1. Customer segmentation: people are like birds of a feather, and customer segmentation or customer grouping is the basis of modern marketing. Through the method of cluster analysis, customers are divided to obtain different characteristics of each customer group, so as to differentiate the customer group. For example, the banking industry divides customers into different groups and provides them with different personalized investment products.
2. Customer churn prediction: Research shows that the cost of retaining old customers is far lower than the cost of acquiring new customers. However, it is unrealistic and very expensive to retain all customers. Through the mining of customer behavior patterns, customer churn prediction only finds out those customers who may be lost. Targeted retention of these customers can reduce marketing costs and increase product income, which is very necessary for telecom, banking, insurance and other industries with a large number of customers.
3. customer value analysis: customers have different contributions to the enterprise. Generally speaking, they follow the "2-8" principle, and a few customers contribute to the enterprise in a large proportion. So, which customers are the best customers of the enterprise? Is it just the group that has recently contributed the most income? Who are the potential good customers? Through customer value analysis, we can find the best customers of the enterprise and use the limited resources on the customers who can bring the greatest value.
4. Abnormal discovery: By analyzing the data, we can find out the abnormal points. For example, credit card is a widely used financial product nowadays. With the intensification of competition, banks are competing to vigorously promote credit cards, and a few criminals take the opportunity to apply for credit cards by using false information to defraud money. By learning and scoring the application materials through data mining, applicants with credit fraud can be found and losses can be avoided; Through the analysis of tax data, tax evasion is found.
5. Cross-marketing: Through the analysis of the combination marketing mode of goods and services, we can find the collocation marketing mode between goods. Using these models, we can design cross-selling strategies. For example, in the retail industry, the cradle of customer shopping is analyzed, and the shelves are rearranged according to the results, thus increasing the sales volume; Through the analysis of the curator's viewing habits, the radio station rearranged the programs to improve the ratings; Wal-Mart, a retail giant, uses data warehouse and data mining technology to analyze customers' buying patterns for inventory management and sales opportunities.
6. Personalized service: analyze everyone's consumption, find out other unusual consumption habits, and provide targeted services or promote sales. For example, in e-commerce, the website will recommend new products to customers according to past purchase records; According to the behavior of most people buying goods, recommend the relationship of the currently bought goods to customers.
7. database direct sales: generally speaking, a large number of direct sales emails are sent to customers at random, and less than 5% of customers may respond. According to the feedback of small-scale direct mail, data mining establishes a model, finds out the potential customers who are most likely to respond, and improves the response rate to 15%, thus cutting costs and increasing sales.
8. Improve work efficiency: By analyzing daily work or business data, find an optimized mode, thus improving work efficiency or business process. For example, the NBA uses a set of data mining tools to analyze the movements of players, so as to help coaches find the best ways to organize attack and defense; Through the analysis of the daily activities of the manufacturer's supply chain, find out the optimal operation mode of the supply chain; Through the analysis of data such as production plan and production efficiency, the most effective scheduling method is found; Through the analysis of the relationship between production technology and quality data, a good production process is found.
9. scientific discovery: by analyzing a large number of scientific experimental data, the hidden patterns are found, which can lead to new scientific discoveries. For example, through data mining and analysis of astronomical data, new stars are discovered; Through the analysis of biological information data, new genes and protein folding were found. Identify molecules with good drug characteristics for the manufacture of new drugs; Through the analysis of medical data, the relationship between drugs and diseases is found.
1. Early warning: through the analysis of the trend in the data, early warning will be given to the possible events. For example, in the telecommunications industry, through the analysis of previous early warning data, it is found that what conventional alarms may be precursors of major problems, and early warning is put forward to prevent accidents; Analyze the production data of the factory, identify the precursors of major quality problems, and take necessary measures to avoid the occurrence of product quality trials.
and so on, it is a very developed subject.