Geo-Marketing Segmentation with Deep Learning

Oussama Benbrahim Ansari
Additional contact information
Oussama Benbrahim Ansari: Triagon Academy Munich, School of Business and Law, Steinheilstrasse 5, 85737 Ismaning, Germany

Businesses, 2021, vol. 1, issue 1, 1-21

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.

Keywords: spatial clustering; market segmentation; artificial neural networks; deep learning; self-organizing maps; channel management (search for similar items in EconPapers)
JEL-codes: A1 D0 D4 D6 D7 D8 D9 E0 E2 E3 E4 E5 E6 E7 F0 F2 F3 F4 F5 F6 G0 G1 G2 H0 J0 K2 L0 L1 L2 M0 M1 M2 M3 M4 M5 N0 N1 N2 O0 O1 P0 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2673-7116/1/1/5/pdf (application/pdf)
https://www.mdpi.com/2673-7116/1/1/5/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jbusin:v:1:y:2021:i:1:p:5-71:d:575806

Access Statistics for this article

Businesses is currently edited by Dr. Patrick Han

More articles in Businesses from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().