AI-Powered Customer Engagement Sequence Analysis and Conversion Funnel Optimization in Multi-Channel E-Commerce Marketing
DOI:
https://doi.org/10.70393/6a69656173.333138ARK:
https://n2t.net/ark:/40704/JIEAS.v3n5a02Disciplines:
Artificial Intelligence TechnologySubjects:
Machine LearningReferences:
54Keywords:
Customer Engagement Sequence, Conversion Funnel Optimization, Multi-channel Marketing, Deep LearningAbstract
Multi-channel e-commerce environments generate complex customer engagement sequences that traditional funnel analysis cannot effectively model, limiting marketing optimization capabilities. This paper presents an AI-powered framework that leverages deep learning architectures to analyze temporal customer engagement patterns and optimize conversion funnels across heterogeneous marketing channels. We develop a sequential analysis system incorporating LSTM networks with attention mechanisms, transformer architectures for long-range dependency modeling, and graph neural networks for cross-channel interaction effects. The framework processes 847 million interaction events from 2.4 million customers across an 18-month period, implementing real-time optimization through reinforcement learning-based budget allocation algorithms. Experimental validation demonstrates 18.7% improvement in conversion rate prediction accuracy compared to traditional methods, with MAPE ranging from 8.2-12.7% across different customer segments. Marketing ROI increases by 34.7% through optimized channel allocation, while customer acquisition costs decrease by 22.1%. The multi-objective optimization algorithm successfully balances conversion maximization, cost minimization, and customer experience constraints. Our framework provides scalable sequential pattern recognition capabilities and actionable insights for dynamic marketing strategy optimization, advancing the state-of-the-art in AI-driven customer journey analytics.
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