Old Customer Segmentation Of Real Estate Development Enterprise Based On Data Mining

As the country issued a series of macro-control policies, the competition of real estate market is more and more intense. As a result of the change of market situation, real estate development enterprises change their strategy from the product as the center to take the customer as the center by gradually. Customer is the source of enterprise profit, and the high quality customers are the core competitiveness of enterprises. If real estate enterprises want to survive in the fierce competition, they need to accurately determine customers’ requirements, seize the high quality customers, and then provide targeted management, marketing and service to customers, and utilize limited resources to create maximum profits for the enterprise. Therefore, real estate development enterprise needs customer relationship management(CRM). Customer segmentation is one of the important steps and tools of customer relationship management. Customer segmentation can classify customers, and help the enterprise to identify the characteristics of different customers, and to find customers with high value quickly, so the enterprise can make more effective management. Studies have shown that the cost of developing new customers is much higher than maintaining existing customers. The existing customers have a possibility of rebuy, cross-buying and recommending new customers, which are an important profit source of enterprises. To maintain existing customer relationship is also an important guarantee for enterprise survival. Therefore, segmentation of existing customers and identification of their differences are also important for customer relationship management of the real estate development enterprise. However, as the enterprise accumulates more and more customer data, the traditional simple segmentation methods based on experience or statistics cannot meet the demand. The development of data mining technology is just to solve the problem. Data mining technology can process large amounts of data, find hidden and valuable knowledge and patterns, etc. Applying data mining techniques to customer segmentation can satisfy the requirement for large amounts of data processing, and improve the scientific nature and accuracy of customer segmentation results. Therefore, this article will research the existing customer segmentation of the real estate development enterprise based on data mining technology.Firstly, this paper discusses the theoretical knowledge about customer segmentation and data mining technology, which can provide theoretical support for later study. They mainly include the concept, indexes and methods of customer segmentation, as well as the concept of data mining, processes, functions and customer segmentation methods based on data mining.Secondly, this paper designs a customer segmentation model for the real estate development enterprise. They include through summary of the relevant literatures, and combining with the characteristics of customer consumption of real estate development enterprise, to build the index system of customer segmentation from two dimensions of customer value and customer loyalty. K-means algorithm, which is one of clustering algorithm of data mining, is also introduced, and the method is chosen as the customer segmentation method. And combine with the index system and customer segmentation method to build the customer segmentation model.Finally, this paper analyzes the example and gives suggestions. They include collecting customer data from a real estate development enterprise, using customer segmentation model to analyze the samples, and then analyzing the characteristics of each category of customers by the obtained results, and putting forward resources strategy and customer retention strategy. At the same time, put forward some suggestions from the operating level of enterprises in order to make the enterprise’s customer segmentation can go smoothly, and make full use of the results.