AI-Augmented Business Intelligence for Campaign Performance Optimization in U.S. Retail and e-Commerce: A Mixed-Methods Study of Marketing ROI

Abstract

This study investigates the growing problem that many U.S. retail and e-commerce firms invest in digital marketing technologies and data systems but still struggle to convert campaign data into consistently optimized performance and stronger marketing return on investment. The purpose of the research was to examine whether AI-augmented business intelligence improves campaign performance optimization and marketing ROI, and to identify which analytical dimensions matter most in practice. The study adopted a quantitative, cross-sectional, case-based design focused on cloud-enabled and enterprise retail and e-commerce campaign environments, using survey evidence from 214 valid professional respondents drawn from campaign managers, BI analysts, e-commerce managers, and digital marketing specialists. The key independent variables were predictive analytics capability, real-time insight capability, customer segmentation intelligence, and decision automation support, while the main dependent variables were campaign performance optimization and marketing ROI. Data were analyzed using descriptive statistics, reliability testing, Pearson correlation, and multiple regression. The findings showed strong measurement quality, with Cronbach’s alpha values ranging from 0.77 to 0.88, and high construct means, including AI-augmented BI capability (M = 4.08, SD = 0.61), campaign performance optimization (M = 4.14, SD = 0.57), and marketing ROI (M = 4.02, SD = 0.64). Correlation results indicated that AI-augmented BI was strongly associated with campaign performance optimization (r = .710, p < .001) and marketing ROI (r = .640, p < .001), while campaign optimization was also strongly related to ROI (r = .680, p < .001). Regression analysis revealed that AI-BI dimensions explained 58.4% of the variance in campaign optimization (R² = .584, F = 73.48, p < .001), with predictive analytics capability emerging as the strongest predictor (β = .310, p < .001), followed by customer segmentation intelligence (β = .270, p < .001), real-time insight capability (β = .220, p = .002), and decision automation support (β = .140, p = .018). AI-BI and campaign optimization jointly explained 52.1% of the variance in marketing ROI (R² = .521, F = 114.06, p < .001). The study implies that firms can improve campaign precision, responsiveness, budget efficiency, and financial returns by strategically embedding AI-enhanced BI into campaign decision processes rather than using BI only for descriptive reporting.