Conjoint analysis

M.Karthikram

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)

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)

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

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

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.

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.

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.

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.)

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

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

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

Formula

•

• 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.

EXAMPLE: factor ratings (prefer ability)

EXAMPLE: comparisons of factor levels

EXAMPLE: product comparisons

EXAMPLE: purchase likelihood

• 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)

Choice based conjoint analysis question

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

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