Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue

TL;DR

This paper presents a multivariate Bayesian dynamic modeling approach for 12-week ahead revenue forecasting in large retail chains, leveraging hierarchical structures and multi-scale information to improve accuracy and analyze cross-category dependencies.

Contribution

Introduces a hierarchical multivariate Bayesian dynamic model tailored for large-scale retail revenue forecasting, enabling integrated analysis and scalable computation.

Findings

Multivariate models improve forecast accuracy for key product categories.

Multi-scale information enhances predictions related to pricing and promotions.

Cross-category dependencies inform strategic promotion decisions.

Abstract

Forecasting enterprise-wide revenue is critical to many companies and presents several challenges and opportunities for significant business impact. This case study is based on model developments to address these challenges for forecasting in a large-scale retail company. Focused on multivariate revenue forecasting across collections of supermarkets and product Categories, hierarchical dynamic models are natural: these are able to couple revenue streams in an integrated forecasting model, while allowing conditional decoupling to enable relevant and sensitive analysis together with scalable computation. Structured models exploit multi-scale modeling to cascade information on price and promotion activities as predictors relevant across Categories and groups of stores. With a context-relevant focus on forecasting revenue 12 weeks ahead, the study highlights product Categories that benefit from multi-scale information, defines insights into when, how and why multivariate models improve forecast accuracy, and shows how cross-Category dependencies can relate to promotion decisions in one Category impacting others. Bayesian modeling developments underlying the case study are accessible in custom code for interested readers.