Predective Anlaysis Consumer Behavior With Statistics

Abstract

Predictive analysis has become a critical tool for understanding consumer behavior in increasingly data-driven markets. This study examines the application of statistical predictive techniques to forecast consumer purchasing patterns and support informed business decision-making. Using a quantitative research design and secondary data sources, the analysis integrates descriptive statistics, correlation analysis, multiple regression, and time-series forecasting to identify key behavioral drivers of purchase intention. The findings reveal that digital engagement and past purchase history are the strongest predictors of consumer behavior, while income shows a moderate influence and age demonstrates limited significance. The regression model explains a substantial proportion of variance in purchase intention, confirming the effectiveness of statistics-based predictive approaches. Seasonal trends identified through forecasting further highlight the practical relevance of analytics for demand planning and targeted marketing strategies. The study emphasizes that behavioral variables outperform traditional demographic factors in predicting consumer decisions, reinforcing the importance of data-driven customer insights. Despite limitations related to secondary data usage and contextual factors, the research provides valuable evidence for organizations seeking to enhance personalization, customer retention, and operational efficiency. Overall, the study demonstrates that statistical predictive analysis offers a robust framework for anticipating consumer needs and improving strategic marketing outcomes in competitive digital environments.