Evaluating the Impact of AI-Based Recommendation Engines on Customer Behavior and Business Profitability

AI-based recommendation engines increasingly shape digital commerce, yet their specific behavioral and profitability effects remain insufficiently quantified within controlled academic settings, requiring rigorous empirical evaluation. This study aims to evaluate how AI-driven recommendation engines influence customer engagement, conversion behavior, and firm-level profitability metrics using multidimensional behavioral analytics and financial indicators within a defined academic research environment. A prospective analytical study was conducted in the Department of Business Analytics, Southern Arkansas University, from June–December 2024, involving 46 participants interacting with an AI-enabled e-commerce simulation platform. Behavioral logs, engagement time, click-through rate, purchase probability, and profitability indicators were analyzed using multivariate regression, paired t-testing, and effect-size estimation. Statistical significance was defined at p<0.05 with 95% confidence intervals. AI-generated recommendations increased mean engagement duration from 142.3±38.6 seconds to 198.7±42.1 seconds (mean difference 56.4 seconds; p=0.003). Click-through rate improved from 22.8% to 37.5% (+14.7%; Cohen’s d=0.81). Purchase likelihood rose from 18.4% to 29.6% (+11.2%; p=0.021). Average order value increased from USD 32.7±9.5 to 41.2±10.3 (p=0.017). Customer satisfaction (measured on a 10-point scale) improved from 6.1±1.4 to 7.8±1.2 (p=0.004). Profitability indicators demonstrated a 19.4% rise in simulated gross margin, while retention probability increased by 23.7% based on a logistic model (OR=1.42; CI: 1.06–1.95). All calculations represent study-generated estimates rather than market-level metrics. AI-driven recommendation systems substantially enhance customer engagement, behavioral conversion, and simulated profitability metrics. Future research should validate these findings using larger datasets, cross-industry samples, and real-time commercial deployment environments.