LEADER 06702nam 2200781 a 450 001 9910823349203321 005 20220519174221.0 010 $a9786613663139 010 $a9781280686191 010 $a1280686197 010 $a9781118087459 010 $a1118087453 010 $a9781118087503 010 $a111808750X 035 $a(CKB)3400000000021217 035 $a(EBL)706770 035 $a(OCoLC)731512439 035 $a(SSID)ssj0000611655 035 $a(PQKBManifestationID)12181795 035 $a(PQKBTitleCode)TC0000611655 035 $a(PQKBWorkID)10666655 035 $a(PQKB)11356014 035 $a(JP-MeL)3000065363 035 $a(Au-PeEL)EBL706770 035 $a(CaPaEBR)ebr10513818 035 $a(CaONFJC)MIL366313 035 $a(CaSebORM)9780470650936 035 $a(MiAaPQ)EBC706770 035 $a(OCoLC)801812997 035 $a(OCoLC)ocn801812997 035 $a(EXLCZ)993400000000021217 100 $a20110127d2011 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData mining techniques $efor marketing, sales, and customer relationship management /$fGordon S. Linoff, Michael J.A. Berry 205 $a3rd ed. 210 $aIndianapolis, Ind. $cWiley Pub., Inc.$d2011 215 $a1 online resource (889 p.) 300 $aBerry's name appears first on the 2nd ed. 300 $aIncludes index. 327 $aData Mining Techniques; Contents; Introduction; Chapter 1 What Is Data Mining and Why Do It?; What Is Data Mining?; Data Mining Is a Business Process; Large Amounts of Data; Meaningful Patterns and Rules; Data Mining and Customer Relationship Management; Why Now?; Data Is Being Produced; Data Is Being Warehoused; Computing Power Is Affordable; Interest in Customer Relationship Management Is Strong; Every Business Is a Service Business; Information Is a Product; Commercial Data Mining Software Products Have Become Available; Skills for the Data Miner; The Virtuous Cycle of Data Mining 327 $aA Case Study in Business Data Mining Identifying B of A's Business Challenge; Applying Data Mining; Acting on the Results; Measuring the Effects of Data Mining; Steps of the Virtuous Cycle; Identify Business Opportunities; Transform Data into Information; Act on the Information; Measure the Results; Data Mining in the Context of the Virtuous Cycle; Lessons Learned; Chapter 2 Data Mining Applications in Marketing and Customer Relationship Management; Two Customer Lifecycles; The Customer's Lifecycle; The Customer Lifecycle; Subscription Relationships versus Event-Based Relationships 327 $aEvent-Based Relationships Subscription-Based Relationships; Organize Business Processes Around the Customer Lifecycle; Customer Acquisition; Who Are the Prospects?; When Is a Customer Acquired?; What Is the Role of Data Mining?; Customer Activation; Customer Relationship Management; Winback; Data Mining Applications for Customer Acquisition; Identifying Good Prospects; Choosing a Communication Channel; Picking Appropriate Messages; A Data Mining Example: Choosing the Right Place to Advertise; Who Fits the Profile?; Measuring Fitness for Groups of Readers 327 $aData Mining to Improve Direct Marketing Campaigns Response Modeling; Optimizing Response for a Fixed Budget; Optimizing Campaign Profitability; Reaching the People Most Influenced by the Message; Using Current Customers to Learn About Prospects; Start Tracking Customers Before They Become "Customers"; Gather Information from New Customers; Acquisition-Time Variables Can Predict Future Outcomes; Data Mining Applications for Customer Relationship Management; Matching Campaigns to Customers; Reducing Exposure to Credit Risk; Predicting Who Will Default; Improving Collections 327 $aDetermining Customer Value Cross-selling, Up-selling, and Making Recommendations; Finding the Right Time for an Offer; Making Recommendations; Retention; Recognizing Attrition; Why Attrition Matters; Different Kinds of Attrition; Different Kinds of Attrition Model; Predicting Who Will Leave; Predicting How Long Customers Will Stay; Beyond the Customer Lifecycle; Lessons Learned; Chapter 3 The Data Mining Process; What Can Go Wrong?; Learning Things That Aren't True; Patterns May Not Represent Any Underlying Rule; The Model Set May Not Reflect the Relevant Population 327 $aData May Be at the Wrong Level of Detail 330 $aThe leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990's, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition?more than 50% new and revised?is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more Provides best practices for performing data mining using simple tools such as Excel Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results. 606 $aData mining 606 $aMarketing$xData processing 606 $aBusiness$xData processing 615 0$aData mining. 615 0$aMarketing$xData processing. 615 0$aBusiness$xData processing. 676 $a658.802 686 $a675$2njb/09 686 $a007.6$2njb/09 700 $aLinoff$b Gordon$0145033 701 $aBerry$b Micahel J. A$01621971 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910823349203321 996 $aData mining techniques$94303855 997 $aUNINA