes) and provision (No) have equal or lower ratings than transportation (No). Value of Provision > value of Transportation

Thus, Provision > Transportation > Proximity

Self Explicated Method Purists do not consider it to be a conjoint as

there is no trade off to be made. Compositional techniques as the respondents

rate or rank the attributes and their levels. Preferable option over traditional conjoint

when the attributes and their levels are large Used as a fundamental part of ACA or hybrid

conjoint.

Self Explicated Method (Contd.) Please rate the levels in a scale of 1-10 (1-

Lowest, 10- Highest) based on the value you think they would provides you and divide 100 points among the attributes based on the importance you give to each of them for contributing to the functionability of a laptop (Total points should not be more or less than 100).Attribute Level 1 Level 2 Level 3 Level 4

Hard Disk 150 GB 200 GB 250 GB 300 GB

RAM 1 GB 2 GB 3 GB 4 GB

Processor 1.5 GHz 1.8 GHz 2.0 GHz 2.2 GHz

OS Win XP Win Vista (Home)

Win Vista (Pro) Linux

Self Explicated Method (Contd.) Below is the table showing the self explicated

ratings. Note, Total possible value for the entire profile = (40)x100= 4000 Hard Disk = 980/4000 = 0.245 RAM = 560/4000 = 0.14 Processor = 750/ 4000 = 0.1875 Operating System = 440/4000 = 0.11

Attribute Level 1 Level 2 Level 3 Level 4 Total

Hard Disk (35)

5 6 8 9 (28)x35= 980

RAM (20) 6 6 7 9 (28)x20= 560

Processor (25)

6 7 8 9 (30)x25= 750

OS (20) 4 6 9 3 (22)x20= 440

Self Explicated Method (Contd.) The inherent problem with this method is that

respondents inadvertently tend to give higher ratings to the levels that have higher value. As a result, at the initial stage itself this estimation technique is flawed.

Due to the absence of trade off while rating the stimuli, the respondents have the inclination to rate the attributes and their levels based on what he thinks to be most ideal and not what gives him the greatest utility.

When the attributes are large it is taxing on the respondent to rate them or put value to them objectively.

Thank You.