Author Description

The global economic crisis highlights how marketing forecasts are, most of the times, inaccurate and how companies' survival is safeguarded by a systematic knowledge of the market players' behaviors. The main goal for organizations to be competitive in the market is to increase the forecast accuracy and reduce the gap between actual versus expected results. Appropriate data mining models are one of the best supporting approaches to make different marketing decisions for decision makers. Analyzing and understanding customers' behavior in advance represents the fundamentals for the development of winning marketing strategies. This research shows the strategic magnitude of how predictive data mining modeling tools uncover the precious hidden knowledge stored in large databases, through a structured marketing performance forecasting process in today's competitive landscape.