Regional Analysis of New Energy Vehicle Consumer Preferences Based on Sales Data Mining

Abstract

The rapid expansion of new energy vehicle (NEV) markets has created significant regional variations in consumer adoption patterns and preferences. This study presents a comprehensive analysis of NEV consumer preferences across different geographical regions using advanced sales data mining techniques. By leveraging multi-source datasets including sales transactions, demographic information, and regional economic indicators, we investigate the underlying factors driving regional disparities in NEV adoption. Our methodology combines statistical analysis with machine learning approaches to identify distinct consumer preference clusters and regional characteristics. The analysis reveals substantial geographical variations in NEV adoption rates, with coastal regions demonstrating higher preference for premium electric vehicles while inland regions favor affordable hybrid alternatives. Regional economic development levels, infrastructure availability, and local policy incentives emerge as primary determinants of consumer preferences. The findings provide valuable insights for automotive manufacturers in developing region-specific marketing strategies and product positioning. Policy makers can utilize these insights to design targeted incentive programs that address regional characteristics and accelerate NEV adoption. The research contributes to understanding the complex interplay between geographical factors and consumer behavior in emerging automotive markets.