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.

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“…However, they have the disadvantage of missing complex outliers and being sensitive to parameters. Dimensionality reduction algorithms include PCA [22,23], t-SNE [43], Autoencoders [19], UMAP [44,57], and MiniSom [45,58,59], which provide advantages such as reduced data complexity, ease of visualization, and capturing nonlinear relationships. However, they have disadvantages such as high computational intensity and the need to select appropriate dimensions.…”

“…They have disadvantages such as the amount of Table 2 shows the advantages or disadvantages of unsupervised learning algorithms. Clustering algorithms consist of k-means [19,[31][32][33][34][35][36][37][38][39][40][41][42][43][44][45], DBSCAN [21], hierarchical clustering [46][47][48], and spectral clustering [49][50][51], and provide a simple structure, widely used methodology, and diversity according to various needs. However, they have disadvantages, such as spherical cluster assumption, sensitivity to scale, and difficulty processing complex data structures.…”

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“…Respond to reviews promptly and professionally. Implement schema markup to provide search engines with structured data about your business, helping it appear in local search results [39,40].…”

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“…They are able to shed light on the diversity and inequalities in different domains and areas, such as education inequality in Beijing (Xiang et al, 2018 ), and in UK higher education (Singleton & Longley, 2009 ). Elsewhere, Benbrahim Ansari ( 2021 ) utilised unsupervised deep learning with self-organising maps for creating a geo-marketing segmentation for Business to Business industrial automation market, it allows users to better identify market demands. A local fuzzy geographically weighted clustering model was applied in the work of Grekousis ( 2021 ) to generate the geo-segments of cancer incident.…”