Cross-Platform Marketing Strategies using Reinforcement Learning to Maximize Multi-Channel Engagement

To address the shortcomings with existing approaches suggest a brand-new marketing system based on reinforcement learning (RL) that will enable engagement maximization across several platforms and channels. Traditional rule-based algorithms in use now don't adapt to real-time resource changes brought about by user activity. The study use offline reinforcement learning to increase user engagement and conversion rates by analyzing interactions from other platforms. With an average increase of 7.8 minutes in session length, a 4.8% increase in conversion rate, and a 6.7% increase in CTR, the evaluation results show that RL systems outperform conventional systems. Although it is effective in all marketing channels, its conversion rates are much higher when it is employed on emails, social media, and online advertisements. The results show that the RL system, in addition to providing a scalable and efficient quaternary solution for contemporary marketing difficulties, may increase engagement.