Submitted by:AMIT KUMAR

MINAKSHI ROYMUKUL SINGH

VIBHANSHU KUMAR

Flow of contents…Introduction Definition Conjoint analysis decision stepsAreas of applicationModelsConcept exemplifiedExamplesAdvantage & Disadvantage

IntroductionMetric/ Non- metric responses conversion using

an interval scaleExamples-

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What is Conjoint AnalysisResearch technique developed in early 70s

Measures how buyers value components of a product/service bundle

Dictionary definition-- “Conjoint: Joined together, combined.”

Marketer’s catch-phrase-- “Features CONsidered JOINTly”

DefinitionConjoint analysis is a statistical technique used

in market research to determine how people value different features that make up an individual product or service.

sometimes referred to as “trade-off” analysis because respondents in a conjoint study are forced to make trade-offs between product features.

Objective:- to determine what combination of a limited number of attributes is most influential on respondent choice or decision making.

Different Perspectives, Different Goals

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

Sellers want to maximize profits by: 1) minimizing costs of providing features 2) providing products that offer greater overall value than the competition

Conjoint Analysis Contd…It is a tool that allows a subset of the

possible combinations of product features to be used to determine the relative importance of each feature in the purchasing decision.

based on the fact that the relative values of attributes considered jointly can better be measured than when considered in isolation.

Conjoint Analysis Contd…Today it is used in many of the social

sciences and applied sciences including marketing, product management, and operations research.

It is used frequently in testing customer acceptance of new product designs, in assessing the appeal of advertisements and in service design.

It has been used in product positioning.

Conjoint Analysis Contd…Measures consumer preferences for

alternative product concepts.Helps derive utility value attached by

customers to the product attributes.Hypothetical models proposition.Helps estimate market share and profits

Contd.PsychometricsMarketing researchConjoint is becoming very much removed

from theoretical roots i.e. hypothetical models toNumerical measurement of behaviorMoving from non-metric to metric

Research Problem

Define Stimuli (factors and levels)

Basic model form

Full profile Trade off Pairwise

Data Collection

Select preference measure

Survey Administration

Assumptions

Select estimation technique

Evaluate results

Interpret results

Validate

Apply results

Conjoint Analysis Decision Process

This technique requires a lot of upfront work to think through the design, data collection, and analysis options.

1.Choose product attributes, for example, appearance, size, or price.

2.Choose the values or options for each attribute. For example, for the attribute of size, one may choose the levels of 5", 10", or 20". The higher the number of options used for each attribute, the more burden that is placed on the respondents.

3.Define products as a combination of attribute options. The set of combinations of attributes that will be used will be a subset of the possible universe of products.

4.Choose the form in which the combinations of attributes are to be presented to the respondents. Options include verbal presentation, paragraph description, and pictorial presentation.

Steps in Developing a Conjoint Analysis

5. Decide how responses will be aggregated. There are three choices - use individual responses, pool all responses into a single utility function, or define segments of respondents who have similar preferences.

6. Select the technique to be used to analyse the collected data. The part-worth model is one of the simpler models used to express the utilities of the various attributes. There also are vector (linear) models and ideal-point (quadratic) models.

Steps in Developing a Conjoint Analysis(cont…)

Areas of application1. To find the product with the optimum set of

features2. Determine the relative importance of each

feature in consumer choices3. Estimate market share among products4. Identify market segments5. Evaluate the impact of price changes or

other marketing mix decisions.

Three Main “Flavors” of Conjoint Analysis

Traditional Full-Profile Conjoint

Adaptive Conjoint Analysis (ACA)

Choice-Based Conjoint (CBC), also known as Discrete Choice Modeling (DCM)

Strengths of Traditional ConjointGood for both product design and pricing issuesCan be administered on paper, computer/internetShows products in full-profile, which many argue

mimics real-worldCan be used even with very small sample sizes

o Limited ability to study many attributes (more than about six)

o Limited ability to measure interactions and other higher-order effects (cross-effects)

Weaknesses

Adaptive Conjoint Analysis

Developed in 80s by Rich Johnson, Sawtooth Software

Devised as way to study more attributes than was prudent with traditional full-profile conjoint

Adapts to the respondent, focusing on most important attributes and most relevant levels

Shows only a few attributes at a time (partial profile) rather than all attributes at a time (full-profile)

Strengths of ACAAbility to measure many attributes, without

wearing out respondentRespondents find interview more interesting and

engagingEfficient interview: high ratio of information gained

per respondent effortCan be used even with very small sample sizes

WeaknessPartial-profile presentation less realistic than

real worldRespondents may not be able to assume attributes

not shown are “held constant”

