Reinforcement learning is a type of machine learning algorithm that has gained popularity in recent years due to its ability to learn from experience and make decisions based on trial and error. In the context of customer relationship management (CRM) in overseas store markets, reinforcement learning can be applied to mine potential customers and improve the effectiveness of marketing strategies. One of the key challenges in overseas store markets is identifying potential customers who are likely to make a purchase. Traditional CRM systems rely on historical data and predefined rules to segment customers and target them with marketing campaigns. However, these approaches may not be effective in overseas markets where customer behavior and preferences can vary significantly from one region to another. Reinforcement learning offers a more adaptive and dynamic approach to CRM mining by allowing the system to learn from interactions with customers and adjust its strategies in real-time. For example, the algorithm can be trained to analyze customer interactions with the store's website, social media, and other online platforms to identify patterns and preferences. Based on this information, the system can then personalize marketing messages and offers to target potential customers more effectively. Furthermore, reinforcement learning can also be used to optimize the allocation of marketing resources in overseas store markets. By continuously learning from customer interactions and feedback, the algorithm can identify the most effective marketing channels and strategies for different customer segments. This can help store managers to allocate their marketing budget more efficiently and maximize the return on investment. In addition, reinforcement learning can also be applied to improve customer engagement and retention in overseas store markets. By analyzing customer feedback and behavior, the algorithm can identify opportunities to enhance the customer experience and build long-term relationships. For example, the system can be trained to recommend personalized products or services based on individual customer preferences, leading to higher customer satisfaction and loyalty. Overall, the application of reinforcement learning in CRM mining of potential customers in overseas store markets offers several benefits. By leveraging the algorithm's ability to learn from experience and adapt to changing market conditions, store managers can improve the effectiveness of their marketing strategies, optimize resource allocation, and enhance customer engagement and retention. As a result, businesses can gain a competitive edge in overseas markets and drive sustainable growth.