Currently, there is a lot of research on the data mining techniques and methods applied in CRM, and the CRM applications of enterprises in different industries and environments vary greatly, and the specific data mining techniques and methods applied to them will also be different. Data mining techniques and methods are endless, and it is difficult to cover all of them here. Although the data mining techniques applied to different CRM applications are many and complex, the purpose of CRM data mining is mainly in the following four aspects: customer segmentation, acquiring new customers, enhancing customer value and keeping customers to prevent churn. Data mining in the retail industry CRM is mainly used in the following aspects. First, the premise of CRM implementation - customer segmentation Customer segmentation is the customer according to its gender, income, trading behavior characteristics and other attributes are subdivided into groups with different needs and trading habits, the same group of customers in the demand for products and trading psychology and other aspects of the similarity, while the difference between the different groups is large. Customer segmentation can enable enterprises to formulate correct marketing strategies in marketing, through the different categories of customers to provide targeted products and services to improve customer satisfaction with the enterprise and products to obtain greater profits. Customer segmentation can be done either by categorization or by clustering. For example, customers can be categorized into high-value and low-value customers, and then determine the factors that have an impact on the classification, and then extract the customer data that has the relevant attributes, and select the appropriate algorithm to process the data to get the classification rules. Using the method of clustering, it is not known before that the customers can be divided into several categories, after clustering the data, then the resultant data is analyzed to summarize the similarities and ****. Each category of customers has similarity attributes, and different categories of customers have different attributes, so as to determine the interests, consumption habits, consumption tendencies and consumption needs of a particular consumer group or individual, and then infer the next step in the consumption behavior of the corresponding consumer group or individual. Segmentation allows users to view the data in the entire database from a higher level, and also allows companies to adopt different marketing strategies for different customer groups, effectively utilizing limited resources. Reasonable customer segmentation is the basis for the implementation of customer relationship management. Second, acquiring new customers - customer response analysis In most business sectors, the ability to acquire new customers is included in the key indicators of business development. New customer acquisition includes finding customers who do not know your product, they may be potential consumers of your product, or they may be customers who have previously received services from your competitors. Before finding new customers, a company should determine which customers are possible prospects, which customers are easy to acquire, and which customers are more difficult to acquire, so that the company's limited marketing resources can be utilized in the most rational way. Therefore, predicting how potential customers will respond to an enterprise's sales promotion activities is a prerequisite for customer acquisition. Due to the increasingly large number of potential customers, how to improve the relevance and effectiveness of marketing promotions has become a key issue in acquiring new customers. Data mining can help enterprises to identify potential customer groups, improve the corresponding rate of customer marketing activities, so that the enterprise to do a clear and targeted. According to the enterprise given a series of customer information and other inputs, data mining tools can establish a "customer response" prediction model, the use of this model can be calculated for a customer response to a marketing activity indicators, according to these indicators can be identified for those interested in the services provided by the enterprise customers, and thus achieve the purpose of acquiring customers. The purpose of customer acquisition. The correlation analysis, clustering and classification functions in data mining technology can complete this analysis very well. Third, enhance customer value - cross-selling Cross-selling refers to the marketing process of selling new products or services to the original customers of the enterprise, which is not only an effective means to increase profits by expanding sales to existing customers, but also an important strategy to enhance the image of the enterprise, cultivate customer loyalty, and safeguard the sustainable development of the enterprise. The business relationship between a company and its customers is a continuous and evolving relationship. After the customer and the company have established this two-way business relationship, there are many ways to optimize the relationship and extend its duration. During the maintenance of this relationship, increase the mutual contacts and try to get more profit out of each mutual contact. And cross-selling is such a tool, i.e. the process of offering new products and services to existing customers. In cross-selling activities, data mining can help companies to analyze the optimal sales match. The customer information held by the enterprise, especially the information of previous purchasing behavior, may contain the key, or even the determining factor for this customer to decide his next purchasing behavior. Through relevant analysis, data mining can help analyze the optimal and most reasonable sales matching. The general process is this, first of all, analyze the existing customer's purchasing behavior and consumption habits data, and then use some algorithms of data mining to model individual behavior under different sales methods; secondly, use the