Download - Conjoint Analysis Final
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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