Eliciting Consumer Preferences Using Robust Adaptive Choice Questionnaires
Abernethy, Jacob
Evgeniou, Theodoros
Toubia, Olivier
Vert, Jean-Philippe
This paper appears in: Knowledge and Data Engineering, IEEE Transactions on
Publication Date: Feb. 2008
Volume: 20,
Issue: 2
On page(s): 145-155
Location: Los Angeles, CA, USA,
ISSN: 1041-4347
Digital Object Identifier: 10.1109/TKDE.2007.190632
Current Version Published: 2007-12-26
Abstract
We propose a framework for designing adaptive choice-based conjoint questionnaires that are robust to response error. It is developed based on a combination of experimental design and statistical learning theory principles. We implement and test a specific case of this framework using Regularization Networks. We also formalize within this framework the polyhedral methods recently proposed in marketing. We use simulations as well as an online market research experiment with 500 participants to compare the proposed method to benchmark methods. Both experiments show that the proposed adaptive questionnaires outperform existing ones in most cases. This work also indicates the potential of using machine learning methods in marketing.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
