PREDICTIVE ANALYTICS IN STRATEGIC COST MANAGEMENT: HOW COMPANIES USE DATA TO OPTIMIZE PRICING AND OPERATIONAL EFFICIENCY

Predictive analytics has become a crucial tool in strategic cost management, enabling companies to optimize pricing strategies and improve operational efficiency. This study is essential as it addresses the growing need for businesses to leverage data-driven insights in cost optimization amid increasing financial complexities. The research aimed to evaluate the impact of predictive analytics on pricing optimization, cost reduction, and operational efficiency. Using secondary data analysis, correlation analysis, and regression modeling, the study examined cost management practices across various industries. The findings revealed a strong positive correlation (r = 0.96, p < 0.01) between predictive analytics adoption and cost efficiency, with regression analysis showing that predictive models explained 92% (R² = 0.92) of the variance in financial performance. Companies utilizing predictive analytics achieved an 18% reduction in operational costs and a 25% increase in revenue growth through optimized pricing strategies. The study concludes that predictive analytics significantly enhances cost management by providing accurate financial forecasting and enabling dynamic decision-making. These results have critical implications for businesses seeking competitive advantage, policymakers developing regulatory frameworks, and researchers exploring advanced cost management techniques. The study recommends that firms integrate AI-driven predictive analytics tools, enhance workforce data literacy, and implement regulatory guidelines to ensure ethical data use. Future research should explore hybrid AI models for cost optimization in emerging markets.