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| data mining in marketing: Data Mining Techniques Michael J. A. Berry, Gordon S. Linoff, 2004-04-14 Packed with more than forty percent new and updated material,this edition shows business managers, marketing analysts, and datamining specialists how to harness fundamental data mining methodsand techniques to solve common types of business problems Each chapter covers a new data mining technique, and then showsreaders how to apply the technique for improved marketing, sales,and customer support The authors build on their reputation for concise, clear, andpractical explanations of complex concepts, making this book theperfect introduction to data mining More advanced chapters cover such topics as how to prepare datafor analysis and how to create the necessary infrastructure fordata mining Covers core data mining techniques, including decision trees,neural networks, collaborative filtering, association rules, linkanalysis, clustering, and survival analysis |
| data mining in marketing: Data Mining for Design and Marketing Yukio Ohsawa, Katsutoshi Yada, 2009-01-26 Data Mining for Design and Marketing shows how to design and integrate data mining tools into human thinking processes in order to make better business decisions, especially in designing and marketing products and systems. The expert contributors discuss how data mining can identify valuable consumer patterns, which aid marketers and designers in detecting consumers’ needs. They also explore visualization tools based on the computational methods of data mining. Discourse analysis, chance discovery, knowledge discovery, formal concept analysis, and an adjacency matrix are just some of the novel approaches covered. The book explains how these methods can be applied to website design, the retrieval of scientific articles from a database, personalized e-commerce support tools, and more. Through the techniques of data mining, this book demonstrates how to effectively design business processes and develop competitive products and services. By embracing data mining tools, businesses can better understand the behavior and needs of their customers. |
| data mining in marketing: Optimal Database Marketing Ronald G Drozdenko, Perry D Drake, 2002-03-26 Destined to be the definitive guide to database marketing applications, analytical strategies and test design. - Brian Kurtz, Executive Vice President, Boardroom Inc., 2000 DMA List Leader of the Year and DMA Circulation Hall of Fame Inductee 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 Ron Drozdenko and Perry Drake have written a guide to database marketing that is thorough and that covers the subject in considerable depth. It presents both the concepts underlying database marketing efforts and the all-important quantitative reasoning behind it. The material is accessible to students and practitioners alike and will be an important contribution to improved understanding of this important marketing discipline. Mary Lou Roberts, Boston University and author of Direct Marketing Management I think it is a terrific database marketing book, it′s got it all in clear and logical steps. The benefit to the marketing student and professional is that complex database concepts are carefully developed and thoroughly explained. This book is a must for all marketing managers in understanding database issues to successfully manage and structure marketing programs and achieve maximum results. - Dante Cirille, DMEF Board Member and Retired President, Grolier Direct Marketing An excellent book on the principles of Direct Marketing and utilization of the customer database to maximize profits. It is one of the best direct marketing books I have seen in years in that it is broad with specific examples. I am going to require new hires to read this (book) to get a better understanding of the techniques used in Database Marketing. - Peter Mueller, Assistant Vice President of Analysis, Scholastic, Grolier Division This is an amazingly useful book for direct marketers on how to organize and analyze database information. It′s full of practical examples that make the technical material easy to understand and apply by yourself. I strongly recommend this book to direct and interactive marketers who want to be able to perform professional database analyses themselves, or be better equipped to review the work of analysts. - Pierre A. Passavant, Professor of Direct Marketing, Mercy College and Past Director, Center for Direct Marketing, New York University The most useful database marketing reference guide published today. The authors do an excellent job of laying out all the steps required to plan and implement an effective database marketing strategy in a clear and concise manner. A must have for academics, marketing managers and business executives. - Dave Heneberry, Director, Direct Marketing Certificate programs, Western Connecticut State University and Past Chair, Direct Marketing Association This book is essential for all direct marketers. It serves as a great introduction to the technical and statistical side of database marketing. It provides the reader with enough information on database marketing and statistics to effectively apply the techniques discussed or manage others in the environment - Richard Hochhauser, President, Harte-Hanks Direct Marketing Ronald G. Drozdenko, Ph.D., is Professor and Chair of the Marketing Department, Ancell School of Business, Western Connecticut State University. He is also the founding Director of