By Gordon S. Linoff Michael J. A. Berry
During this follow-up to their profitable first publication, info Mining suggestions, Michael J. A. Berry and Gordon S. Linoff provide a case study-based consultant to most sensible practices in advertisement facts mining. Their first ebook familiar you with the recent iteration of knowledge mining instruments and strategies and confirmed you ways to exploit them to make higher company judgements. getting to know information Mining shifts the point of interest from figuring out facts mining concepts to reaching enterprise effects, putting specific emphasis on client dating management.In this ebook, you are going to tips on how to practice facts mining innovations to unravel functional enterprise difficulties. After offering the elemental rules of knowledge mining and purchaser courting administration, Berry and Linoff percentage the teachings they've got realized via a sequence of warts-and-all case experiences drawn from their adventure in numerous industries, together with e-commerce, banking, cataloging, retailing, and telecommunications. in the course of the circumstances, you are going to find out how to formulate the enterprise challenge, research the knowledge, review the consequences, and make the most of this knowledge for related company difficulties in several industries.Berry and Linoff make it easier to use info mining to: keep shopper loyalty* goal the suitable customers* determine new markets for services and products* realize cross-selling possibilities off and on the internet
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Additional info for Mastering Data Mining: The Art and Science of Customer Relationship Management
2). 3 Cumulated Targets vs. ERR, COD:1.. Page 31 This affects the decision to use flash. The camera is programmed with some set of assumptions about the user's intentions. When all goes well, these assumptions are close enough to what the photographer had in mind so that the resulting pictures are satisfactory. When the assumptions are violated, the result is disappointment. A data mining tool or vertical application with embedded expertise has the same potential to please or disappoint depending on how well its assumptions match the actual requirements of the business.
6 shows a confusion matrix, both graphically and as a table. This tells us how many predictions made by a predictive model are correct and how many are incorrect. Which is the best model depends on the business problem. Page 56 r- .. a, . , . , 56 K. "- For a medical diagnostic test for cancer, the situation is quite different. We might use such a model as an initial screen. When there is an indication of cancer , the doctors would then run more elaborate tests. " In this case, we care a lot about false negatives-and we want as few as possible.
The question might be whether or not to approve a credit application, whether or not to pay an insurance claim, or whether or not to place a particular name on an outbound telemarketing call list, but the model takes the same basic form. The users, however, have different words for the inputs and outputs to these models, and addressing them in terms of "loss ratios" or "responders" as appropriate can make them feel more comfortable. For example, the popular Model 1 product from Unica Technologies contains four modules, each of which addresses a particular direct marketing challenge .
Mastering Data Mining: The Art and Science of Customer Relationship Management by Gordon S. Linoff Michael J. A. Berry