established prediction model to predict and analyze the customer's future consumption behavior, and evaluate each type of sales method; and finally, use the established analytical model to analyze the new customer data in order to decide to provide customers with which cross-selling approach is most appropriate. There are several data mining methods that can be applied to cross-selling. Correlation rule analysis, which can find out which items customers tend to buy in association; cluster analysis, which can find out user groups interested in a specific product; neural networks, regression, and other methods, which can predict the likelihood of a customer buying that new product. The results of correlation analysis can be used in two aspects of cross-selling: on the one hand, for the purchase of more frequent combination of goods, to find out those who have purchased a combination of most of the goods of the customer, to promote them "missing" goods; on the other hand, each customer to find out the more applicable correlation law, to promote the corresponding series of goods to them. Fourth, keep customers - customer churn analysis With the increasingly fierce competition, the cost of acquiring new customers continues to rise. For most businesses, the cost of acquiring a new customer greatly exceeds the cost of maintaining an existing customer, and it has become the *** knowledge of most businesses that the work of keeping existing customers is becoming more and more valuable. The longer you keep a customer, the longer it takes to collect the initial investment and acquisition costs you spend on that customer, and the more profit you make from the customer. However, due to the uncertainty of various factors and the ever-growing market, as well as the presence of some competitors, many clients are constantly switching from you to another service provider in search of lower fees and other service providers who offer additional favorable terms for new clients than you do. We call the behavior of customers moving from one service provider to another as customer migration. In order to analyze what are the main factors that lead to customer transfers and can be targeted to retain those customers who have a tendency to leave, we can model customers who have already been lost by using data mining tools to identify the patterns that lead to their transfers, and then use these to identify the current customers who may be lost, so that the company can target the needs of the customer and take appropriate measures to prevent the loss of the customer, and thus achieve the goal of retaining the original customers. To solve the problem of customer churn, you first need to identify what kind of customers are being churned. If the loss of poor-quality customers, the enterprise is begging for; if the loss of high-quality customers, the enterprise is a huge loss. If the enterprise quality customers the longer the period of stability, the enterprise and its maintenance of the relationship with the lower the cost, the greater the gain. Therefore, in order to maintain high-quality customers, you need to identify high-quality customers first. This can be done through the previous customer segmentation, which analyzes customer profitability and identifies and predicts customer strengths and weaknesses. When it is possible to identify the strengths and weaknesses of customers, first, according to the data of lost customers, you can use decision trees, neural networks, etc. to analyze and mine, to find the characteristics of lost customers; and then, the analysis of the existing customer consumption behavior to determine the likelihood of loss of each type of customer, which focuses on the discovery of those who have a high risk of transferring the possibility of high business value of the customer, before the transfer of these customers to other service providers in the same industry, to take the appropriate measures. This focuses on identifying customers with high risk of diversion and high business value, and taking appropriate business initiatives to retain these valuable customers before they move on to other service providers in the same industry. We call this process customer retention or customer retention. When choosing a data mining tool, a decision tree tool is a good choice if you want to be able to segment your customers and have a clear understanding of the reasons for customer churn. Although some other data mining techniques such as neuron networks can also produce good predictive models, these models are difficult to understand. When these models are used for predictive analytics, it is difficult to get an in-depth understanding of the causes of customer churn, much less get any clues to deal with customer churn. In this case, segmentation and clustering techniques can also be used to get insights, but generating predictive models with these techniques is comparatively much more complex. Generally, in customer retention, mostly categorical regression decision trees are used to generate predictive models. To summarize, data mining has a wide range of applications in CRM and from a certain point of view it can be said that it is the soul of CRM. Through the use of data mining techniques, the relationships and rules in the data can be discovered to provide important decision-making references for managers to formulate accurate market strategies. Moreover, through the sales and service departments to communicate with customers, and strive to optimize the satisfaction of customer needs, improve customer loyalty and satisfaction, enhance customer value, improve corporate earnings, to achieve a "win-win" situation between the enterprise and the customer. It is this that has made CRM a great success. At present, there are many studies on data mining techniques and methods applied in CRM, and the CRM applications of enterprises in different industries and environments are very different, and the specific data mining techniques and methods applied to them will also be different. Data mining techniques and methods are endless, and it is difficult to cover all of them here.