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.
Abernethy, Jacob, Theodoros Evgeniou, Olivier Toubia, and Jean-Philippe Vert. "Eliciting Consumer Preferences using Robust Adaptive Choice Questionnaires." IEEE Transactions on Knowledge and Data Engineering 20, no. 2 (February 2008): 145-155.
Each author name for a Columbia Business School faculty member is linked to a faculty research page, which lists additional publications by that faculty member.
Each topic is linked to an index of publications on that topic.