{'fraction_non_alphanumeric': 0.015779092702169626, 'fraction_numerical': 0.0, 'mean_word_length': 5.590909090909091, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Micro-Market Segmentation Using a Neural Network Model Approach

Abstract Market segmentation is one of the fundamental conceptual frameworks of the modern marketing theory and practice. Over time, several methods of market segmentation were proposed in the rich literature on the subject. In the past several years, a cluster analysis method was used to segment markets on various consumer behavioral bases. However, the more recent paradigm shift in marketing in favor of relationship marketing, growing importance of global marketing in diverse countries and focus on micro-market segments calls for better methodological tools for micro-market segmentation than the traditional parametric multivariate approaches with their methodological and practical limitations. In this context, in this paper it is proposed that a neural network model can be a viable alternative for micro-market segmentation. The neural network model applicability to micro-market segmentation was demonstrated with an empirical example.