Application of hottest data mining in CRM

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The application of data mining in CRM

MIT's science and Technology Review Magazine puts forward ten emerging technologies that will have a significant impact on mankind in the next five years, "data mining" ranks third

data mining

crm can help enterprises establish a huge data warehouse, but data mining in data warehouse is gradually becoming the core part of CRM

Data mining is a process of extracting potential and valuable knowledge, models or rules from a large amount of data. For enterprises, data mining can help find the trend of business development, reveal known facts, predict unknown results, and help enterprises analyze the key factors needed to complete the task, so as to increase revenue, reduce costs, and put enterprises in a more favorable competitive position

due to the automation of business operation processes in various industries, a large amount of business data has been generated in the enterprise. These data are not collected for the purpose of analysis, but generated due to business operations. The analysis of these data is not for the needs of research, but to provide real valuable information for business decision-making, so as to obtain profits

but a common problem faced by all enterprises is: the amount of information and data is very large, and what is the really valuable information? What are the connections between these information? Therefore, it is necessary to analyze a large amount of data in depth to obtain information that is conducive to business operation and improve competitiveness. Data mining is to mine more valuable information from a large amount of data

data mining technology helps enterprises manage all stages of the customer life cycle, including winning new customers, making existing customers create more profits, maintaining valuable customers, and so on. It can help enterprises determine the characteristics of customers, Enable enterprises to provide customers with (for example, rope samples with a diameter of less than 1mm, including steel wire, iron wire, thin wire and other targeted services. Sometimes in order to reduce weight, non-ferrous metals and special metals such as aluminum alloy are also used.

customers obtain

the traditional ways to obtain customers generally include extensive media advertising, a large number of marketing, billboards in downtown and station terminals, etc. for advertising, most of them choose the mainstream with the largest overlap between reader groups and direct target customer groups Media. But data mining can help you change that

suppose you are the marketing manager of an enterprise that produces baby diapers and decide to use direct mail to promote the product. The most traditional way is to choose a region of interest first, and get the business data of this region that meets your conditions through information intermediary companies. Generally, the conditions you require may be: the list and address of people aged 25-32 who have recently bought baby carriages. Then you will contact them and send them the information. This is a very simple direct mail advertisement. Although it is much more economical and effective than ordinary advertisements, we think it is still relatively elementary and not completely satisfactory. Because among these 25-32 year-old people who recently bought a stroller, there are many other factors. For example, many people will prepare a stroller before their child is born, and they have not yet decided which brand of diapers to use

after adopting data mining, the effectiveness and response rate of direct mail advertisements provided to customers have been greatly improved. Through data mining, we can find out whether the consumers who buy baby diapers are men or women, their educational background, income, hobbies, occupations and so on. We can even find out how long different people will start buying diapers after buying strollers, and what kind of people (babies) will buy diapers of what type, and so on. Many factors may not seem to have any connection with buying baby diapers on the surface, but the results of data mining prove that there is a connection between them

cross selling

the relationship between enterprises and customers is constantly changing. Once a person or company becomes your customer, you should try your best to make this customer relationship perfect for you. Generally speaking, you can use these three methods: 1. Maintain this relationship for the longest time; 2. Deal with your clients as often as possible; 3. Ensure the maximum amount of profits per transaction. Therefore, we need to cross sell our existing customers

cross selling refers to the process of selling new products or services to existing customers. It is easy to understand that a customer who has bought a stroller is likely to be interested in your baby diapers or other baby products. But for enterprises, the real concern is how to find the inherent subtle relationship. Data mining can help enterprises find the relationship between them

the advantage of cross selling is that for original customers, enterprises can easily get rich information about this customer, and a large amount of data is very helpful for the accuracy of data mining. The customer information that the enterprise has, especially the information of previous purchase behavior, may contain the key and even decisive factors for this customer to decide his next purchase behavior. At this time, the role of data mining will be reflected, which can help enterprises find these factors that affect their purchase behavior

customer retention

now the competition in various industries is becoming more and more fierce, and the cost of obtaining new customers is rising. Therefore, maintaining original customers is becoming more and more important for all enterprises. For example, in the United States, the average cost for a mobile communication company to acquire a new user is $300, while the cost of retaining an old customer may be just one. The cost difference may be different in various industries. In financial services, communications and high-tech product sales, this figure is very amazing, but no matter what industry, the gap of more than 6-8 times is recognized by the industry. And often lost customers contribute more profits than newly acquired customers

in recent years, domestic one-to-one marketing (onetoone) is being promoted by more and more enterprises and media. One to one marketing refers to knowing each of your customers and establishing a long-term and lasting relationship with them. This seemingly new concept has been implemented in a very old way. Even some companies understand that one-to-one marketing is to send a card to a customer every birthday or anniversary. Today, with the development of science and technology, it is true that everyone can have some unique goods or services, such as making a suit of clothes that fit well according to their own size, but in fact, marketing is not cutting clothes. You can know what clothes are suitable for your customers, but you will never know what stocks are suitable for your customers. One to one marketing is a rational concept with curve display idealization, which is difficult to achieve in most industries in practice

data mining can divide a large number of customers into different classes. Customers in each class have similar attributes, while customers in different classes have different attributes. You can provide completely different services to these two types of customers to improve customer satisfaction. The benefits of customer classification are obvious. It is not only a very simple classification, but also can bring a satisfactory result to Chinese enterprises that are still in the deep development stage of industrialization, informatization, urbanization, marketization and internationalization. For example, if you know that 85% of your customers are elderly, or only 20% are women, I believe your marketing strategy will be different. Data mining can also help you classify customers. Detailed and practical customer classification is of great benefit to the business strategy of enterprises

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