Forecasting and analysis of marketing data using neural networks

This study aims to incorporate Artificial Neural Networks into a Marketing Decision Support System (MDSS), specifically, by discovering important variables that influence sales performance of colour television (CTV) sets in the Singapore market using neural networks. Three kinds of variables, expert knowledge, marketing information and environmental data, are examined. The information about the effects of each of these variables has been studied and made available for decision making. However, their combined effect is unknown. This study attempts to explore the combined effect for the benefit of our collaborator, a multinational corporation (MNC) in the consumer electronics industry in Singapore. Putting these three variables together as input variables results in a neural network model. Neural network training is conducted using historical data on CTV sales in Singapore collected over the past one and a half years. Sensitivity analysis is then performed to reduce input variables of neural networks. This is done by analyzing the weights of the input node connections in the trained neural networks using two different methods. The weaker variables can be excluded, and this results in a simpler model. Further, an R-Square value of almost 1 is obtained through the inclusion of an Unknown variable when the network model consisting only of the most influential variables is trained and tested. Knowing the most influential variables, which in this case include Average Price, Screen Size, Stereo Systems, Flat-Square screen type and Seasonal Factors, marketing managers can improve sales performance by paying more attention to them.