Exploring artificial intelligence-based data fusion for conjoint analysis

Conjoint analysis is used to understand how consumers develop preferences for products or services, which encompass, as usual, multi-attributes and multi-attribute levels. Conjoint analysis has been one of the popular tools for multi-attribute decision-making problems on products and services for consumers over the last 30 years. It has also been used to market segmentation and optimal product positioning. In spite of its popularity and commercial success, a major weakness of conjoint analysis has been pointed such that respondents participating in conjoint experiment have to evaluate a number of hypothetical product profiles. To reduce the number of hypothetical products, this paper proposes a systematic method, called data fusion, and explores the usability of various data fusion techniques. The paper evaluates traditional data fusion (correlation-based), hierarchical Bayesian-based data fusion, and neural network-based data fusion.