Often not good at pricing researchTends to understate importance of price, and within

each respondent assumes all brands have equal price elasticities

Must be computer-administered (PC or Web)

Choice-Based Conjoint (CBC)Became popular

starting in early 90s

Respondents are shown sets of cards and asked to choose which one they would buy

Can include “None of the above” response, or multiple “held-constant alternatives”

Choice-Based Conjoint Question

Strengths of CBCQuestions closely mimic what buyers do in real world: choose from

available products

Can investigate interactions, alternative-specific effects

Can include “None” alternative, or multiple “constant alternatives”

Paper or Computer/Web based interviews possible

• Usually requires larger sample sizes than with CVA or ACA

• Tasks are more complex, so respondents can process fewer attributes (CBC recommended <=6)

• Complex tasks may encourage response simplification strategies

• Analysis more complex than with CVA or ACA

Weaknesses

Concept exemplifiedGreen & wind’s illustration

3 package designs (A,B,C)3 brand designs (x, y, z)3 prices (1,2,3)Guarantee of the product (y/n)Derive utility for all attributes b/w 0 to 1.Higher utility stronger preference.

Factorial design combinations(3*3*3*2)=54 combinations possible.

A simple exampleWe want to market a new golf ball.There are three important product features.

Average Driving DistanceAverage Ball LifePrice

Average Driving Distance

Average Ball Life Price

275 yards 54 holes Rs. 70

250 yards 36 holes Rs. 80

225 yards 18 holes Rs. 92

• Obviously, the “ideal” ball from consumers’ view is:– Average Driving Distance: 275 yards– Average Ball Life: 54 holes– Price: Rs. 70

• The “ideal” ball from manufacturers’ view is:– Average Driving Distance: 225 yards– Average Ball Life: 18 holes– Price: Rs. 92

• Lose money selling the first, but consumers won’t be happy with the second option.

(Average life vs. Average distance)Buyer 1

54 holes

36 holes

18 holes

275 yards

1 2 4

250 yards

3 5 6

225 yards

7 8 9

Buyer 2

54 holes

36 holes

18 holes

275 yards

1 3 6

250 yards

2 5 8

225 yards

4 7 9

Both buyers agree on the most and the least preferred ball.But from other choices, buyer 1 tends to trade-off ball life for distance.Buyer 2 makes the opposite trade-off.The differences between Figure 2 and 1 are the essence of conjoint analysis.

Simple Example of Conjoint AnalysisProduct Option

Cuisine Distance Price PreferenceRank Value

1 Italian Near $10 8

2 Italian Near $15 6

3 Italian Far $10 4

4 Italian Far $15 2

5 Thai Near $10 7

6 Thai Near $15 5

7 Thai Far $10 3

8 Thai Far $15 1

Product Option

Cuisine Distance Price PreferenceRank Value

1 Italian Near $10 ?

2 Italian Near $15 ?

3 Italian Far $10 ?

4 Italian Far $15 ?

5 Thai Near $10 ?

6 Thai Near $15 ?

7 Thai Far $10 ?

8 Thai Far $15 ?

Type of crust (3 types) Type of cheese (3 types) Price (3 levels)

Attributes Topping (4 varieties) Amount of cheese (2

levels)

A total of 216 (3x4x3x2x3) different pizzas can be developed from these options!

Crust Topping

Type of cheese

PanThinThick

PineappleVeggieSausagePepperoni

RomanoMixed cheeseMozzeralla

Amount of cheese Price400 gm.600 gm.

Rs 300Rs. 200Rs. 150

Designing a Frozen Pizza

Advantageestimates psychological tradeoffs that consumers

make when evaluating several attributes togethermeasures preferences at the individual leveluncovers real or hidden drivers which may not be

apparent to the respondent themselvesrealistic choice or shopping taskable to use physical objectsif appropriately designed, the ability to model

interactions between attributes can be used to develop needs based segmentation

Disadvantagedesigning conjoint studies can be complexwith too many options, respondents resort to simplification

strategiesdifficult to use for product positioning research because

there is no procedure for converting perceptions about actual features to perceptions about a reduced set of underlying features

respondents are unable to articulate attitudes toward new categories, or may feel forced to think about issues they would otherwise not give much thought to

poorly designed studies may over-value emotional/preference variables and undervalue concrete variables

does not take into account the number items per purchase so it can give a poor reading of market share

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