International Segmentation of Countries Using Unsupervised Machine Learning Algorithms

Abstract

As markets grow more global, global market segmentation becomes increasingly important for creating, marketing, and selling items globally. Operating a business in many nations introduces unique obstacles. International markets are more diverse than domestic markets due to differences in historical, institutional, cultural, and political environments among nations. Clustering algorithms enable multinational companies to give better-personalized products and customer services to their foreign customers. By gaining a deeper understanding of the nation’s characteristics, companies may identify the appropriate products or services to distribute, choose the best marketing channels for the target consumers, discover new and important insights, and launch new business strategies. This study used unsupervised machine learning algorithms such as Affinity Propagation, DBSCAN, and Hierarchical clustering to segment 150 countries. The segments of countries were established based on their brand awareness, gross national income, and trade liberalization, which are considered to be some of the most relevant qualities to employ when determining the macro segmentation of countries in the context of international business. This research emphasizes and recommends that various machine learning technologies be used to construct segmented and countrywide business strategies and marketing tactics in order to advance the global market expansion.