Geo-Marketing Segmentation with Deep Learning

Abstract

Spatial clustering is a fundamental instrument in modern geo-marketing. The complexity of handling of high-dimensional and geo-referenced data in the context of distribution networks imposes important challenges for marketers to catch the right customer segments with useful pattern similarities. The increasing availability of the geo-referenced data also places more pressure on the existing geo-marketing methods and makes it more difficult to detect hidden or non-linear relationships between the variables. In recent years, artificial neural networks have been established in different disciplines such as engineering, medical diagnosis, or finance, to solve complex problems due to their high performance and accuracy. The purpose of this paper is to perform a market segmentation by using unsupervised deep learning with self-organizing maps in the B2B industrial automation market across the United States. The results of this study demonstrate a high clustering performance (4 × 4 neurons) as well as a significant dimensionality reduction by using self-organizing maps. The high level of visualization of the maps out of the initially unorganized data set allows a comprehensive interpretation of the different clusters and patterns across space. The centroids of the clusters have been identified as footprints for assigning new marketing channels to ensure a better market coverage.