the Center for Business Research at the Ancell School. He has more than 25 years of teaching experience. The courses he teaches include Strategic Marketing Databases, Interactive/Direct Marketing Management, Product Management, Marketing Research, and Consumer Behavior. He is collaborating with the Direct Marketing Education foundation to develop a model curriculum for universities pursing the area of interactive or direct marketing. Working with an advisory board of industry experts, he co-developed the Marketing Database course in model curriculum. Dr. Drozdenko has co-directed more than 100 proprietary research projects since 1978 for the marketing and research and development of several corporations, including major multinationals. These projects were in the areas of strategic planning, marketing research, product development, direct marketing, and marketing database analysis. He also has published several articles and book chapters. He holds a Ph.D. in Experimental Psychology from the University of Missouri and is a member of the American Marketing Association, the Society for Consumer Psychology, and the Academy of Marketing Sciences. He is also the co-inventor on three U.S. patents. Perry D. Drake has been involved in the direct marketing industry for nearly 15 years. He is currently the Vice President of Drake Direct, a database marketing consulting firm specializing in response modeling, customer file segmentation, lifetime value analysis, customer profiling, database consulting, and market research. Prior to this, Perry worked for approximately 11 years in a variety of quantitative roles at The Reader′s Digest Association, most recently as the Director of Marketing Services. In addition to consulting, Perry has taught at New York University in the Direct Marketing Master′s Degree program since Fall, 1998, currently teaching Statistics for Direct Marketers and Database Modeling. Perry was the recipient of the NYU Center for Direct and Interactive Marketing′s 1998-1999 Outstanding Master′s Faculty Award. Perry also lectures on testing and marketing financials for Western Connecticut State University′s Interactive Direct Marketing Certificate Program. Along with Ron, he is collaborating with the Direct Marketing Education Foundation to develop a model curriculum for universities pursuing the area of interactive or direct marketing. Perry earned a Masters of Science in Applied Statistics from the University of Iowa and a Bachelor of Science in Economics from the University of Missouri. The book evolved from an outlined developed by an advisory board of industry experts that was established by the Direct Marketing Educational Foundation. Contemporary direct marketing and e-commerce could not exist without marketing databases. Databases allow marketers to reach customers and cultivate relationships more effectively and efficiently. While databases provide a means to establish and enhance relationships, they can also be used incorrectly, inefficiently, and unethically. This book looks beyond the temptation of the quick sale to consider 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 not another book on the market today that reveals the level of detail regarding database marketing applications - the how′s, why′s and when′s. Features/Benefits: 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, maintained, 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 |
| data mining in marketing: Data Mining Cookbook Olivia Parr Rud, 2001-06-01 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. |
| data mining in marketing: Data Mining and Market Intelligence for Optimal Marketing Returns Susan Chiu, Domingo Tavella, 2008 Shows how marketing research and data mining techniques will boost return on investment. |
| data mining in marketing: Data Mining Techniques in CRM Konstantinos K. Tsiptsis, Antonios Chorianopoulos, 2011-08-24 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 in marketing: Customer and Business Analytics Daniel S. Putler, Robert E. Krider, 2012-05-07 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 tex |
| data mining in marketing: Handbuch Data Mining im Marketing Hajo Hippner, 2001-01 |
| data mining in marketing: Predictive Analytics for Marketers Barry Leventhal, 2018-02-03 Predictive analytics has revolutionized marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current data and make predictions about unknown future events. In business terms, this enables companies to forecast consumer behaviour and much more. Predictive Analytics for Marketers will guide marketing professionals on how to apply predictive analytical tools to streamline business practices. Including comprehensive coverage of an array of predictive analytic tools and techniques, this book enables readers to harness patterns from past data, to make accurate and useful predictions that can be converted to business success. Truly global in its approach, the insights these techniques offer can be used to manage resources more effectively across all industries and sectors. Written in clear, non-technical language, Predictive Analytics for Marketers contains case studies from the author's more than 25 years of experience and articles from guest contributors, demonstrating how predictive analytics can be used to successfully achieve a range of business purposes. |
| data mining in marketing: Statistical Modeling and Analysis for Database Marketing Bruce Ratner, 2003-05-28 Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses. Statistical Modeling and Analysis fo |
