Segmentation via Principal Component Analysis for Perceptron Classification

Abstract

In today’s competitive environment, companies must identify their most profitable customer groups and the groups that have the biggest potential to become as such. By identifying these critical groups, they can target their actions, such as launching tailored products and target one-to-one marketing to meet customer expectations. With the profound advancements in clustering algorithms, segmentation has emerged as the method of choice for isolating the various groups of interest. However, the quality of segments of the groups of interest is affected by the type of input data to the clustering algorithms resulting from high dimensionality. In this study, Principal Component Analysis was usedto solve the high dimensionality of data problem in the the subscriber data. Principal component analysis was used to reduce the nine variables in the subscriber data to five. The factored data was then used to cluster the various customers into segments. The elbow criterion was used to determine the optimum number of clusters. The data was then clustered via several methods; K-means, Fuzzy C-means , Partioning About MediodsPAM, and Hierarchal Clustering. Results showed that k-means was not just the simplest method but also performed best with dimensionally reduced data. By using real case data, the study was able to verify that dimensional reduction can be applied before clustering algorithms. The dimension reduction of telecom data can thus be solved via Principal Component Analysis. The study was extended to include the classification of new subscribers basing on the dimensionally reduced data. For that purpose, a perceptron neural network was developed. Using the k-means clusters as targets, a perceptron capable of classifying was created and validated. The perceptron was able to classify new subscribers with acceptable accuracy. The perceptron model was validated and found to be accurate with an R2 of 0.9999, Root Mean Square Error of 0.01813, Sum of Square Error of 1.0947 in all the data. Dimension reduction via Principal Component Analysis can, therefore, be used to achieve the segmentation of existing customers. The use of a perceptron is also important for automating the process of customer classification. Companies can therefore easily identify profitable customers from both old and new customers.