Category management—a relatively new function in marketing—involves large‐scale, real‐time forecasting of multiple data series in complex environments. In this paper, we illustrate how Bayesian Vector Auto regression (BVAR) fulfils the category manager's decision‐support requirements by providing accurate forecasts of a category's state variables (prices, volumes and advertising levels), incorporating management interventions (merchandising events such as end‐aisle displays), and revealing competitive dynamics through impulse response analyses. Using 124 weeks of point‐of‐sale scanner data comprising 31 variables for four brands, we compare the out‐of‐sample forecasts from BVAR to forecasts from exponential smoothing, univariate and multivariate Box‐Jenkins transfer function analyses, and multivariate ARMA models. Theil U's indicate that BVAR forecasts are superior to those from alternate approaches. In large‐scale forecasting applications, BVAR's ease of identification and parsimonious use of degrees of freedom are particularly valuable.
All Science Journal Classification (ASJC) codes
- Modeling and Simulation
- Computer Science Applications
- Strategy and Management
- Statistics, Probability and Uncertainty
- Management Science and Operations Research