Enhancing Customer Acquisition Cost Efficiency through Reinforcement Learning and Genetic Algorithms in AI-driven Strategies

Abstract

This research explores the innovative application of reinforcement learning (RL) and genetic algorithms (GA) to optimize customer acquisition cost (CAC) efficiency in AI-driven marketing strategies. By integrating RL and GA, the study aims to develop a hybrid model that autonomously adapts and evolves marketing tactics to reduce CAC while maintaining high conversion rates. The paper first reviews the theoretical underpinnings of RL and GA, focusing on their potential synergistic benefits in dynamic decision-making processes. An experimental setup simulates a marketing environment where the hybrid model is tested against traditional CAC reduction strategies. Results demonstrate that the RL-GA model significantly decreases CAC by approximately 25% compared to conventional methods, achieving faster adaptation to changing market conditions and consumer behavior patterns. The study's findings suggest that leveraging the exploratory capabilities of reinforcement learning with the evolutionary nature of genetic algorithms allows for more precise targeting and personalization of customer acquisition efforts. Implications for businesses include enhanced ROI from marketing campaigns and an AI-driven framework capable of responding to unpredictable market shifts. The paper concludes with a discussion of limitations and future research directions, including the exploration of additional AI techniques to further refine the hybrid model's efficiency and applicability across different industries.