AI-Driven Customer Behavior Modeling for Performance-Based Digital Marketing Systems

Abstract

This study addresses a practical problem in performance-based digital marketing: organizations increasingly deploy AI models to predict and influence customer behavior, yet reported improvements in conversion efficiency and ROI are inconsistent because data signals, activation decisions, and measurement logic vary widely across cloud marketing stacks and enterprise platforms. The purpose of this research is to quantify, compare, and explain what AI-driven customer behavior modeling approaches work best, under what data conditions, and with which evaluation designs, using a quantitative cross-sectional, case-based synthesis. The sample comprises N = 45 peer-reviewed cloud and enterprise cases (2005–2023) drawn from marketing systems implemented across contexts such as e-commerce conversion optimization, subscription and SaaS retention, mobile funnels, and omnichannel operations. Key variables include AI technique family (supervised ML, deep learning or sequence models, recommenders, causal or uplift models, bandits or reinforcement learning), signal strategy (RFM and value features, clickstream and session features, exposure intensity, context and creative features, multi-source integration), activation lever (targeting, bidding and budget allocation, creative selection, timing and frequency control, lifecycle messaging), and measurement approach (observational attribution vs quasi-experimental incrementality vs experimental lift tests). The analysis plan applies descriptive frequency statistics, structured vote-counting of KPI direction, and a 5-point Likert evidence-support scoring to test hypotheses about performance lift, multi-source advantage, incrementality alignment, and governance effects. Headline findings show supervised ML as the most prevalent technique (31/45, 68.9%), while deep learning or sequence models appear in 19/45 (42.2%) and causal or uplift modeling in 12/45 (26.7%); overall, 33/45 studies (73.3%) report positive KPI movement attributable to AI-informed modeling, with H1 receiving strong support (M = 4.08, SD = 0.71). Multi-source data integration demonstrates stronger consistency, with 17/20 (85.0%) multi-source studies reporting positive impact versus 16/25 (64.0%) single-source studies, supporting H2 (M = 3.89, SD = 0.77). Measurement rigor is the most decisive moderator: incrementality-oriented studies report positive conclusions 24/27 (88.9%) versus 9/18 (50.0%) for attribution-only studies, yielding the strongest hypothesis support for H3 (M = 4.22, SD = 0.64). Finally, governance-explicit cases show more stable gains (15/18, 83.3%) than governance-implicit cases (18/27, 66.7%), supporting H4 (M = 3.76, SD = 0.80). These results imply that enterprises should prioritize consent-aware multi-source signal integration, operationalize model outputs into clear activation levers, and validate impact through incrementality-based measurement to avoid optimizing toward credited but non-incremental outcomes.