[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-113994-en":3,"doc-seo-113994-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},113994,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Data Mining for Managers - How to Use Data (Big and Small) to Solve Business Challenges","A practical guide for managers on applying data mining to address real business challenges using both big and small datasets. It walks through the complete data mining lifecycle—from identifying problems and building analytical files to incorporating external data sources, securing stored data, and addressing privacy concerns. The book also covers data quality, segmentation, and applying common modeling and analytics techniques for evaluation, tracking, implementation, and value-based decisions, including case-focused scenarios across industries.","UTSA Libraries己DDDD DDD5b2b5吕  \nhttps://archive.org/details/dataminingforman0000boir  \n1  \n# DATA MINING FOR MANAGERS\n\nWITHDRAWNUTSALibranies  \n# DATA MINING FOR MANAGERS\n\nHoW TO USE DATA(BIG ANDSMALL)TO SOLVE BUSINESSCHALLENGES  \nRICHARD BOIRE  \n。口  \nDATA MINING FOR MANAGERSCopyright ◎ Richard Boire,2014.  \nAll rights reserved.  \nFirst published in 2014 byPALGRAVE MACMILLAN⑧  \nin the United States—a division of St.Martin's Press LLC,175 Fifth Avenue,New York,NY 10010.  \nWhere this book is distributed in the UK,Europe and the rest of the world,this is by Palgrave Macmillan,a division of Macmillan Publishers Limited,registered in England,company number 785998,of Houndmills,Basingstoke,Hampshire RG216XS.  \nPalgrave Macmillan is the global academic imprint of the above companiesand has companies and representatives throughout the world.  \nPalgrave⑧and Macmillan⑧are registered trademarks in the United States,the United Kingdom,Europe and other countries.  \nISBN:978-1-137-40617-0  \nLibrary of Congress Cataloging-in-Publication Data  \nBoire,Richard.  \nData mining for managers:how to use data(big and small)to solvebusiness challenges/Richard Boire.  \npages cm  \nISBN 978-1-137-40617-0(alk.paper)  \n1.Management—Computer programs.2.Data mining.1.Title.  \nHD30.2.B63732014658'.056312—dc23  \n2014015559  \nA catalogue record of the book is available from the British Library  \nDesign by Newgen Knowledge Works(P)Ltd.,Chennai,India.  \nFirst edition:October 2014  \n10987654321  \nPrinted in the United States of America.  \nLibrary  \nUniversity of Texasat San Antonio  \nCONTENTS  \nList of FiguresviiForewordxiAcknowledgmentsxiii  \n1.Introduction1  \n2.Growth of Data Mining—An Historical Perspective7  \n3.Data Mining in the New Economy15  \n4.Using Data Mining for CRM Evaluation23  \n5.The Data Mining Process:Problem Identification  \n29  \n6.The Data Mining Process:Creation of theAnalytical File41  \n7.Data Mining Process:Creation of the Analytical Filewith External Data Sources  \n59  \n8.Data Storage and Security  \n63  \n9.Privacy Concerns Regarding the Use of Data65  \n10.Types and Quality of Data  \n75  \n11.Segmentation83  \n12.Applying Data Mining Techniques  \n95  \n13.Gains Charts  \n115  \n14.Using RFM as One Targeting Option121  \n15.The Use of Multivariate Analysis Techniques125  \n133  \n16.Tracking and Measuring  \n141  \n17.Implementation and Tracking  \n143  \n18.Value-Based Segmentation and the Use of CHAID  \n151  \n19.Black Box Analytics  \n155  \n20.Digital Analytics:A Data Miner's Perspective  \n165  \n21.Organizational Considerations:People and Software  \n22.Social Media Analytics181  \n23.Credit Cards and Risk185  \n24.Data Mining in Retail193  \n25.Business-to-Business Example  \n201  \n26.Financial Institution Case Study207  \n27.Using Marketing Analytics in the Travel/EntertainmentIndustry211  \n28.Data Mining for Customer Loyalty:A Perspective  \n215  \n29.Text Mining:The New Data Mining Frontier  \n221  \n30.Analytics and Data Mining for Insurance Claim Risk229  \n31.Future Thoughts:The Big Data Discussion and theKey Roles in Analytics231  \nIndex","cbCairKSo30MLC8z","https://ap.wps.com/l/cbCairKSo30MLC8z","pdf",13283823,1,264,"English","en",105,"# Contents\n## List of Figures\n## Foreword\n## Acknowledgments\n## Introduction\n## Growth of Data Mining—An Historical Perspective\n## Data Mining in the New Economy\n## Using Data Mining for CRM Evaluation\n## The Data Mining Process: Problem Identification\n## The Data Mining Process: Creation of the Analytical File\n## Data Storage and Security\n## Privacy Concerns Regarding the Use of Data\n## Types and Quality of Data\n## Segmentation\n## Applying Data Mining Techniques\n## Gains Charts\n## Using RFM as One Targeting Option\n## The Use of Multivariate Analysis Techniques\n## Tracking and Measuring\n## Implementation and Tracking\n## Value-Based Segmentation and the Use of CHAID\n## Black Box Analytics\n## Digital Analytics: A Data Miner's Perspective\n## Organizational Considerations: People and Software\n## Social Media Analytics\n## Credit Cards and Risk\n## Data Mining in Retail\n## Business-to-Business Example\n## Financial Institution Case Study\n## Text Mining: The New Data Mining Frontier\n## Analytics and Data Mining for Insurance Claim Risk\n## Future Thoughts: The Big Data Discussion and the Key Roles in Analytics\n## Index","[{\"question\":\"What is the core goal of Data Mining for Managers?\",\"answer\":\"To show managers how to use data mining with big and small data to solve practical business challenges, from problem definition to implementation and measurement.\"},{\"question\":\"How does the book structure the data mining workflow?\",\"answer\":\"It guides the reader 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