"Leveraging the Power of Images in Predicting Product Return Rates." Dzyabura, Daria, Siham El Kihal, John R. Hauser, and Marat Ibragimov, MIT Sloan Working Paper 5856-19. Cambridge, MA: MIT Sloan School of Management, September 2022.
"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)
"Product Aesthetic Design: A Machine Learning Augmentation." Burnap, Alex, John R. Hauser, and Artem Timoshenko, MIT Sloan Working Paper 5814-19. Cambridge, MA: MIT Sloan School of Management, October 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.
- Marketing Management
- New Product and Service Development
- Competitive Marketing Strategy
- Marketing Models
- Measurement and Marketing Research
- Research Methodology
- Marketing Analytics
- 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.