JL

I am a Professor of Data Analytics and Marketing at the College of Business, University of La Verne.
My research focuses on the development and application of quantitative methods in marketing, including consumer choice, market segmentation, prelaunch forecasting, and predictive marketing analytics.
My expertise in data analysis and machine learning encompasses a wide range of skills and knowledge in deep learning techniques and AI principles for the development of complex predictive models.

Jonathan Lee

Education

  
    PhD
    
    Marketing
    University of Pittsburgh
  
    MS
    
    Economics
    Yale University
  
    BA
    
    Economics
    Sogang University

Research

My research focuses on the development and application of quantitative methods in marketing, which includes the different aspects of consumer choice modeling, market segmentation, prelaunch forecasting, structural causal modeling, social network analysis, and predictive marketing analytics. Managerial applications focus on marketing performance analytics, customer relationship management, and prelaunch forecasting based on a hierarchy-of-effects model for new products. I have consulted several companies in different industries, including telecom, airline, department store, and pharmaceutics, and successfully addressed the problems in managing reward programs, web diagnostics, and new product forecasting. I have published in Management Science, Advances in Consumer Research, Marketing Science, Journal of Consumer Research, Psychometrika, and Journal of the Academy of Marketing Science. ResearchGate statistics show that my publications reached over 1,300 citations and 30,000 reads.

My current research focuses on personalized customer journeys built on a foundation of explainable AI, which envisions AI tailoring interactions to each individual's unique needs, motivations, and context. The topics include (i) Dynamic Customer Profiling, (ii) Contextual and Emotion-Aware AI, and (iii) Explainable Recommenders to design explainable recommendation systems that not only present personalized suggestions but also clearly explain the reasoning behind them.