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Conjoint analysis is a survey-based statistical technique used in market research that helps determine how people value different attributes (feature, function, benefits) that make up an individual product or service.
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conjoint analysis attributes used research choice product may respondents levels market set marketing design preferences profiles attribute determine number different
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Conjoint analysis | is a | survey-based statistical technique used in market research that helps determine how people value different attributes | 0.90 | text |
| best | instance of | who invented and developed choice-based approaches to conjoint analysis and related techniques | 0.80 | text |
| AHP | instance of | and mathematical approaches | 0.80 | text |
| PAPRIKA | instance of | and mathematical approaches | 0.80 | text |
| evolutionary algorithms or rule-developing experimentation | instance of | and mathematical approaches | 0.80 | text |
| volumetric conjoint analysis may remedy this Advantagesestimates psychological tradeoffs that consumers make when evaluating several attributes togethercan measure preferences at the individual leveluncovers real or hidden drivers which may not be apparent to respondents themselvesmimics realistic choice or shopping taskable to use physical objectsif appropriately designed | instance of | but weighting respondents by their self-reported purchase volume or extensions | 0.80 | text |
| can model interactions between attributesmay be used to develop needs-based segmentation | instance of | but weighting respondents by their self-reported purchase volume or extensions | 0.80 | text |
| when applying models that recognize respondent heterogeneity of tastes Disadvantagesdesigning conjoint studies can be complexwhen facing too many product features | instance of | but weighting respondents by their self-reported purchase volume or extensions | 0.80 | text |
| product profiles | instance of | but weighting respondents by their self-reported purchase volume or extensions | 0.80 | text |
| respondents often 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 featuresrespondents are unable to articulate attitudes toward new categories | instance of | but weighting respondents by their self-reported purchase volume or extensions | 0.80 | text |
| or may feel forced to think about issues they would otherwise not give much thought topoorly designed studies may over-value emotionally-laden product features | instance of | but weighting respondents by their self-reported purchase volume or extensions | 0.80 | text |
| undervalue concrete featuresdoes not take into account the quantity of products purchased per respondent | instance of | but weighting respondents by their self-reported purchase volume or extensions | 0.80 | text |
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