For example predictive patterns through data mining

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Unformatted text preview: ur mailing list to whom you will send solicitations. 7. Incorporating data mining in your CRM solution In building a CRM application, data mining is often only a small, albeit critical, part of the final product. For example, predictive patterns through data mining may be combined with the knowledge of domain experts and incorporated in a large application used by many different kinds of people. The way data mining is actually built into the application is determined by the nature of the customer interaction. There are two main ways you interact with your customers: they contact you (inbound) or you contact them (outbound). The deployment requirements are quite different. 8 Income > $60,000 No Yes Yes Job > 5 Years No Married Yes No Mustang Volvo Wagon Porsche Camry Outbound interactions are characterized by your company originating the contact such as in a direct mail campaign. Thus you will be selecting the people to whom you mail by applying the model to your customer database. Another type of outbound campaign is an advertising campaign. In this case you would match the profiles of good prospects shown by your model to the profile of the people your advertisement would reach. For inbound transactions, such as a telephone order, an Internet order, or a customer service call, the application must respond in real time. Therefore the data mining model is embedded in the application and actively recommends an action. In either case, one of the key issues you must deal with in applying a model to new data is the transformations you used in building the model. Thus if the input data (whether from a transaction or a database) contains age, income, and gender fields, but the model requires the age-to-income ratio and gender has been changed into two binary variables, you must transform your input data accordingly. The ease with which you can embed these transformations becomes one of the most important productivity factors when you want to rapidly deploy many models. Conclusion Customer relationship management is essential to compete effectively in today’s marketplace. The more effectively you can use the information about your customers to meet their needs the more profitable you will be. But operational CRM needs analytical CRM with predictive data mining models at its core. The route to a successful business requires that you understand your customers and their requirements, and data mining is the essential guide. Appendix: Data mining technology Decision trees Decision trees are a way of representing a series of rules that lead to a class or value. For example, you may wish to offer a prospective customer a particular product. The figure shows a simple decision tree that solves this problem while illustrating all the basic components of a decision tree: the decision node, branches and leaves. A simple classification tree. The first component is the top decision node, or root node, which specifies a test to be carried out. 9 Each branch will lead either to another decision node or to the bottom of the tree, called a...
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This note was uploaded on 11/25/2010 for the course CENG ceng taught by Professor Ceng during the Spring '10 term at Universidad Europea de Madrid.

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