conjoint analysis
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Conjoint analysis
M.Karthikram
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Definition
Conjoint Analysis (kuh n-joint uh-nal-uh-sis):
•“Conjoint analysis is a multivariate technique developed specifically to understand how respondents develop preferences for objects (products, services, or ideas).”
•Source: Hair, Black, Babin, and Anderson (2009)
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History• Conjoint analysis grew out of conjoint measurement
in mathematical psychology.• Green and Rao (1971) and Rao and Wind (1975)
were some of the first academics to use conjoint analysis in a business context—marketing research.
• During the 1980s, conjoint analysis gained widespread acceptance in many industries, with usage rates increasing up to tenfold.
• By the end of the 1990s, many other disciplines had adopted conjoint analysis techniques.
• Sources: Hair et. al (2009) and Kuhfeld (2010)
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Different perspectives and 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
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Products/Services are Composed of Features/Attributes
• Credit Card:
Brand + Interest Rate + Annual Fee + Credit Limit
• On-Line Brokerage:
Brand + Fee + Speed of Transaction + Reliability of Transaction + Research/Charting Options
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Company’s objective
• How our product or services compares to our competitors and how we can best optimise the value we give to the customer?
• By Conjoint analysis:• we can give up the total value or utility
value our product is giving the customer and compare it to the value for the competition.
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Requirements for successful conjoint analysis
• Defining the total utility of the object• All attributes that potentially create or detract
from the overall utility of the product or service should be included.
• Specifying the determinant factors• include the factors that best differentiate
between the objects.
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Assumptions of conjoint analysis
• The product is a bundle of attributes.• Utility of a product is a simple function of the
Utility of attributes.• Utility predicts behaviour.
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How Does Conjoint Analysis Work?
• We vary the product features (independent variables) to build many (usually 12 or more) product concepts
• We ask respondents to rate/rank those product concepts (dependent variable)
• Based on the respondents’ evaluations of the product concepts, we figure out how much unique value (utility) each of the features added
• (Regress dependent variable on independent variables; betas equal part worth utilities.)
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Rules for Formulating Attribute Levels
• Don’t include too many levels for any one attribute
– The usual number is about 3 to 5 levels per attribute– The temptation (for example) is to include many, many
levels of price, so we can estimate people’s preferences for each
– But, you spread your precious observations across more parameters to be estimated, resulting in noisier (less precise) measurement of ALL price levels
– Better approach usually is to interpolate between fewer more precisely measured levels for “not asked about” prices
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Rules for Formulating Attribute Levels
• Whenever possible, try to balance the number of levels across attributes
• There is a well-known bias in conjoint analysis called the “Number of Levels Effect”
– Holding all else constant, attributes defined on more levels than others will be biased upwards in importance
– For example, price defined as ($10, $12, $14, $16, $18, $20) will receive higher relative importance than when defined as ($10, $15, $20) even though the same range was measured
– The Number of Levels effect holds for quantitative (e.g. price, speed) and categorical (e.g. brand, color) attributes
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Rules for Formulating Attribute Levels
• Make sure levels from your attributes can combine freely with one another without resulting in utterly impossible combinations (very unlikely combinations OK)
– Resist temptation to make attribute prohibitions (prohibiting levels from one attribute from occurring with levels from other attributes)!
– Respondents can imagine many possibilities (and evaluate them consistently) that the study commissioner doesn’t plan to/can’t offer. By avoiding prohibitions, we usually improve the estimates of the combinations that we will actually focus on.
– But, for advanced analysts, some prohibitions are OK, and even helpful
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Formula
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• ACA
• Adaptive Conjoint Analysis is a hybrid conjoint approach in that it uses • both analysis of product combinations (combinations of factor levels) as well • as self-reported importance information to derive utilities.
• Three components of analysis:
• -Factor ratings (preferability)• -Rank order of levels within factors• -Graded comparisons of partial product combinations
• -It allows for a larger number of factors and levels can be analyzed.• -Can only be administered via computer.• -Cannot analyze interactions.• -Price elasticity still an issue.
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EXAMPLE: factor ratings (prefer ability)
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EXAMPLE: comparisons of factor levels
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EXAMPLE: product comparisons
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EXAMPLE: purchase likelihood
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• CBC
• CBC, or Choice Based Conjoint, has become the preferred method, due to it’s ability to truly gauge price elasticity, and it’s easy to comprehend trade-off task.
• Full product combinations are pitted against each other in “choice sets”. Respondents choose among the products depicted, or (as an option) can choose none of the products.
• A respondent typically receives anywhere from 10 to 20 choice sets, depending on the number of factors and levels in the design.
• -It’s modeling capabilities (interactions, special effects, etc.) are seen as an • improvement from prior methods.• -Due to relative pricing, elasticity models are more accurate.• -Like ACA, allows for more factors and levels than traditional method.• -Individual utilities now available (first versions generated aggregate
models)
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Choice based conjoint analysis question
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Strengths of CBC
• Questions 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
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Weaknesses of CBC
• 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
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