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Data Mining Techniques in CRM

Data Mining Techniques in CRM Author Konstantinos K. Tsiptsis
ISBN-10 9781119965459
Release 2011-08-24
Pages 372
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This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.



Data Mining Techniques in CRM

Data Mining Techniques in CRM Author Konstantinos K. Tsiptsis
ISBN-10 0470743972
Release 2010-03-01
Pages 372
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This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.



Data Mining Techniques in CRM

Data Mining Techniques in CRM Author Konstantinos Tsiptsis
ISBN-10 9780470685822
Release 2010-01-21
Pages 372
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This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.



Effective CRM Using Predictive Analytics

Effective CRM Using Predictive Analytics Author Antonios Chorianopoulos
ISBN-10 9781119011552
Release 2016-01-19
Pages 392
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A step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes. Additionally, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications. Key Features: Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues. Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Accompanied by a website featuring material from each case study, including datasets and relevant code. Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers. The book will also be useful to academics and students interested in applied data mining.



Data Mining Techniques

Data Mining Techniques Author Gordon S. Linoff
ISBN-10 1118087453
Release 2011-03-23
Pages 888
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The 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 1990s, 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.



Segmentation and Lifetime Value Models Using SAS

Segmentation and Lifetime Value Models Using SAS Author Edward C. Malthouse
ISBN-10 9781612907062
Release 2013-07-18
Pages 182
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Help your organization determine the value of its customer relationships with Segmentation and Lifetime Value Models Using SAS. This book contains a wealth of information that will help you perform analyses to identify your customers and make informed marketing investments. It answers core questions on customer relationship management (CRM), provides an overall framework for thinking about CRM, and offers real-world examples across a variety of industries. Edward C. Malthouse introduces you to a number of useful models, ranging from simple to more complicated examples, and discusses their applications. You'll learn about segmentation models for identifying groups of customers and about lifetime value models for estimating the future value of the segments. You'll learn how to prepare data and estimate models using Base SAS, SAS/STAT, SAS/IML, and SQL. Marketing analysts, CRM analysts, database managers, and anyone looking to address the challenges of allocating marketing resources to different customer groups will benefit from the concepts and exercises in this book. Analysts will learn how to approach unique business problems. Managers will gain a sense of what's possible and what to ask of their analytics departments. This book is part of the SAS Press program.



Customer and Business Analytics

Customer and Business Analytics Author Daniel S. Putler
ISBN-10 9781498759700
Release 2015-09-15
Pages 315
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Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the text is ideal for students in customer and business analytics or applied data mining as well as professionals in small- to medium-sized organizations. The book offers an intuitive understanding of how different analytics algorithms work. Where necessary, the authors explain the underlying mathematics in an accessible manner. Each technique presented includes a detailed tutorial that enables hands-on experience with real data. The authors also discuss issues often encountered in applied data mining projects and present the CRISP-DM process model as a practical framework for organizing these projects. Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities.



Data Mining for Business Analytics

Data Mining for Business Analytics Author Galit Shmueli
ISBN-10 9781118879337
Release 2017-09-12
Pages 574
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Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: • Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government • Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students • More than a dozen case studies demonstrating applications for the data mining techniques described • End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented • A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions www.dataminingbook.com Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “ This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 publications including books. Peter C. Bruce is President and Founder of the Institute for Statistics Education at Statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O’Reilly). Inbal Yahav, PhD, is Professor at the Graduate School of Business Administration at Bar-Ilan University, Israel. She teaches courses in social network analysis, advanced research methods, and software quality assurance. Dr. Yahav received her PhD in Operations Research and Data Mining from the University of Maryland, College Park. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. Kenneth C. Lichtendahl, Jr., PhD, is Associate Professor at the University of Virginia. He is the Eleanor F. and Phillip G. Rust Professor of Business Administration and teaches MBA courses in decision analysis, data analysis and optimization, and managerial quantitative analysis. He also teaches executive education courses in strategic analysis and decision-making, and managing the corporate aviation function.



