Hierarchical Signal Propagation for Household Level Sales in Bayesian Dynamic Models

Large consumer sales companies frequently face challenges in customizing decision making for each individual customer or household. This dissertation presents a novel, efficient and interpretable approach to such personalized business strategies, involving multi-scale dynamic modeling, Bayesian decision analysis and detailed application in the context of supermarket promotion decisions and sales forecasting.

We use a hierarchical, sequential, probabilistic and computationally efficient Bayesian dynamic modeling framework to propagate signals down the hierarchy, from the level of overall supermarket sales in a store, to items sold in a department of the store, within refined categories in a department, and then to the finest level of individual items on sale. Scalability is achieved by extending the decouple-recouple concept: the core example involves 162,319 time series over a span of 112 weeks, arising from combinations of 211 items and 2,000 households. In addition to novel dynamic model developments and application in this multi-scale framework, this thesis also develops a comprehensive customer labeling system, built based on customer purchasing behavior in the context of prices and discounts offered by the store. This labeling system addresses a main goal in the applied context to define customer categorization to aid in business decision making beyond the currently adopted models. Further, a key and complementary contribution of the thesis is development of Bayesian decision analysis using a set of loss functions that suit the context of the price discount selection for supermarket promotions. Formal decision analysis is explored both theoretically and via simulations. Finally, some of the modeling developments in the multi-scale framework are of general interest beyond the specific applied motivating context here, and are incorporated into the latest version of PyBATS, a Python package for Bayesian time series analysis and forecasting.