ConjointAnalyse

Conjointanalyse, or conjoint analysis, is a family of statistical techniques used in market research to quantify how consumers value different features that make up a product or service. Respondents evaluate or choose among profiles described by multiple attributes and levels, such as price, performance, size, and brand. By analyzing these choices, the method estimates the relative value (utilities) of attribute levels and the overall trade-offs consumers are willing to make.

There are several approaches within conjointanalyse. Traditional full-profile conjoint presents many attributes at once, often using rating, ranking, or choice tasks. Choice-based conjoint (CBC), also known as discrete choice experiments, asks respondents to choose preferred options from sets. Adaptive conjoint analysis and related methods adjust tasks based on prior responses to improve efficiency. Estimation typically uses models such as multinomial logit or hierarchical Bayes to derive part-worth utilities and attribute importance.

The outputs of conjointanalyse include part-worth utilities for each attribute level, the relative importance of attributes, and simulated market shares for hypothetical product configurations. These results support product design decisions, pricing strategies,Portfolio optimization, and market segmentation by revealing which features drive choices.

Design considerations are essential: careful selection of relevant attributes and levels, efficient experimental design (often fractional factorial or D-efficient designs), and awareness of potential biases. Limitations include hypothetical bias, complexity in design and interpretation, and model assumptions about substitution among attributes. Conjointanalyse remains widely used for forecasting demand and testing new product concepts across industries.