This study explores the use of rough-set methods for marketing decision support systems in the retail business. A tutorial presentation of Rough Set Data Analysis (RSDA) in the context of knowledge discovery from time series databases is given. We show how an RSDA model can be used to develop a marketing decision support system which can capture the complex relationships between marketing factors, such as advertising and promotion, and the total impact on sales levels in order to find influential advertising strategies. This information is used by the business manager to make faster and better strategy decisions for the business to survive in the rapidly changing and competitive environments. The data set used for RSDA application example contains weekly investments in different media categories: TV, radio, cinema, morning press, evening press, popular press, special interest press, and outdoor posters; for seven makes of cars in the Swedish market.
Advertising, strategy, decision-making, forecasting, promotion, sales forecasting, time series, knowledge discovery, rough sets, decisions under uncertainty, marketing decision support system
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