Consumer Search and Dynamic Preference: A Deep Structural Econometric Model
Abstract:
Modeling and capitalizing on consumers' dynamic preferences presents significant business potential. Deep learning methods empirically promise us advantageous capabilities in dealing with manifold consumer data to predict their future actions, but these opaque predictive approaches don't explicitly model consumers' decision-making processes, making them difficult to interpret. On the other hand, the economic theory of sequential search suggests that consumers adopt a sequential search strategy when looking for the best product to purchase, which involves searching through a series of alternatives until they find the best option that meets their preferences. Based on these two pillars, we propose a theory-driven deep learning model called Consumer Preference Transformer (CPT), which leverages the deep learning model to learn dynamic consumer preferences and sequential search theory to model consumers' search and purchase decisions. CPT integrates these two building blocks into a unified model that can be estimated via end-to-end learning. Diverging from conventional deep learning, we incorporate economic theory that explicitly models the consumer's decision, opening up the black box of the model and providing reasonable interpretations of the formation process of dynamic consumer preferences. Empirical evaluations demonstrate the superiority of our proposed method over state-of-the-art deep learning and structural econometric models in predicting consumer click and purchase actions. The deep structural econometric model additionally allows for the assessment of various intervention policies. Policy experiments reveal that implementing CPT's product and attribute recommendation policies enhances product recommendations and new product promotion strategies, promising improved user experiences and the potential for heightened business revenues.
Bio:
Dr. Song's research focus on personalization technology helps users navigate an overwhelming amount of information, enhances their satisfaction, and increases revenue through personalized experiences. The key theme throughout his work is the development of personalization approaches by developing advanced machine learning and structural economic modeling techniques. Dr. Song has published/accepted papers in top IS and CS journals including Management Science, ISR, Informs Journal on Computing, ACM Transactions on Intelligent Systems and Technology, and IEEE Transactions on Multimedia. He won the Best Paper Award at the 2018 INFORMS Conference on Information Systems and Technology (CIST 2018), the Best Paper Award at the 2014 Workshop on Information Technologies and Systems (WITS 2014), and runner up of the Best Paper Award at the 2023 Conference on Information Systems and Technology (CIST 2023).