Other applications of data mining systems include: In terms of customer analysis: bank credit card and insurance industries, using data mining to divide the market into meaningful groups and departments , thereby assisting marketing managers and business executives to better focus

on promotional activities and design new market campaigns. In terms of customer relationship management: Data mining can find product usage patterns or help understand customer behavior

This can improve channel management (such as bank branches and 6, etc.). Another example is that right-time sales are implemented based on the customer life cycle model. In the retail industry: Data mining for analysis of customer shopping baskets can assist business activities such as shelf layout, promotion time, promotional product mix, and understanding the status of slow-moving and best-selling products. Through

analysis of the market share of a manufacturer's products in various chain stores, customer statistics and analysis of historical conditions

the effectiveness of sales and advertising business can be determined. In terms of product quality assurance: Data mining assists in managing the interactions between a large number of variables, and can automatically discover certain abnormal data distributions and reveal changes in manufacturing and assembly operations

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conditions and various factors, thereby assisting quality engineers to quickly notice the scope of the problem and take corrective measures

In terms of remote communications: Analysis based on data mining assists organizational strategy changes to adapt to changes in the external world, and determines market change patterns to guide sales plans. In terms of network capacity utilization, data mining can provide insights into the structure and patterns of customer aggregate service usage, thereby guiding capacity planners to make optimal investment decisions in network facilities. In various enterprises and institutions, data mining plays an important role in many aspects such as counterfeit detection and hazard assessment, error avoidance, resource allocation, market sales forecast, advertising investment, etc. For example, in the chemical and

pharmaceutical industries, using data mining for huge amounts of biological information can discover new useful chemical components; in the field of remote sensing, based on the huge amounts of data coming from satellites and other sources every day, weather forecasting , ozone layer monitoring, etc. can play a big role.