A DSS-Based Dynamic Programming for Finding Optimal Markets Using Neural Networks and Pricing

One of the substantial challenges in marketing efforts is determining optimal markets, specifically in market segmentation. The problem is more controversial in electronic commerce and electronic marketing. Consumer behaviour is influenced by different factors and thus varies in different time periods. These dynamic impacts lead to the uncertain behaviour of consumers and therefore harden the target market determination. Real time decision making is a crucial task for obtaining competitive advantage. Decision Support Systems (DSSs) can be an appropriate process for taking real time decisions. DSSs are classified as information system based computational systems helping in decision making supporting business decision making and facilitate data collection and processing within market analysis. In this paper, different markets exist that are supplied by a producer. The producers need to find out which markets provide more profits for more marketing focuses. All consumers’ transactions are recorded in databases as unstructured data. Then, neural network is employed for large amount of data processing. Outputs are inserted to an economic producer behaviour mathematical model and integrated with a proposed dynamic program to find the optimal chain of markets. The sensitivity analysis is performed using pricing concept. The applicability of the model is illustrated in a numerical example.

Keywords:

Information Technology (IT) , Decision Support Systems (DSS) , Perceptron Neural Network , Dynamic programming (DP)