| data mining in marketing: Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence Trivedi, Shrawan Kumar, Dey, Shubhamoy, Kumar, Anil, Panda, Tapan Kumar, 2017-02-14 The development of business intelligence has enhanced the visualization of data to inform and facilitate business management and strategizing. By implementing effective data-driven techniques, this allows for advance reporting tools to cater to company-specific issues and challenges. The Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence is a key resource on the latest advancements in business applications and the use of mining software solutions to achieve optimal decision-making and risk management results. Highlighting innovative studies on data warehousing, business activity monitoring, and text mining, this publication is an ideal reference source for research scholars, management faculty, and practitioners. |
| data mining in marketing: Data Mining Robert Groth, 2000 PLEASE PROVIDE COURSE INFORMATION PLEASE PROVIDE |
| data mining in marketing: Contemporary Perspectives in Data Mining, Volume 2 Kenneth D. Lawrence, Ronald Klimberg, 2015-07-01 The series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner. Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups. Data mining applications are in marketing (customer loyalty, identifying profitable customers, instore promotions, e-commerce populations); in business (teaching data mining, efficiency of the Chinese automobile industry, moderate asset allocation funds); and techniques (veterinary predictive models, data integrity in the cloud, irregular pattern detection in a mobility network and road safety modeling.) |
| data mining in marketing: Data Mining for Design and Marketing Yukio Ohsawa, Katsutoshi Yada, 2009-01-26 Data Mining for Design and Marketing shows how to design and integrate data mining tools into human thinking processes in order to make better business decisions, especially in designing and marketing products and systems.The expert contributors discuss how data mining can identify valuable consumer patterns, which aid marketers and designers in de |
| data mining in marketing: A Practical Guide to Data Mining for Business and Industry Andrea Ahlemeyer-Stubbe, Shirley Coleman, 2014-03-31 Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest. |
| data mining in marketing: Visual Data Mining Tom Soukup, Ian Davidson, 2002-09-18 Marketing analysts use data mining techniques to gain a reliable understanding of customer buying habits and then use that information to develop new marketing campaigns and products. Visual mining tools introduce a world of possibilities to a much broader and non-technical audience to help them solve common business problems. Explains how to select the appropriate data sets for analysis, transform the data sets into usable formats, and verify that the sets are error-free Reviews how to choose the right model for the specific type of analysis project, how to analyze the model, and present the results for decision making Shows how to solve numerous business problems by applying various tools and techniques Companion Web site offers links to data visualization and visual data mining tools, and real-world success stories using visual data mining |
| data mining in marketing: Applied Data Mining Paolo Giudici, 2005-09-27 Data mining can be defined as the process of selection, explorationand modelling of large databases, in order to discover models andpatterns. The increasing availability of data in the currentinformation society has led to the need for valid tools for itsmodelling and analysis. Data mining and applied statistical methodsare the appropriate tools to extract such knowledge from data.Applications occur in many different fields, including statistics,computer science, machine learning, economics, marketing andfinance. This book is the first to describe applied data mining methodsin a consistent statistical framework, and then show how they canbe applied in practice. All the methods described are eithercomputational, or of a statistical modelling nature. Complexprobabilistic models and mathematical tools are not used, so thebook is accessible to a wide audience of students and industryprofessionals. The second half of the book consists of nine casestudies, taken from the author's own work in industry, thatdemonstrate how the methods described can be applied to realproblems. Provides a solid introduction to applied data mining methods ina consistent statistical framework Includes coverage of classical, multivariate and Bayesianstatistical methodology Includes many recent developments such as web mining,sequential Bayesian analysis and memory based reasoning Each statistical method described is illustrated with real lifeapplications Features a number of detailed case studies based on appliedprojects within industry Incorporates discussion on software used in data mining, withparticular emphasis on SAS Supported by a website featuring data sets, software andadditional material Includes an extensive bibliography and pointers to furtherreading within the text Author has many years experience teaching introductory andmultivariate statistics and data mining, and working on appliedprojects within industry A valuable resource for advanced undergraduate and graduatestudents of applied statistics, data mining, computer science andeconomics, as well as for professionals working in industry onprojects involving large volumes of data - such as in marketing orfinancial risk management. |
