Edge AI Conversion Modelling Optimizing TOFU-to-BOFU Dynamics for Intent-Based Digital Marketing Revenue Acceleration

The real-time nature of the digital world has limited cloud-based marketing analytics. Latency and privacy issues are hindering optimization of the customer journey. The paper presents an Edge AI Conversion modelling framework which deploys lightweight transformer-conformer hybrids on user devices for dynamically optimizing TOFU-to-BOFU funnel dynamics using an intent-based inference mechanism. This model combines various methods such as text, voice, and behavioural data via on-device processing to predict the chances of conversion along with recommending actions specific to the stage like personal nurturing to MOFU leads or urgency tactics to BOFU closures. This is formulated as reinforcement learning with Markov decision processes. This would help maximize the revenues by minimizing drop-offs on the funnels as well as lifetime value. The system achieves 32% uplift in the return on ad spend (ROAS) on a suite of simulated e-commerce as well as the SaaS campaigns. Several key innovations comprise quantized edge inference at a latency of under 50ms, federated updates for scalabilit and privacy-preserving synchronization. Our evaluations on a 1M-session dataset show that our approach outperforms centralized baselines in terms of accuracy (92% intent detection) and responsiveness, thereby addressing critical gaps in intent-driven marketing. This project will lead to the development of self-sufficient revenue engines.