PREDICTIVE ANALYTICS FOR SALES FORECASTING AND REVENUE OPTIMIZATION: AN EMERGING BUSINESS

Abstract

The increasing adoption of digital technologies and data-driven decision-making has positioned predictive analytics as a strategic capability for improving organizational performance and sustaining competitive advantage (Davenport & Harris, 2017). Despite significant advancements in business intelligence systems, many organizations continue to encounter challenges in producing accurate sales forecasts due to dynamic market conditions, changing customer preferences, and demand uncertainty (Shmueli & Koppius, 2011). This study aims to examine the influence of predictive analytics on sales forecasting accuracy and revenue optimization in contemporary business environments. A quantitative research approach was employed using a structured questionnaire administered to 150 business professionals representing retail, manufacturing, and service organizations. The collected data were analyzed using descriptive statistics, reliability analysis, correlation, and multiple regression techniques to evaluate the relationships among the study variables (Hair et al., 2022). The findings reveal that predictive analytics significantly enhances forecasting precision, supports informed pricing and inventory decisions, improves customer demand estimation, and contributes positively to revenue optimization, consistent with earlier empirical evidence (Wamba et al., 2020; Choi et al., 2018). The study concludes that integrating predictive analytics into business operations strengthens strategic decision-making, operational efficiency, and long-term financial performance. The findings provide practical guidance for business managers, policymakers, and researchers seeking to leverage advanced analytical techniques for sustainable revenue growth and improved organizational competitiveness in an increasingly data-centric economy. Keywords: Predictive Analytics, Sales Forecasting, Revenue Optimization, Business Intelligence, Machine Learning, Data-Driven Decision Making, Organizational Performance. Etc.