Forecasting market share using predicted values of competitive behavior: further empirical results

Abstract

Forecasting is an important marketing activity for evaluating the expected performance of alternative marketing plans, especially in order to predict earnings, sales or market shares. The purpose of this paper is fourfold. Firstly, we develop and evaluate alternative econometric approaches to predict competitors’ future actions. Secondly, the forecasting performance of attraction models is compared to those of linear and multiplicative market share models not only if competitors’ actions are known a priori but also if competitors’ actions are forecasts. Thirdly, the effects of alternative structural specifications of attraction models on the forecasting accuracy are investigated. Finally, we reinvestigate the impact of OLS estimation versus GLS estimation on the forecasting performance. The adopted empirical methods account for the interdependence of marketing instruments. We also allow for competitive reactions up to 10 periods ago and introduce a new approach concentrating on so-called marketing events characterizing directly the contemporaneous choice of several promotional activities within a brand. Analyzing weekly scanner data from three markets we find that attraction models outperform the share predictions of the linear and multiplicative models even if competitors’ actions are forecast. This result is valid on the market and brand level. In addition, response models outperform the naive model on the market level irrespective of whether competitors’ actions are known a priori or if they are forecasts. On the brand level the superiority of response models over naive models diminishes though it still exists. With respect to the best method of predicting competitors’ actions it turns out that parsimonious specifications like autoregressive price predictions or binary logit models perform conveniently.