Data mining algorithms have become an essential tool for businesses to gain insights from large volumes of data. One area where data mining algorithms are being increasingly applied is in customer stratification for data-driven customer management. This is particularly important for overseas store systems, where understanding and managing customer behavior is crucial for success. Customer stratification involves dividing customers into different segments based on their behavior, preferences, and other characteristics. This allows businesses to tailor their marketing, sales, and customer service efforts to better meet the needs of different customer groups. Data mining algorithms play a key role in this process by analyzing large amounts of customer data to identify patterns and trends that can be used to create meaningful customer segments. One of the most common data mining algorithms used in customer stratification is clustering. Clustering algorithms group customers based on similarities in their behavior, such as purchasing patterns, frequency of visits, and average spending. This allows businesses to identify different customer segments, such as high-value customers, loyal customers, and occasional customers. By understanding the characteristics of each segment, businesses can develop targeted strategies to engage and retain customers in each group. Another important data mining algorithm for customer stratification is classification. Classification algorithms are used to predict the behavior of customers based on historical data. For example, businesses can use classification algorithms to identify customers who are likely to churn or those who are likely to respond to a specific marketing campaign. This allows businesses to proactively address customer churn and optimize their marketing efforts to maximize their impact. In the context of overseas store systems, data-driven customer management is particularly important. With customers from different cultural backgrounds, languages, and preferences, understanding and managing customer behavior can be challenging. Data mining algorithms can help businesses gain a deeper understanding of their overseas customers and tailor their strategies to better meet their needs. CRM (Customer Relationship Management) systems play a crucial role in implementing data-driven customer management strategies. By integrating data mining algorithms into CRM systems, businesses can leverage customer data to create personalized experiences, improve customer satisfaction, and drive business growth. For overseas store systems, this can be especially valuable in building strong relationships with customers and fostering loyalty across different markets. In conclusion, the application of data mining algorithms in customer stratification is essential for data-driven customer management, particularly in overseas store systems. By leveraging clustering and classification algorithms, businesses can gain valuable insights into customer behavior and preferences, allowing them to develop targeted strategies to engage and retain customers. When integrated into CRM systems, data mining algorithms can help businesses create personalized experiences and drive business growth in overseas markets.