| data mining in marketing: Data Mining for Marketing Hina Kanth, Aiman Mushtaq, Rafi Ahmad Khan, 2015-04-27 Research Paper from the year 2015 in the subject Business economics - Marketing, Corporate Communication, CRM, Market Research, Social Media, The University of Kashmir, language: English, abstract: This paper gives a brief insight about data mining, its process and the various techniques used for it in the field of marketing. Data mining is the process of extracting hidden valuable information from the data in given data sets .In this paper cross industry standard procedure for data mining is explained along with the various techniques used for it. With growing volume of data every day, the need for data mining in marketing is also increasing day by day. It is a powerful technology to help companies focus on the most important information in their data warehouses. Data mining is actually the process of collecting data from different sources and then interpreting it and finally converting it into useful information which helps in increasing the revenue, curtailing costs thereby providing a competitive edge to the organisation. |
| data mining in marketing: Data Analysis, Machine Learning and Applications Christine Preisach, Hans Burkhardt, Lars Schmidt-Thieme, Reinhold Decker, 2008-04-13 Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007. |
| data mining in marketing: Data Mining and Data Based Direct Marketing Activities T. Brüggemann, P. Hedström, M. Josefsson, 2007-08-22 Master's Thesis from the year 2004 in the subject Business economics - Offline Marketing and Online Marketing, grade: 1,7 (A-), Växjö University (School of Management and Economics), course: International Business Environment, language: English, abstract: Widespread changes within business environments in recent years has demanded acquisitions of new tools that are more skilled to cope with new challenges and demands in business. Advances in computer technologies, higher accessibility of computer associated tools and decreased prices of general computer-related products are reasons enough for at least considerations about higher usage of new technologies. Particularly in direct marketing activities discussed technology is called Data Mining. Companies are faced with hosts of data collected in their data repositories. Of course, companies want to make use of their data and aim to discover interesting patterns of knowledge within their data repositories. Direct marketers which can be involved in catalogue marketing, telemarketing or widely known direct-mail marketing are intensive users of Data Mining Technologies. Because of that, the authors strive to do research concerning reasons for and advantages and disadvantages with using Data Mining as support for direct marketing activities. Chapter 1 deals with general information for the reader as support for delving into the topic. The included problem discussion finishes with the final problem formulation of this thesis. Chapter 2 is about the Methodology which includes considerations of Gummesson. The following theoretical part is divided into two major parts, Data Mining and Direct Marketing, which underpin the whole thesis. The authors want to inform the reader about important and sophisticated contents concerning both Data Mining and Direct Marketing. Without overloading the implementations about Data Mining and Direct Marketing, the authors conduct the reader to essential and detailed aspects of both fields for u |
| data mining in marketing: MASTERING DATA MINING: THE ART AND SCIENCE OF CUSTOMER RELATIONSHIP MANAGEMENT Michael J. A. Berry, Gordon S. Linoff, 2008-09-01 Special Features: · Best-in-class data mining techniques for solving critical problems in all areas of business· Explains how to pick the right data mining techniques for specific problems· Shows how to perform analysis and evaluate results· Features real-world examples from across various industry sectors· Companion Web site with updates on data mining products and service providers About The Book: Companies have invested in building data warehouses to capture vast amounts of customer information. The payoff comes with mining or getting access to the data within this information gold mine to make better business decisions. Readers and reviewers loved Berry and Linoff's first book, Data Mining Techniques, because the authors so clearly illustrate practical techniques with real benefits for improved marketing and sales. Mastering Data Mining takes off from there-assuming readers know the basic techniques covered in the first book, the authors focus on how to best apply these techniques to real business cases. They start with simple applications and work up to the most powerful and sophisticated examples over the course of about 20 cases. (Ralph Kimball used this same approach in his highly successful Data Warehouse Toolkit). As with their first book, Mastering Data Mining is sufficiently technical for database analysts, but is accessible to technically savvy business and marketing managers. It should also appeal to a new breed of database marketing managers. |
| data mining in marketing: Data Mining Applications with R Yanchang Zhao, Yonghua Cen, 2013-11-26 Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool. R code, Data and color figures for the book are provided at the RDataMining.com website. - Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries - Presents various case studies in real-world applications, which will help readers to apply the techniques in their work - Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves |
