Abstract:

In this paper, a Decison Support System is presented that enables the decision-maker to make optimal use of existing marketing research data in the area of fast-moving consumer goods. Many valuable market data are not analyzed in depth by the decision-maker, who, therefore, do not get all the possible information out of the research budget. "SalesPlan" is a model that combines information that is already available by most brand producers. The main data of the analysis are time-series data of Category Sales, Brand Sales and Market Share, and corresponding values of the brand price, average competitor price, company and competitors’ distribution, amount of company and competitors’ in-store promotion, and data of company and competitors’ retail inventory. All these data are available from Nielsen-Data, which most brand producers buy regularly, typically on a bi-monthly basis. Also, the analysis is based on GRP- and Adstock data of company and competitors' advertising efforts. These data are also available on commercial basis in most product areas. Finally, the decision-support part of the model uses company-internal data of brand production costs. Since data are time series, "SalesPlan" models trend and seasonal patterns if they exist. "SalesPlan" consists of two separate modules: A forecasting module and a decision support module. In the Forecasting Module econometric models of Category Sales, Brand Sales and Market share are estimated as functions of the set of explanatory variables. The ability of the models to describe the historical data provide valuable insight into the relative importance of the marketing decision variables and are basis for the decision support model. Based on forecasts of the important competitors' decision variables and the company's own marketing plans, short term forecasts of Company Sales, Brand Sales, and Market Share are developed.