AI-Driven Optimization of Multi-Channel Digital Campaigns Using Predictive Analytics and Project Management Frameworks
DOI:
https://doi.org/10.15680/IJCTECE.2025.0803013Keywords:
Causal AI, digital campaign optimization, uplift modeling, agile project management, multi-channel marketing, D2C e-commerce, execution reliabilityAbstract
AI-driven digital campaign optimization has improved customer targeting, channel choice, and budget allocation, but many campaign models still assume that a recommended action can be implemented without friction. That assumption is weak in subscription-based direct-to-consumer e-commerce, where campaign value depends on customer response as well as creative approval speed, sprint stability, backlog control, task completion, cross-functional handoffs, and rework frequency. This study develops and evaluates a sprint-aware causal AI framework for multi-channel digital campaign optimization by integrating predictive uplift analytics with agile project-execution reliability signals. The analysis uses a quantitative longitudinal causal machine-learning design based on customer-campaign observations, CRM records, campaign exposure logs, channel-spend data, transaction records, subscription activity, and project-management workflow records across email, SMS, paid social, paid search, app push, and display retargeting campaigns. Five campaign optimization models were compared: rule-based allocation, response prediction, attribution-based optimization, causal uplift modeling without sprint data, and the proposed sprint-aware causal AI model. The proposed model produced the strongest results, with AUUC = 0.286, Qini coefficient = 0.241, incremental ROAS = 3.18x, CAC reduction = 18.4%, and CLV uplift = 14.7%. It improved incremental campaign performance by 24% to 31% compared with conventional AI-based campaign optimization. The results show that customer-channel uplift allocation accuracy mediates the effect of AI-driven optimization on campaign performance, while agile project-execution reliability strengthens the realization of AI-generated campaign recommendations. The study contributes a causal and execution-aware framework for improving campaign allocation decisions in subscription-based D2C e-commerce.References
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