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What is the typical application of CRM data statistics and analysis? aiming at this problem, this article introduces the corresponding analysis and solution in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

Through the CRM customer relationship management system, enterprises can collect, analyze and track customer information, so as to obtain accurate customers. The typical applications of CRM system for data analysis can be summarized as follows:

Customer acquisition

The traditional way to get customers is to attract new customers through a large number of media advertisements and other ways, this way is too wide to achieve targeted, increase a lot of costs. Through the data statistics and analysis function of CRM, a data model can be established from the useful data collected from previous market activities. Therefore, enterprises can understand the characteristic classification of real potential customers, so as to achieve targeted rather than traditional empirical conjecture in future market activities.

Customer segmentation

Customer segmentation can observe the data from a higher level, and customer segmentation can treat different customer groups in different ways. Classification, clustering and other technologies in data mining can enable users to subdivide the data in the database according to age, occupation, region, preferences and other attributes that enterprises are interested in. Customer segmentation is not only the basis for enterprises to determine products and services, but also the basis for the establishment of customer one-to-one marketing.

Analysis of customer profitability

As far as the customers of the enterprise are concerned, the vast majority of the profits of the enterprise come from a small number of customers, but it is difficult for the enterprise to determine which customers have high profit returns and which customers have low or even negative profit returns. Data mining technology can help enterprises to distinguish customers with different profit returns. As a result, more resources can be allocated to customers with high profit returns to generate greater profits, while reducing the input of customers with low or negative profit returns. For this reason, before data mining, enterprises should establish a set of optimization objective methods to calculate profit returns. It can be a simple calculation, such as the income generated by a customer minus all the corresponding expenses, or it can be a more complex formula. Then use data mining tools to mine the corresponding knowledge from the transaction records.

Customer retention

With the increasingly fierce competition in the market, the cost of obtaining a new customer is much higher than the maintenance cost of the old customer. Therefore, how to maintain the original old customers and not let them lose has become an important topic of the CRM system. In practical application, the established data model can be used to predict customers and judge the customers who may be lost in the future. In this way, enterprises can study the needs of these customers and take corresponding measures to prevent their loss, so as to achieve the goal of keeping customers.

The answer to the question about the typical application of CRM data statistics and analysis is shared here. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel to learn more about it.

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