| data mining in marketing: Statistical and Machine-Learning Data Mining: Bruce Ratner, 2017-07-12 Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with. |
| data mining in marketing: Commercial Data Mining David Nettleton, 2014-01-29 Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling. Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book. - Illustrates cost-benefit evaluation of potential projects - Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools - Approachable reference can be read from cover to cover by readers of all experience levels - Includes practical examples and case studies as well as actionable business insights from author's own experience |
| data mining in marketing: The Online Customer Yinghui Yang, 2006 In The Online Customer, Yinghui Yang details how data mining and marketing approaches can be used to study marketing problems. The book uses a vast dataset of web transactions from the largest internet retailers, including Amazon.com. In particular, she deftly shows how to integrate and compare statistical methods from marketing and data mining research. The book comprises two parts. The first part focuses on using behavior patterns for customer segmentation. It advances data mining theory by presenting a novel pattern-based clustering approach to customer segmentation and valuation. The second part of the book explores how free shipping impacts purchase behavior online. It illuminates the importance of shipping policies in a competitive setting. With complete documentation and methodology, this book is a valuable reference that business and Internet Studies scholars can build upon. |
| data mining in marketing: Data Driven Marketing For Dummies David Semmelroth, 2013-10-07 Embrace data and use it to sell and market your products Data is everywhere and it keeps growing and accumulating. Companies need to embrace big data and make it work harder to help them sell and market their products. Successful data analysis can help marketing professionals spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty. Data Driven Marketing For Dummies helps companies use all the data at their disposal to make current customers more satisfied, reach new customers, and sell to their most important customer segments more efficiently. Identifying the common characteristics of customers who buy the same products from your company (or who might be likely to leave you) Tips on using data to predict customer purchasing behavior based on past performance Using customer data and marketing analytics to predict when customers will purchase certain items Information on how data collected can help with merchandise planning Breaking down customers into segments for easier market targeting Building a 360 degree view of a customer base Data Driven Marketing For Dummies assists marketing professionals at all levels of business in accelerating sales through analytical insights. |
| data mining in marketing: Data Mining for Managers R. Boire, 2014-11-17 Big Data is a growing business trend, but there little advice available on how to use it practically. Written by a data mining expert with over 30 years of experience, this book uses case studies to help marketers, brand managers and IT professionals understand how to capture and measure data for marketing purposes. |
| data mining in marketing: Advanced Digital Marketing Strategies in a Data-Driven Era Saura, Jose Ramon, 2021-06-25 In the last decade, the use of data sciences in the digital marketing environment has increased. Digital marketing has transformed how companies communicate with their customers around the world. The increase in the use of social networks and how users communicate with companies on the internet has given rise to new business models based on the bidirectionality of communication between companies and internet users. Digital marketing, new business models, data-driven approaches, online advertising campaigns, and other digital strategies have gathered user opinions and comments through this new online channel. In this way, companies are beginning to see the digital ecosystem as not only the present but also the future. However, despite these advances, relevant evidence on the measures to improve the management of data sciences in digital marketing remains scarce. Advanced Digital Marketing Strategies in a Data-Driven Era contains high-quality research that presents a holistic overview of the main applications of data sciences to digital marketing and generates insights related to the creation of innovative data mining and knowledge discovery techniques applied to traditional and digital marketing strategies. The book analyzes how companies are adopting these new data-driven methods and how these strategies influence digital marketing. Discussing topics such as digital strategies, social media marketing, big data, marketing analytics, and data sciences, this book is essential for marketers, digital marketers, advertisers, brand managers, managers, executives, social media analysts, IT specialists, data scientists, students, researchers, and academicians in the field. |
| data mining in marketing: Principles of Data Mining David J. Hand, Heikki Mannila, Padhraic Smyth, 2001-08-17 The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing. |
