Clustering Marketing Data Using Nonparametric Bayesian Methods
Wednesday, June 3, 2020
Which customers are predicted to spend the most in the next year? Which should I select for retargeting? How often should different customers hear from my brand? Answering these requires segmenting customers but instead of applying ad hoc rules, clustering algorithms can reveal the hidden structure in data to group them on their similarity. Dirichlet Process Mixture Modeling (DPMM) is a nonparametric Bayesian method that also infers the optimal number of clusters. Raghav gives a high-level tour of Bayesian methods and DPMM followed by results on customer transaction data. Walk away with a better understanding of nonparametric Bayesian modeling and a greater appreciation for how flexible modeling with these approaches can help uncover the hidden structure in marketing datasets.