Cluster-based visualisation of marketing data

Abstract

Marketing data analysis typically aims to gain insights for targeted promotions or, increasingly, to implement collaborative filtering. Ideally, data would be visualised directly. There is a scarcity of methods to visualise the position of individual data points in clusters, mainly because dimensionality reduction is necessary for analysis of high-dimensional data and projective methods tend to merge clusters together. This paper proposes a cluster-based projective method to represent cluster membership, which shows good cluster separation and retains linear relationships in the data. This method is practical for the analysis of large, high-dimensional, databases, with generic applicability beyond marketing studies. Theoretical properties of this non-orthogonal projection are derived and its practical value is demonstrated on real-world data from a web-based retailer, benchmarking with the visualisation of clusters using Sammon and Kohonen maps. © Springer-Verlag Berlin Heidelberg 2004.