A Study Of The Application Of Analytical CRM In Marketing Decision-making

CRM is a kind of enterprise operation and management mode considering "customer-centric" as its core idea. It is first all a marketing idea, and also a kind of advanced technology integration system. CRM systems can be divided into the types of carrier-based CRM, collaborative CRM and analytical CRM. By the client-oriented business workflow automation in all areas, and applying databases, data warehousing, data mining, and other technical means, it can make such marketing idea to practice in the daily operation of enterprises, and this result will be transformed into supporting enterprises' decision making. Data Mining is a process to extract and show the previously hidden information, these new knowledge from the database can be put into action to support the decision-making services. It can be effective in the data previously hidden information in the display in front of customers, In today's mainstream of large and medium-sized business organizations, industry and public agencies have considered the data mining as an important way to tap the "hidden" in the transaction data inside the regulations. The analytical CRM based on the data mining can effectively mine the valuable knowledge and regulations for enterprises' decision making from customers' large amount of data.Based on the study of the core idea, system and structure, function and characteristics of CRM, this paper analyzes present system and structure of CRM, it constructs analytical CRM system based on data mining using logical reasoning method with the key role of data mining technique in CRM and it also concludes the application and presentation of analytical CRM in eight aspects in marketing decision making. Based on the above theory study, this paper processes empirical analysis on the application of analytical CRM in customer segmentation and loss. The empirical study selects a certain telecommunication business as a target, utilizes Clementine software, and keeps to CRISP-DM methodology. According to different application themes, it uses the K-means clustering mining algorithms to achieve customer segmentation, and applies C5 algorithm's decision making tree mining technique to achieve the analysis of customer loss. Finally, this paper establishes different marketing strategy for different characteristics of the targets according to the results of customer segmentation, and makes detainment measures for possible customer loss according to the results of customer loss, and then it realizes the purpose of using CRM to achieve enterprise-locked marketing objectives, the extension of marketing profits and lower marketing costs.In this paper, the major innovation reflects in the following points: for the contents of the study, this paper does a preliminary study of the application of analytical CRM in supporting marketing decision-making. It integrates the analytical CRM into the analysis of marketing decision-making and makes it to a new height. The results of the analysis can't be got if just using the traditional statistical methods. It replaces the static marketing with dynamic marketing and realizes the marketing innovation from extensive marketing into the fine marketing. In the aspects of real application, this paper does customer segmentation study from the customer behavior, demographic and customer value. Compared to the single dimension segmentation, it can get better macro, comprehensive analysis of customers and get better results. For study methods, it first introduces the common model methods and then do empirical study in telecommunication business both for customer segmentation and customer loss analysis.The paper applies the K-means algorithm with the use of cluster mining technique to divide telecommunication business objective customers into 8 categories, and conducts targeted marketing for different groups' characteristics; it uses decision tree mining technique to analyze the customer loss, gets the loss of early warning score rules, and then makes retainable measures for possible losing customers. This method from general to the specific has made the effect of "model of the application process," and provides a reference for the application of analytical CRM in marketing decision-making.