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.
ML Projects
MLOps with SAS Enterprise Miner
Customer Segmentation ML App using PyCaret and Streamlit
Object Detection and Classification
Build and Deploy a Customer Churn Model
Recommender System with Deep Collaborative Filtering
Generative Adversarial Networks (GANs)
Use of Multi-modal Prompts in Marketing
Fine-tuning Open LLMs
