【正文】 d like to concentrate on those prospects who have large amounts of long distance usage. You can acplish this by building a model. Table 2 illustrates the data used for building a model for new customer prospecting in a data warehouse. Customers Prospects General information (. demographic data) Known Known Proprietary information (. customer transactions) Known Target Table 2 Data Mining for Prospecting The goal in prospecting is to make some calculated guesses about the information in the lower right hand quadrant based on the model that we build going from Customer General Information to Customer Proprietary Information. For instance, a simple model for a telemunications pany might be: 98% of my customers who make more than $60,000/year spend more than $80/month on long distance This model could then be applied to the prospect data to try to tell something about the proprietary information that this telemunications pany does not currently have access to. With this model in hand new customers can be selectively targeted. Test marketing is an excellent source of data for this kind of modeling. Mining the results of a test market representing a broad but relatively small sample of prospects can provide a foundation for identifying good prospects in the overall market. Table 3 shows another mon scenario for building models: predict what is going to happen in the future. Yesterday Today Tomorrow Static information and current plans (. demographic data, marketing plans) Known Known Known Dynamic information (. customer transactions) Known Known Target Table 3 Data Mining for Predictions If someone told you that he had a model that could predict customer usage how would you know if he really had a good model? The first thing you might try would be to ask him to apply his model to your customer base where you already knew the answer. With data mining, the best way to acplish this is by setting aside some of your data in a vault to isolate it from the mining process. Once the mining is plete, the results can be tested against the data held in the vault to confirm the model’s validity. If the model works, its observations should hold for the vaulted data. An Architecture for Data Mining To best apply these advanced techniques, they must be fully integrated with a data warehouse as well as flexible interactive business analysis tools. Many data mining tools currently operate outside of the warehouse, requiring extra steps for extracting, importing, and analyzing the data. Furthermore, when new insights require operational implementation, integration with the warehouse simplifies the application of results from data mining. The resulting analytic data warehouse can be applied to improve business processes throughout the anization, in areas such as promotional campaign management, fraud detection, new product rollout, and so on. Figure 1 illustrates an architecture for advanced analysis in a large data warehouse. Figure 1 Integrated Data Mining Architecture The ideal starting point is a data warehouse containing a bination of internal data tracking all customer contact coupled with external market data about petitor activity. Background information on potential customers also provides an excellent basis for prospecting. This warehouse can be implemented in a variety of relational database systems: Sybase, Oracle, Redbrick, and so on, and should be optimized for flexible and fast data access. An OLAP (OnLine Analytical Processing) server enables a more sophisticated enduser business model to be applied when navigating the data warehouse. The multidimensional structures allow the user to analyze the data as they want to view their business – summarizing by product line, region, and other key perspectives of their business. The Data Mining Server must be integrated with the data warehouse and the OLAP server to embed ROIfocused business analysis directly into this infrastructure. An advanced, processcentric metadata template defines the data mining objectives for specific business issues like campaign management, prospecting, and promotion optimization. Integration with the data warehouse enables operational decisions to be directly implemented and tracked. As the warehouse grows with new decisions and results, the anization can continually mine the best practices and apply them to future decisions. This design represents a fundamental shift from conventional decision support systems. Rather than simply delivering data to the end user through query and reporting software, the Advanced Analysis Server applies users’ business models directly to the warehouse and returns a proactive analysis of the most relevant information. These results enhance the metadata in the OLAP Server by providing a dynamic metadata layer that represents a distilled view of the data. Reporting, visualization, and other analysis tools can then be applied to plan future actions and confirm the impact of those plans. Profitable Applications A wide range of panies have deployed successful applications of data mining. While early adopters of this technology have tended to be in informationintensive industries such as financial services and direct mail marketing, the technology is applicable to any pany looking to leverage a large data warehouse to better manage their customer relationships. Two critical factors for success with data mining are: a large, wellintegrated data warehouse and a welldefined understanding of the busi