Global Engineering Solutions Review is an international peer-reviewed journal targeting to broadcast innovative research and ideas in engineering fields. Stressing the creation and application of new engineering solutions to global issues, it is one of the most important divisions. The journal welcomes the submissions that present efficient, repeatable, and easy to implement solutions to present day challenges in any branch of engineering; civil, mechanical, electrical, environmental, and industrial engineering to mention but a few.
This study explains the limits of applying traditional B2B market segmentation strategies that tend to rely on the firmographic variables and most of the times do not pay enough attention to the intricate decision-making process in organizations. Corporate culture together with the decision-makers' psychographic traits prove vital in shaping B2B purchasing choices according to the study's hypothesis. The main purpose of this study was to understand and define significant psychographic variables that have predictive value in buying choices. The study established a machine learning segmentation system that merges psychographic types and buyer behaviors for creating precise marketing segments which companies can act upon. Research tested this proposed psychographic segmentation method against traditional firmographic segmentation strategies to produce evidence about superior B2B customer targeting strategies. In this study the researcher attempts to extend beyond firmographic views seconded by conventional B2B customer engagement by employing insights of AI to better understand B2B customer engagement.
B2B Market Segmentation, Psychographics, Behavioral Segmentation, Machine Learning, Corporate Culture, Decision-Making.