Customer Knowledge Modeling And Customer Analysis In CRM

Nowadays, modern enterprises make 80% of their profits on their 20% key customers while the remaining 80% of the customers have made little or none profits to the enterprises. In order to obtain the greatest degree of profit, the enterprises may need to implement dynamic and different customer management, and make the survival of the fittest customers. This will not only help enterprises reduce customer costs and enhance customer profitability, but also spend enterprise resources on the 20% key customers, which will give the core interests for the enterprises, to maximize the interests of enterprises. Therefore, to establish a highly efficient customer knowledge management, implement a reasonable customer analysis model and effectively gain the key customers, thus implement different management for the enterprise customer, is the urgent problem for business.This paper brings forward a knowledge-based CRM system, which can provide analysis support for the enterprises. The system can access, analyze the customer knowledge and build a knowledge-based customer analysis model by using data mining methods, so as to implement customer classification and provide reliable basis for enterprise marketing activity. According to the process of data mining in CRM, we give the example of implementing customer classification as follow: Firstly, identify the problem domain - the customer segmentation; Secondly, prepare the data; Thirdly, establish the data mining algorithms; Fourthly, implement data mining.During the process of establishing data mining algorithms, as the types of customers are unknown in advance, we use a classic clustering algorithm -- distance-based K-Means clustering algorithm as a classification algorithm. At the same time the author introduces simulated annealing strategy thinking on the traditional K-Means clustering algorithm, so as to improve the traditional algorithm and solve the partial optimization problem caused by isolated data points during the clustering process.During the process of preparing the data, the customer knowledge can not be used directly as the classification distance for clustering, as they are scattered, small in particle size. This paper builds an integrated assessment model to draw indicators out from the fragmented customer knowledge and integrate these indicators into a synthetical indicator by using dimension reduction method. The synthetical indicator is named as customer comprehensive assessment value, which can be used as the classification distance in clustering. During this process, the author uses the dimensionless method to standardize the indicators and then uses AHP method to integrate the indicators.At the end, this paper designs and establishes a knowledge-based CRM system. With the aim of achieving the goal of knowledge innovation, the system implements the functions as follow:Through the enterprise portals internet to provide multiple channels for enterprise to obtain the customer knowledge.Through the business application subsystem and customer analysis subsystem to apply customer knowledge.Through the customer knowledge base management subsystem to share, innovation customer knowledge.The customer knowledge base management subsystem not only provides the model and the algorithm template for customer analysis, but also provides enterprise functionalities of customizing customer analysis model and algorithm.