An Application of Self-Organising and Backpropagation Neural Networks for Predicting Segment Classification

Inadequate market segmentation and clustering problems could cause an enterprise to either miss a strategic marketing opportunity or not cash-in on a tactical campaign. The need for in-depth knowledge of customer segments and the need to overcome the limitations of using linear techniques to analyse non-linear problems requires a re-assessment of generally used approaches. The objectives of the research are (1) to consider the use of self-organising (SOM) neural networks for segmenting customer markets and (2) to analyse the predictive ability of backpropagation (BP) neural networks for classifying new customers by using the output provided by SOM neural networks. The nature and scope of neural networks are considered and the conceptual differences between Cluster Analysis and SOM neural networks as well as BP neural networks and multiple linear regression (MLR) static filter model are highlighted. The findings of the SOM neural network modelling indicate four natural clusters. In addition, the predictive ability of the BP neural network model was superior to that of MLR. Additional knowledge was also extracted from the BP neural network model by analysing the relationship between the input variables and each segment by means of input-output analysis. Sensitivity analysis was also used to identify important variables for each segment. Input-output analysis is also used to compile broad profiles of differences between the segments. The BP neural network model developed for this application is also suitable for deployment (i.e. classification of “new” customers) with a high level of confidence.