Conjoint Analysis

what is conjoint analysis

quantitative marketing research method used to understand consumer prreferences + trade offs

main goal of conjoint

understand how people value product attributes and make trade offs

why is conjoint important

helps firms determine what customers value and what they willing to pay for

how widely is conjoint used

extremely widely

most common uses of conjoint

new product development

pricing new products

repositioning products

feature pricing

what is the key assumption of conjoint analysis

products are bundles of independent attributes

what is an attribute

a product feature

what are levels

the specific options within an attribute

what is a product profile

a specific combination of attribute levels

what is utility

the total value a consumer derives from a product

what is a partworth

the value contributed by a specific attribute level

how is total utility calculated

utility = sum of all partworths

how do consumers make choices in conjoint analysis

they choose the option with the highest utility

what is the first step in conjoint analysis

study design

what happens in the data collection step

surveys are administered to the target market

what happens in the estimation step

statistical models estimate partworths

what is the dependent variable in conjoint estimation

product choices

what are the independent variable

product attributes and their levels

what is the final step

analysis and insights

why cant we rely on direct survey qs about preferences

people are inconsistent and struggle to evaluate abstract features

how does conjoint solve this problems

it forces consumers to make trade-offs w

what advantage does conkoint have over simple surveys

quantifies preferences on common scale

how does conjoint improve prediction

it better reflects real decision-making behaviour