Forecasting performance of market share models: an assessment, additional insights, and guidelines

Abstract

The research provides an assessment of the relevant literature on market share models and identifies the need for further research. Additional insights are generated in this study by using point-of-sale scanners for evaluating the forecasting performance of market share models under various conditions. Specifically, weekly store-level scanner data for four frequently purchased product categories-saltine crackers, baking chips, diapers and toilet tissue, and simulated data are used in this study. Consistent with theoretical expectations, the attraction models estimated by GLS produce the best forecasts even (1) at the brand level, and (2) when competitors' actions are predicted. However, the superiority of the attraction models is diminished when systematic errors are introduced to the values of the competitors' predictor variables in the holdout sample. In fact, naive models outperform all types of econometric models when large errors are present in the competitors' predictor variables, and among the econometric models, linear models produce better forecasts than attraction models. The need for estimating the models with GLS (as opposed to OLS) with the use of cross-sectional time-series data is also illustrated. Finally, guidelines are developed for practitioners and researchers on the usefulness of market share models for forecasting.