An Intelligent Computational Framework for Real Time Micro Moment Detection and Conversion Optimization in Smart Digital Marketing Systems

In the rapidly shifting landscape of digital marketing and tailored services, identifying and responding to high-intent user micro-moments has become a necessity for user activation and conversion maximization. Existing deep learning techniques such as single CNNs, LSTMs, and transformer models are promising but are either temporally weak, non-interpretable, or resource-intensive. This paper proposes a novel Attention-Augmented LSTM for Micro-Moment Detection (AALSTM-MM) that integrates behavioral clustering, sessional temporalization, and attention-augmented LSTM network to effectively learn sequential dependencies and intent signals of user browsing sessions. The designed model was experimented and verified against a benchmark e-commerce user session dataset with 92.3% accuracy, 91.7% precision, 90.8% recall, 91.2% F1-score, and an AUC of 0.948. Above all, it posed a low inference latency of 42 milliseconds and therefore was feasible for real-time application. In addition, attention visualizations and behavior clustering ensured interpretability and pattern insights to make decision-making more transparent. This paper delivers a robust, scalable, and interpretable model capable of inferring micro-moment behavior at scale with high accuracy and low latency. Its use cases are in real-time user intent prediction industries such as advertising, recommender systems, and intelligent customer service. The strengths of modeling accuracy, efficiency, and interpretability merge to make AALSTM-MM a significant step toward human-centered, smart digital interaction systems.