By Michael J. A. Berry
* jam-packed with greater than 40 percentage new and up to date fabric, this version exhibits enterprise managers, advertising analysts, and knowledge mining experts easy methods to harness basic facts mining tools and strategies to resolve universal forms of company difficulties * every one bankruptcy covers a brand new info mining method, after which indicates readers how you can follow the process for stronger advertising, revenues, and customer service * The authors construct on their attractiveness for concise, transparent, and sensible reasons of complicated suggestions, making this publication the fitting creation to facts mining * extra complicated chapters conceal such themes as easy methods to arrange facts for research and the way to create the required infrastructure for information mining * Covers center information mining ideas, together with determination timber, neural networks, collaborative filtering, organization principles, hyperlink research, clustering, and survival research
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Additional info for Data Mining Techniques: For Marketing, Sales, and Customer Support
People who buy kitty litter also buy cat food with probability P2. Association rules are discussed in detail in Chapter 9. Clustering Clustering is the task of segmenting a heterogeneous population into a num ber of more homogeneous subgroups or clusters. What distinguishes cluster ing from classification is that clustering does not rely on predefined classes. In classification, each record is assigned a predefined class on the basis of a model developed through training on preclassified examples.
Ultimately, though, the business needs to make the decision. What Is Data Mining? Data mining, as we use the term, is the exploration and analysis of large quan tities of data in order to discover meaningful patterns and rules. For the pur poses of this book, we assume that the goal of data mining is to allow a corporation to improve its marketing, sales, and customer support operations through a better understanding of its customers. Keep in mind, however, that the data mining techniques and tools described here are equally applicable in fields ranging from law enforcement to radio astronomy, medicine, and indus trial process control.
AM FL Y Analytic Customer Relationship Management It is widely recognized that firms of all sizes need to learn to emulate what small, service-oriented businesses have always done well—creating one-toone relationships with their customers. Customer relationship management is a broad topic that is the subject of many books and conferences. Everything from lead-tracking software to campaign management software to call center software is now marketed as a customer relationship management tool. The focus of this book is narrower—the role that data mining can play in improv ing customer relationship management by improving the firm’s ability to form learning relationships with its customers.