Measuring Consumer Preferences Using Conjoint Poker

From Consumer Preferences Towards Buying Decisions - Conjoint Analysis as Preference Measuring Method in Product Recommender Systems

This paper briefly introduces the conjoint analysis as a method to measure consumer preferences. Based on the introduction the conjoint analysis is suggested as preference measuring method in product recommender systems. The challenges and limits in applying the conjoint analysis to product recommender systems are analysed and discussed. In the end we present a set of adaptations to the traditi...

Jenaer Schriften zur Wirtschaftswissenschaft A Comparative Empirical Study on Common Methods for Measuring Preferences

Literature and practice reveal that most marketing related questionnaires measuring consumer preferences rely on some kind of conjoint analysis. Recent studies show the analytic hierarchy process to be suitable for this task, too. This paper gives a comparison of the approaches and the results of former studies. Because we found considerable differences in those results, an additional study has...

An Empirical Comparison of CBC and AHP for Measuring Consumer Preferences

Conjoint Analysis (CA) is a very popular class of methods for measuring consumer preferences, both in research and practice. However, since a couple of years, the Analytic Hierarchy Process (AHP) is being discussed in this field as well. Several empirical studies have shown the general potential of AHP, particularly in complex product evaluation tasks consisting of many attributes. But, in spit...

Evaluating Consumer Preferences for Medicare Part D Using Conjoint Analysis

A Machine Learning Approach to Conjoint Analysis

Choice-based conjoint analysis builds models of consumer preferences over products with answers gathered in questionnaires. Our main goal is to bring tools from the machine learning community to solve this problem more efficiently. Thus, we propose two algorithms to quickly and accurately estimate consumer preferences.