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. The objective of conjoint analysis is to determine the influence of a set of attributes on respondent choice or decision making.

Conjoint analysis is a popular method of product and pricing research that uncovers consumers’ preferences, which is useful when a company wants to: Select product features. Assess consumers’ sensitivity to price changes. Forecast its volumes and market share. Predict adoption of new products or services.

2020年12月18日 · Conjoint analysis is a form of statistical analysis that firms use in market research to understand how customers value different components or features of their products or services. It’s based on the principle that any product can be broken down into a set of attributes that ultimately impact users’ perceived value of an item or service.

2015年6月23日 · A conjoint analysis extends multiple regression analysis and puts the ranking front and center for the participant. It helps identify the optimal combination of features in a product or service.

Definition: Conjoint analysis is a survey-based research technique used to quantify how people value the individual features of a product or service. A conjoint survey question shows respondents a set of concepts, asking them to choose or rank the most appealing ones.

Conjoint analysis, a statistical technique created by mathematical psychologists from 1970 [1], is used to describe a broad range of techniques to estimate how much people value certain attributes and features of a new product or service.

Conjoint analysis provides a number of outputs for analysis including: part worth utilities (or counts), importances, shares of preference and purchase likelihood simulations. This article discusses these measures and gives guidelines for interpreting results and presenting findings to …

Conjoint analysis provides various outputs for analysis, including part-worth util-ities, counts, importances, and shares-of-preference simulations. This chapter dis-cusses these measures and gives guidelines for interpreting results and presenting findings to management.

This chapter describes conjoint analysis and provides examples using SAS. Topics include metric and non-metric conjoint analysis, efficient experimental design, data collection and manipulation, holdouts, brand by price interactions, maximum utility …

Conjoint Analysis is used to determine partial utilities (“partworths”) β for all factor values based upon the ranked data Furthermore, with this partworths it is possible to compute the metric total utilities y of all incentives and the relative importance of the single object attributes