Leveraging Reinforcement Learning and Genetic Algorithms for Optimizing Customer Acquisition Costs in AI-Driven Marketing Strategies

Abstract

This research paper investigates the integration of reinforcement learning (RL) and genetic algorithms (GA) to optimize customer acquisition costs (CAC) in AI-driven marketing strategies. The study addresses the growing need for efficient customer acquisition in competitive markets by proposing a hybrid model that dynamically adapts marketing strategies to minimize costs while maximizing customer engagement and retention. Reinforcement learning is employed to simulate marketing environments, allowing algorithms to learn optimal strategies through trial and error. Simultaneously, genetic algorithms are utilized to evolve marketing strategies by selecting, crossing, and mutating parameters, thereby ensuring continuous improvement and adaptation to changing market conditions. The hybrid approach leverages the strengths of both methodologies—RL’s ability to learn complex decision-making policies and GA’s proficiency in exploring large parameter spaces. Extensive experiments were conducted using real-world marketing data from diverse industries. Results demonstrate a significant reduction in CAC, averaging a 15% improvement over traditional AI-driven marketing techniques. Additionally, the proposed model shows enhanced adaptability, proven by its quick recalibration in response to market shifts. This study contributes to the literature by providing a novel framework that combines two powerful AI techniques, offering a scalable solution for businesses seeking to enhance the efficiency of their marketing expenditures. Future research could explore the application of this hybrid model across various domains and further refine its adaptive capabilities.