| data mining in marketing: Business Modeling and Data Mining Dorian Pyle, 2003-05-17 Business Modeling and Data Mining demonstrates how real world business problems can be formulated so that data mining can answer them. The concepts and techniques presented in this book are the essential building blocks in understanding what models are and how they can be used practically to reveal hidden assumptions and needs, determine problems, discover data, determine costs, and explore the whole domain of the problem. This book articulately explains how to understand both the strategic and tactical aspects of any business problem, identify where the key leverage points are and determine where quantitative techniques of analysis -- such as data mining -- can yield most benefit. It addresses techniques for discovering how to turn colloquial expression and vague descriptions of a business problem first into qualitative models and then into well-defined quantitative models (using data mining) that can then be used to find a solution. The book completes the process by illustrating how these findings from data mining can be turned into strategic or tactical implementations. · Teaches how to discover, construct and refine models that are useful in business situations· Teaches how to design, discover and develop the data necessary for mining · Provides a practical approach to mining data for all business situations· Provides a comprehensive, easy-to-use, fully interactive methodology for building models and mining data· Provides pointers to supplemental online resources, including a downloadable version of the methodology and software tools. |
| data mining in marketing: Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences John J. McArdle, Gilbert Ritschard, 2013-08-15 This book reviews the latest techniques in exploratory data mining (EDM) for the analysis of data in the social and behavioral sciences to help researchers assess the predictive value of different combinations of variables in large data sets. Methodological findings and conceptual models that explain reliable EDM techniques for predicting and understanding various risk mechanisms are integrated throughout. Numerous examples illustrate the use of these techniques in practice. Contributors provide insight through hands-on experiences with their own use of EDM techniques in various settings. Readers are also introduced to the most popular EDM software programs. A related website at http://mephisto.unige.ch/pub/edm-book-supplement/offers color versions of the book’s figures, a supplemental paper to chapter 3, and R commands for some chapters. The results of EDM analyses can be perilous – they are often taken as predictions with little regard for cross-validating the results. This carelessness can be catastrophic in terms of money lost or patients misdiagnosed. This book addresses these concerns and advocates for the development of checks and balances for EDM analyses. Both the promises and the perils of EDM are addressed. Editors McArdle and Ritschard taught the Exploratory Data Mining Advanced Training Institute of the American Psychological Association (APA). All contributors are top researchers from the US and Europe. Organized into two parts--methodology and applications, the techniques covered include decision, regression, and SEM tree models, growth mixture modeling, and time based categorical sequential analysis. Some of the applications of EDM (and the corresponding data) explored include: selection to college based on risky prior academic profiles the decline of cognitive abilities in older persons global perceptions of stress in adulthood predicting mortality from demographics and cognitive abilities risk factors during pregnancy and the impact on neonatal development Intended as a reference for researchers, methodologists, and advanced students in the social and behavioral sciences including psychology, sociology, business, econometrics, and medicine, interested in learning to apply the latest exploratory data mining techniques. Prerequisites include a basic class in statistics. |
| data mining in marketing: Data Mining Your Website Jesus Mena, 1999-07-15 Turn Web data into knowledge about your customers. This exciting book will help companies create, capture, enhance, and analyze one of their most valuable new sources of marketing information-usage and transactional data from a website. A company's website is a primary point of contact with its customers and a medium in which visitor's actions are messages about who they are and what they want. Data Mining Your Website will teach you the tools, techniques, and technologies you'll need to profile current and potential customers and predict on-line interests and behavior. You'll learn how to extract from the huge pools of information your website generates, insights into on-line buying patterns, and how to apply this knowledge to design a website that better attracts, engages, and retains on-line customers. Data Mining Your Website explains how data mining is a foundation for the new field of web-based, interactive retailing, marketing, and advertising. This innovative book will help web developers and marketers, webmasters, and data management professionals harness powerful new tools and processes. The first book to apply data mining specifically to e-commerce Learn effective methods for gathering, managing, and mining Web customer information Use data mining to profile customers and create personalized e-commerce programs |
