1.
6: full conjoint analysis is useful in measuring up t o____ attributes
2.
adaptive conjoint analysis: shows only a few attributes at a time to the respondent and adapts to the respondent as the conjoint exercise goes on
3.
attributes X level X utility value = product: equation for conjoint analaysis
4.
be able to specify the product as a bundle of attributes, know determinant attributes, respondents should be familiar with the product category, firm should be able to act on the results: practical guidelines for the use of conjoint analysis
5.
choice based conjoint analysis: respondent is shown several alternative product choices ad is asked which he or she would prefer
6.
composition rule: how the respondent combines the partworths of the factors to obtain the overall worth
7.
conjoint results: are a valid early indicator of ultimate product success, at least for product extensions
8.
differences of conjoint and gap analysis: Focus is trade-off between attributes, not only identification of gap.
Conjoint analysis can be easily extended to multiple attributes.
Conjoint analysis more realistic (Think about the swimsuit example again).
9.
dimensional analysis: uses any and all features, not just measurements of dimensions (such as spatial--lenght, width and so on) the task involves listing all of the physical features of a product type
10.
information acceleration: customers can have a simulated virtual reality walk through experience that simulates the buying process and marketers can watch what happens
11.
similarities of conjoint and gap analysis: Both are based on product attributes (features/functions/benefits).
Both can demonstrate product difference by numeric difference.
12.
trade off analysis/conjoint analysis: technique that is more commonly used in concept evaluation; used in generating high potential concepts for future evaluation. it is the analysis of the process by which customers compare and evaluate brands based on their attributes or features.
13.
utility value: uses _______ ______ as a proxy for customer preference
