Enhancing Green Marketing Strategies with Reinforcement Learning for Dynamic Product Design

Environmental marketing strategies are important to companies that seek to meet the growing consumer needs and expectations for green products and supply chain information. This two-problem approach aims to utilize reinforcement learning (RL) in improving dynamic product design applied to the project, as well as increase the level of transparency at the communication level through the blockchain technology's efficient log. The RL model modifies an element of a product as per consumers' demographics and contextual environmental standards, while the blockchain streams a history of a product's journey through a decentralized network and cannot be manipulated. Experimental results indicate substantial improvements: The new values are consumer satisfaction being 85.6%, environment impact score of 0.78, blockchain integrity score of 98.5%, and the average time taken to process a transaction of 120 ms. The model that has been proposed here greatly improves flexibility, openness, and consumer confidence. This double approach proves that RL and blockchain can help Sustainable marketing a step forward to reaching efficient Green marketing solutions that will benefit the consumer.