| data mining in marketing: Collaborative Filtering Using Data Mining and Analysis Bhatnagar, Vishal, 2016-07-13 Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material. Collaborative Filtering Using Data Mining and Analysis evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices. Featuring emergent research and optimization techniques in the areas of opinion mining, text mining, and sentiment analysis, as well as their various applications, this book is an essential reference source for researchers and engineers interested in collaborative filtering. |
| data mining in marketing: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data mining in marketing: Introduction to Data Mining and its Applications S. Sumathi, S.N. Sivanandam, 2006-10-12 This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. |
| data mining in marketing: Data Mining and Knowledge Discovery Handbook Oded Maimon, Lior Rokach, 2006-05-28 Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management. |
| data mining in marketing: Marketing Analytics Wayne L. Winston, 2014-01-08 Helping tech-savvy marketers and data analysts solve real-world business problems with Excel Using data-driven business analytics to understand customers and improve results is a great idea in theory, but in today's busy offices, marketers and analysts need simple, low-cost ways to process and make the most of all that data. This expert book offers the perfect solution. Written by data analysis expert Wayne L. Winston, this practical resource shows you how to tap a simple and cost-effective tool, Microsoft Excel, to solve specific business problems using powerful analytic techniques—and achieve optimum results. Practical exercises in each chapter help you apply and reinforce techniques as you learn. Shows you how to perform sophisticated business analyses using the cost-effective and widely available Microsoft Excel instead of expensive, proprietary analytical tools Reveals how to target and retain profitable customers and avoid high-risk customers Helps you forecast sales and improve response rates for marketing campaigns Explores how to optimize price points for products and services, optimize store layouts, and improve online advertising Covers social media, viral marketing, and how to exploit both effectively Improve your marketing results with Microsoft Excel and the invaluable techniques and ideas in Marketing Analytics: Data-Driven Techniques with Microsoft Excel. |
| data mining in marketing: Mining the Web Gordon S. Linoff, Michael J. A. Berry, 2001 Introduces business and technical managers to the exciting new frontier in database technology Web sites gather a lot of detailed information about customers. Unfortunately, most companies lack the means to use that information to improve their marketing and customer support functions. Considered by most experts to be the new frontier in the database and data warehousing fields, Web mining solves that problem. Coauthored by two bestselling data mining authors, Mining the Web explains, for corporate decision makers, IT managers, and database marketers, how data mining principles and techniques can be applied to various types of Web sites. More importantly, they describe techniques for using the resulting goldmine of business data to develop more effective advertising campaigns and better customer service. |
| data mining in marketing: Predictive Analytics and Data Mining Vijay Kotu, Bala Deshpande, 2014-11-27 Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining.You’ll be able to:1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process.2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases.3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner tools Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics Includes practical use cases and examples |
| data mining in marketing: Data Mining and Statistics for Decision Making Stéphane Tufféry, 2011-03-23 Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
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Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
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DATA MINING FOR TARGET MARKETING - Springer
DATA MINING FOR TARGET MARKETING Nissan Levin Q-Ware SofhYare Company, Israel Jacob Zahavi The Wharton School (on leavefrom Tel Aviv Universiry) Abstract Targeting is the …
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Review Application of data mining techniques in customer relationship management: A literature review and classification E.W.T. Ngaia,*, Li Xiub, D.C.K. Chaua a Department of …
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quadratic discriminant analysis, all tested at different levels of data saturation. To illustrate the issues discussed, we built predictive models, which outperform those proposed by other …
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2012). Belakangan ini, data mining sering digunakan pada beberapa industri termasuk asuransi dan perbankan. Penggunaan teknik data mining dalam Bank Direct Marketing bertujuan untuk …
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Data Mining Data mining term is an older term but is gaining importance in today’s world. It is an art of extracting hidden information from the large data sets. Sometimes it can be described as …
Data mining in marketing - ttcenter.ir
Data mining in marketing Thabit Zatari . Abstract: Data mining in marketing is operation of analyzing data from different perspectives in order to summarize and analyze to discover …
Data Mining for Potential Customer Segmentation in the …
Keywords: data mining, bank marketing, SMOTE I. INTRODUCTION A good marketing strategy is needed in the industry to increase profits. The banking industry is no exception,
