By Gordon S. Linoff, Michael J. A. Berry
Very good insurance of varied facets of information mining. well known as a textbook (reason for purchase). lots of photos and illustrations; written in transparent and simply understood English.
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Additional resources for Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Examples of classification tasks that have been addressed using the tech niques described in this book include: ■■ Classifying credit applicants as low, medium, or high risk ■■ Choosing content to be displayed on a Web page ■■ Determining which phone numbers correspond to fax machines ■■ Spotting fraudulent insurance claims ■■ Assigning industry codes and job designations on the basis of free-text job descriptions In all of these examples, there are a limited number of classes, and we expect to be able to assign any record into one or another of them.
At Wachovia, a large North Carolina-based bank, data mining techniques are used to predict which customers are likely to be moving soon. For most people, moving to a new home in another town means closing the old bank account and opening a new one, often with a different company. Wachovia set out to improve retention by identifying customers who are about to move and making it easy for them to transfer their business to another Wachovia branch in the new location. Not only has reten tion improved markedly, but also a profitable relocation business has devel oped.
The Virtuous Cycle of Data Mining As these steps suggest, the key to success is incorporating data mining into business processes and being able to foster lines of communication between the technical data miners and the business users of the results. Identify the Business Opportunity The virtuous cycle of data mining starts with identifying the right business opportunities. Unfortunately, there are too many good statisticians and compe tent analysts whose work is essentially wasted because they are solving prob lems that don’t help the business.