Optimal Database Marketing

Optimal Database Marketing Author Ronald G Drozdenko
ISBN-10 0761923578
Release 2002-03-26
Pages 398
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′This book is accessible and highly readable, with an uncomplicated writing style_ in short this is a book that unravels the mysteries of a vital but "back room" activity′ - International Journal of Market Research `This book is well written with interesting examples and case studies that both illustrate complex techniques and tie the chapters together. The level of detail and treatment of statistical tools and methods provides both understanding and enough detail to begin to use them immediately to target marketing efforts efficiently and effectively. It is perfect for a course in database marketing or as a handy reference for those in the industry′ - C. Samuel Craig, New York University, Stern School of Business `This book should be studied by all who aspire to have a career in direct marketing. It provides a thorough overview of all essential aspects of using customer databases to improve direct marketing results. The material is presented in a style that renders even the technical subjects understandable to the novice direct marketer′ - Kari Regan, Vice President, Database Marketing Services, The Reader′s Digest Association `Finally, practical information on database marketing that tackles this complex subject but makes it clear enough for the novice to understand. This book serves as more than a primer for any senior manager who needs to know the whole story. As one who has spent over 20 years of his career involved in publishing and database marketing, I have a real appreciation for how difficult it is to explain the finer points of this discipline, while keeping it understandable. This book does that admirably. Well done!′ - Patrick E. Kenny, Executive Vice President, Qiosk.com `This book is especially effective in describing the breadth and impact of the database marketing field. I highly recommend this book to anyone who has anything to do with database marketing! -- works in or with this dynamic area′ - Naomi Bernstein, Vice President, BMG Direct This informative book looks at the long-term impact of database marketing techniques on the organization, customers, prospective customers, and society in general. Ron Drozdenko and Perry Drake help the reader gain a thorough understanding of how to properly establish and use databases in order to build strong relationships with customers. There is no other book on the market today that reveals this level of detail regarding database marketing applications - the how′s, why′s and when′s. Appropriate for advanced undergraduate and graduate courses, the book moves from general concepts and examples of marketing databases to more specific data analysis techniques used to enhance the marketing process. This work: - Draws on numerous examples from real businesses - Includes applications to all direct marketing media including the Internet - Describes in step-by-step detail how databases are developed, maintain and mined - Considers both business and social issues of marketing databases - Contains a sample database allowing the reader to apply the mining techniques - Offers access to comprehensive package of academic support materials



Designing a Data Warehouse

Designing a Data Warehouse Author Chris Todman
ISBN-10 0130897124
Release 2001
Pages 323
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The complete guide to building tomorrow's CRM-focused data warehouses. A complete methodology for building CRM-focused data warehouses Planning, ROI, conceptual and logical models, physical implementation, project management, and beyond For database developers, architects, consultants, project managers, and decision-makers Today's next-generation data warehouses are being built with a clear goal: to maximize the power of Customer Relationship Management. To make CRM-focused data warehousing work, you need new techniques, and new methodologies. In this book, Dr. Chris Todman—one of the world's leading data warehouse consultants—delivers the first start-to-finish methodology for defining, designing, and implementing CRM-focused data warehouses. Todman covers all this, and more: Critical design challenges unique to CRM-focused data warehousing A new look at data warehouse conceptual models, logical models, and physical implementation The crucial implications of time in data warehouse modeling and querying Project management: deliverables, assumptions, risks, and team-building—including a full breakdown of work Estimating the ROI of CRM-focused data warehouses up front Choosing software for loading, extraction, transformation, querying, data mining, campaign management, personalization, and metadata DW futures: temporal databases, OLAP SQL extensions, active decision support, integrating external and unstructured data, search agents, and more If you want to leverage the full power of your CRM system, you need a data warehouse designed for the purpose. One book shows you exactly how to build one:Designing Data Warehousesby Dr. Chris Todman.



Data Mining Cookbook

Data Mining Cookbook Author Olivia Parr Rud
ISBN-10 9780471437512
Release 2001-06-15
Pages 416
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Increase profits and reduce costs by utilizing this collection of models of the most commonly asked data mining questions In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be able to mine company databases. This book provides a step-by-step guide to creating and implementing models of the most commonly asked data mining questions. Readers will learn how to prepare data to mine, and develop accurate data mining questions. The author, who has over ten years of data mining experience, also provides actual tested models of specific data mining questions for marketing, sales, customer service and retention, and risk management. A CD-ROM, sold separately, provides these models for reader use.