Marketing Analytics: Methods, Practice, Implementation, and …
academic areas. Contributions in marketing analytics come from a variety of elds including data mining, marketing science, operations research, and statistics. Each discipline has its own …
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procedure of mining knowledge from data. The information or knowledge extracted so can be used for any of the following applications: Market Analysis Fraud Detection Customer …
Data Mining and Predictive Analytics Syllabus - UMD
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Data mining with its role in marketing, sales support and …
Data mining with its role in marketing, sales support and customer identification data analysis Mohammed Bin Ali Al Atif a,1,*, Ahmed H. Shakir b,2, Ahmed Kateb Jumaah Al Nussairi c,3, …
Ethical Considerations in Data Mining: Addressing Bias, …
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Application of Data Mining In Marketing final - arXiv.org
Keywords: Marketing, data mining, decision tree, clustering. 1. Introduction Data mining, as we use the term, is the exploration and analysis of large quantities of data in order to discover …
Data mining techniques for customer relationship …
5. Application Fields Of Data Mining For CRM . Data mining can filter customer information, and mining implicit, unknown and potentially valuable knowledge on business decisions and rules …
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Data Mining: Concepts and Techniques - Veer Surendra Sai …
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Deposit subscribe Prediction using Data Mining Techniques …
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6. Michel Berry and Gordon Linoff, Data mining techniques for Marketing, Sales and Customer support, John Wiley, 2011 7. G. K. Gupta, Ïntroduction to Data mining with Case Studies, …
Business M733 Marketing Analytics Course Outline
Marketing @ DeGroote School of Business, McMaster University . Course Objective Some key words heard frequently in marketing departments today are data science, analytics, …
Handbuch Data Mining im Marketing - gbv.de
2 Der Prozess des Data Mining im Marketing Hajo Hippner, Klaus D. Wilde 1 Aufgabendefinition 22 1.1 Bestimmung der betriebswirtschaftlichen Problemstellung 1.2 Ableitung analytischer …
Data Mining: A Distinctive Approach to CRM - ijsrnsc.org
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Mining important association rules based on the RFMD …
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Data Mining for Customer Relationship Management
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22:198:644:01 Data Mining - Rutgers Business School
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Knowledge management and data mining for marketing
Fig. 1. A taxonomy of data mining tasks. mining task, even as it is used to support other data mining tasks. Different data mining tasks are grouped into categories depending on the type of …
Knowledge management and data mining for marketing
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The Impact of Data Mining on Management and Digital …
The Impact of Data Mining on Management and Digital Marketing in the Age of Big Data Jinghua Liu1* 1School of Linyi Vocational College, Linyi 276000, Shandong, China, …
Data Mining as Tools to Improve Marketing Campaign
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Data Mining During data mining on these three datasets for direct mark eting, w e encoun tered sev eral sp eci c problems. The rst and most ob vious problem is the extremely im balanced …
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Big Data Mining Method of Marketing Management Based …
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Content marketing through data mining on Facebook …
Content marketing through data mining on Facebook social network Saman Forouzandeh Department of Computer Engineering, Kurdistan Science and Research Branch, Islamic Azad …
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A Brief Review on the Knowledge Management and Data …
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Wiley Data Mining Techniques: For Marketing, Sales, and …
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3rd Edition Gordon S. Linoff, Michael J. A. Berry E-Book 978-1-118-08745-9 March 2011 £34.99 …
Decision Focused Causal Learning for Direct Counterfactual …
on historical data. Extrinsic uncertainty is the frequently changing marketing budget, determined by the external environment. An ML model is required to guarantee superior performance …
Introduction to Data Mining and Business Intelligence
• Data mining/analytics is closely related to the fields of database, artificial intelligence, statistics, and information retrieval. But there are considerable differences between data mining and …
menggunakan K-Means Clustering Widya Amelia
Keywords: K-Means Clustering è Customer Segmentation è Data Mining è Marketing Pendahuluan Pada masa modern kini pemasaran secara digital dianggap penting untuk …
Mining The Web Transforming Customer Data Into Customer …
business and marketing managers It should also appeal to a new breed of database marketing managers Data Mining Techniques in CRM Konstantinos Tsiptsis,Antonios …
Knowledge management and data mining for marketing
Fig. 1. A taxonomy of data mining tasks. mining task, even as it is used to support other data mining tasks. Different data mining tasks are grouped into categories depending on the type of …
