Events

"Leveraging the Power of Images in Predicting Product Return Rates." Dzyabura, Daria, Siham El Kihal, John R. Hauser, and Marat Ibragimov, forthcoming Marketing Science, 2023.

"Product Aesthetic Design: A Machine Learning Augmentation." Burnap, Alex, John R. Hauser, and Artem Timoshenko, forthcoming Marketing Science, 2023.

"Artificial Intelligence and User-generated Data are Transforming how Firms come to Understand Customer Needs." Hauser, John R., Chengfeng Mao,and James Li forthcoming Review of Marketing Research, K. Sudhir and Olivier Toubia, eds. (2022)

"Is Deep Learning a Game Changer for Market Analytics?" Urban, Glen, Artem Timoshenko, Paramveer Dhillon, and John R. Hauser. MIT Sloan Management Review Vol. 61, No. 2 (2020): 71-76. Download Paper.

"The Strategic Implications of Scale in Choice-Based Conjoint Analysis." Hauser, John R., Felix Eggers, and Matthew Selove. Marketing Science Vol. 38, No. 6 (2019): 1059-1081. Appendices.

"Recommending Products When Consumers Learn their Preference Weights." Dzyabura, Daria and John R. Hauser. Marketing Science Vol. 38, No. 3 (2019): 417-441. Download Paper.

"Identifying Customer Needs from User Generated Content." Timoshenko, Artem and John R. Hauser. Marketing Science Vol. 38, No. 1 (2019): 1-20. Download Paper.

Consumer decision measurement: conjoint analysis, non-compensatory methods, adaptive methods, machine learning methods, strategic importance of accuracy.
Product forecasting: information acceleration, really-new products, incentive-aligned games.
Consumer behavior: cognitive simplicity in decision making and in dynamic models, theory-based models, vivid stimuli.
Morphing: website, banner, product assortment.
Voice of the customer methods, defensive and competitive strategy, new product development, experimental and quasi-experimental methods.
Machine learning applications: customer needs from UGC, recommendation systems based on preference learning, aesthetics in product design, the identification of new design gaps.

John R. Hauser