Customer Segmentation and Clustering Using SAS Enterprise Miner Second Edition

Customer Segmentation and Clustering Using SAS Enterprise Miner  Second Edition Author Randy Collica
ISBN-10 9781612900926
Release 2011-11-15
Pages 378
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In Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition, Randy Collica employs SAS Enterprise Miner and the most commonly available techniques for customer relationship management (CRM). You will learn how to segment customers more intelligently and to achieve, or at least get closer to, the one-to-one customer relationship that today's businesses want. Step-by-step examples and exercises clearly illustrate the concepts of segmentation and clustering in the context of CRM. The book is divided into four parts. Part 1 reviews the basics of segmentation and clustering at an introductory level, providing examples from a variety of industries. Part 2 offers an in-depth treatment of segmentation with practical topics such as when and how to update your models and clustering with many attributes. Part 3 goes beyond traditional segmentation practices to introduce recommended strategies for clustering product affinities, handling missing data, and incorporating textual records into your predictive model with SAS Text Miner software. Part 4 takes segmentation to a new level with advanced techniques such as clustering of product associations, developing segmentation scoring models from customer survey data, combining segmentations using ensemble segmentation, and segmentation of customer transactions. Updates to the second edition include four new chapters in Part 4, Chapters 13-16, that introduce new and advanced analytic techniques that can be valuable in many customer segmentation applications. In addition, Chapter 9 has a new section on using the Imputation node in SAS Enterprise Miner to accomplish missing data imputation, compared to PROC MI used in earlier sections of Chapter 9. Also included are business insights and motivations for selection settings and analytical decisions on many of the examples included in this second edition. This straightforward guide will appeal to anyone who seeks to better understand customers or prospective customers. Additionally, professors and students will find the book well suited for a business data mining analytics course in an MBA program or related course of study. You should understand basic statistics, but no prior knowledge of data mining or SAS Enterprise Miner is required. This book is part of the SAS Press program.



Computer Networks

Computer Networks Author Andrzej Kwiecien
ISBN-10 9783642388651
Release 2013-05-27
Pages 594
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This book constitutes the refereed proceedings of the 20th International Conference on Computer Networks, CN 2013, held in Lwowek Slaski, Poland, in June 2013. The 58 revised full papers presented were carefully reviewed and selected for inclusion in the book. The papers in these proceedings cover the following topics: computer networks, network architectural issues, Internet and wireless solutions, teleinformatics and communications, new technologies, queueing theory and queueing networks, innovative applications, networking in e-business, security aspects of hardware and software, industrial systems, quantum and bio-informatics, cloud networking and services.



Building Data Mining Applications for CRM

Building Data Mining Applications for CRM Author Alex Berson
ISBN-10 UVA:X004393307
Release 2000
Pages 510
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How data mining delivers a powerful competitive advantage! Are you fully harnessing the power of information to support management and marketing decisions? You will, with this one-stop guide to choosing the right tools and technologies for a state-of-the-art data management strategy built on a Customer Relationship Management (CRM) framework. Authors Alex Berson, Stephen Smith, and Kurt Thearling help you understand the principles of data warehousing and data mining systems, and carefully spell out techniques for applying them so that your business gets the biggest pay-off possible. Find out about Online Analytical Processing (OLAP) tools that quickly navigate within your collected data. Explore privacy and legal issues...evaluate current data mining application packages...and let real-world examples show you how data mining can impact -- and improve -- all of your key business processes. Start uncovering your best prospects and offering them the products they really want (not what you think they want)!



Data Mining Applications for Empowering Knowledge Societies

Data Mining Applications for Empowering Knowledge Societies Author Rahman, Hakikur
ISBN-10 9781599046594
Release 2008-07-31
Pages 356
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Presents an overview of the main issues of data mining, including its classification, regression, clustering, and ethical issues. Provides readers with knowledge enhancing processes as well as a wide spectrum of data mining applications.



Data Mining in Agriculture

Data Mining in Agriculture Author Antonio Mucherino
ISBN-10 9780387886152
Release 2009-09-22
Pages 274
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Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®.



Social Network Mining Analysis and Research Trends Techniques and Applications

Social Network Mining  Analysis  and Research Trends  Techniques and Applications Author Ting, I-Hsien
ISBN-10 9781613505144
Release 2011-12-31
Pages 501
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"This book covers current research trends in the area of social networks analysis and mining, sharing research from experts in the social network analysis and mining communities, as well as practitioners from social science, business, and computer science"--Provided by publisher.