  We present a case study and methodological developments in large-scale
hierarchical dynamic modeling for personalized prediction in commerce. The
context is supermarket sales, where improved forecasting of
customer/household-specific purchasing behavior informs decisions about
personalized pricing and promotions on a continuing basis. This is a big data,
big modeling and forecasting setting involving many thousands of customers and
items on sale, requiring sequential analysis, addressing information flows at
multiple levels over time, and with heterogeneity of customer profiles and item
categories. Models developed are fully Bayesian, interpretable and multi-scale,
with hierarchical forms overlaid on the inherent structure of the retail
setting. Customer behavior is modeled at several levels of aggregation, and
information flows from aggregate to individual levels. Forecasting at an
individual household level infers price sensitivity to inform personalized
pricing and promotion decisions. Methodological innovations include extensions
of Bayesian dynamic mixture models, their integration into multi-scale systems,
and forecast evaluation with context-specific metrics. The use of simultaneous
predictors from multiple hierarchical levels improves forecasts at the
customer-item level of main interest. This is evidenced across many different
households and items, indicating the utility of the modeling framework for this
and other individualized forecasting applications.
