| cluster analysis customer segmentation: Market Segmentation Analysis Sara Dolnicar, Bettina Grün, Friedrich Leisch, 2018-07-20 This book is published open access under a CC BY 4.0 license. This open access book offers something for everyone working with market segmentation: practical guidance for users of market segmentation solutions; organisational guidance on implementation issues; guidance for market researchers in charge of collecting suitable data; and guidance for data analysts with respect to the technical and statistical aspects of market segmentation analysis. Even market segmentation experts will find something new, including an approach to exploring data structure and choosing a suitable number of market segments, and a vast array of useful visualisation techniques that make interpretation of market segments and selection of target segments easier. The book talks the reader through every single step, every single potential pitfall, and every single decision that needs to be made to ensure market segmentation analysis is conducted as well as possible. All calculations are accompanied not only with a detailed explanation, but also with R code that allows readers to replicate any aspect of what is being covered in the book using R, the open-source environment for statistical computing and graphics. |
| cluster analysis customer segmentation: Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition Randall S. Collica, 2017-03-23 Résumé : A working guide that uses real-world data, this step-by-step resource will show you how to segment customers more intelligently and achieve the one-to-one customer relationship that your business needs. -- |
| cluster analysis customer segmentation: Marketing Strategy Robert W. Palmatier, Shrihari Sridhar, 2021-02-05 Marketing Strategy offers a unique and dynamic approach based on four underlying principles that underpin marketing today: All customers differ; All customers change; All competitors react; and All resources are limited. The structured framework of this acclaimed textbook allows marketers to develop effective and flexible strategies to deal with diverse marketing problems under varying circumstances. Uniquely integrating marketing analytics and data driven techniques with fundamental strategic pillars the book exemplifies a contemporary, evidence-based approach. This base toolkit will support students' decision-making processes and equip them for a world driven by big data. The second edition builds on the first's successful core foundation, with additional pedagogy and key updates. Research-based, action-oriented, and authored by world-leading experts, Marketing Strategy is the ideal resource for advanced undergraduate, MBA, and EMBA students of marketing, and executives looking to bring a more systematic approach to corporate marketing strategies. New to this Edition: - Revised and updated throughout to reflect new research and industry developments, including expanded coverage of digital marketing, influencer marketing and social media strategies - Enhanced pedagogy including new Worked Examples of Data Analytics Techniques and unsolved Analytics Driven Case Exercises, to offer students hands-on practice of data manipulation as well as classroom activities to stimulate peer-to-peer discussion - Expanded range of examples to cover over 250 diverse companies from 25 countries and most industry segments - Vibrant visual presentation with a new full colour design |
| cluster analysis customer segmentation: Data Analysis and Decision Support Daniel Baier, Reinhold Decker, Lars Schmidt-Thieme, 2006-05-06 It is a great privilege and pleasure to write a foreword for a book honor ing Wolfgang Gaul on the occasion of his sixtieth birthday. Wolfgang Gaul is currently Professor of Business Administration and Management Science and the Head of the Institute of Decision Theory and Management Science, Faculty of Economics, University of Karlsruhe (TH), Germany. He is, by any measure, one of the most distinguished and eminent scholars in the world today. Wolfgang Gaul has been instrumental in numerous leading research initia tives and has achieved an unprecedented level of success in facilitating com munication among researchers in diverse disciplines from around the world. A particularly remarkable and unique aspect of his work is that he has been a leading scholar in such diverse areas of research as graph theory and net work models, reliability theory, stochastic optimization, operations research, probability theory, sampling theory, cluster analysis, scaling and multivariate data analysis. His activities have been directed not only at these and other theoretical topics, but also at applications of statistical and mathematical tools to a multitude of important problems in computer science (e.g., w- mining), business research (e.g., market segmentation), management science (e.g., decision support systems) and behavioral sciences (e.g., preference mea surement and data mining). All of his endeavors have been accomplished at the highest level of professional excellence. |
| cluster analysis customer segmentation: Global Perspective for Competitive Enterprise, Economy and Ecology Shuo-Yan Chou, Amy J. C. Trappey, Jerzy Pokojski, Shana Smith, 2009-07-01 Global Perspective for Competitive Enterprise, Economy and Ecology addresses the general theme of the Concurrent Engineering (CE) 2009 Conference – the need for global advancements in the areas of competitive enterprise, economy and ecology. The proceedings contain 84 papers, which vary from the theoretical and conceptual to the practical and industrial. The content of this volume reflects the genuine variety of issues related to current CE methods and phenomena. Global Perspective for Competitive Enterprise, Economy and Ecology will therefore enable researchers, industry practitioners, postgraduate students and advanced undergraduates to build their own view of the inherent problems and methods in CE. |
| cluster analysis customer segmentation: A Concise Guide to Market Research Marko Sarstedt, Erik Mooi, 2014-08-07 This accessible, practice-oriented and compact text provides a hands-on introduction to market research. Using the market research process as a framework, it explains how to collect and describe data and presents the most important and frequently used quantitative analysis techniques, such as ANOVA, regression analysis, factor analysis and cluster analysis. The book describes the theoretical choices a market researcher has to make with regard to each technique, discusses how these are converted into actions in IBM SPSS version 22 and how to interpret the output. Each chapter concludes with a case study that illustrates the process using real-world data. A comprehensive Web appendix includes additional analysis techniques, datasets, video files and case studies. Tags in the text allow readers to quickly access Web content with their mobile device. The new edition features: Stronger emphasis on the gathering and analysis of secondary data (e.g., internet and social networking data) New material on data description (e.g., outlier detection and missing value analysis) Improved use of educational elements such as learning objectives, keywords, self-assessment tests, case studies, and much more Streamlined and simplified coverage of the data analysis techniques with more rules-of-thumb Uses IBM SPSS version 22 |
| cluster analysis customer segmentation: Market Segmentation Michel Wedel, Wagner A. Kamakura, 2012-12-06 Modern marketing techniques in industrialized countries cannot be implemented without segmentation of the potential market. Goods are no longer produced and sold without a significant consideration of customer needs combined with a recognition that these needs are heterogeneous. Since first emerging in the late 1950s, the concept of segmentation has been one of the most researched topics in the marketing literature. Segmentation has become a central topic to both the theory and practice of marketing, particularly in the recent development of finite mixture models to better identify market segments. This second edition of Market Segmentation updates and extends the integrated examination of segmentation theory and methodology begun in the first edition. A chapter on mixture model analysis of paired comparison data has been added, together with a new chapter on the pros and cons of the mixture model. The book starts with a framework for considering the various bases and methods available for conducting segmentation studies. The second section contains a more detailed discussion of the methodology for market segmentation, from traditional clustering algorithms to more recent developments in finite mixtures and latent class models. Three types of finite mixture models are discussed in this second section: simple mixtures, mixtures of regressions and mixtures of unfolding models. The third main section is devoted to special topics in market segmentation such as joint segmentation, segmentation using tailored interviewing and segmentation with structural equation models. The fourth part covers four major approaches to applied market segmentation: geo-demographic, lifestyle, response-based, and conjoint analysis. The final concluding section discusses directions for further research. |
| cluster analysis customer segmentation: 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. |
| cluster analysis customer segmentation: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| cluster analysis customer segmentation: Marketing Analytics Rajkumar Venkatesan, Paul W. Farris, Ronald T. Wilcox, 2021-01-13 The authors of the pioneering Cutting-Edge Marketing Analytics return to the vital conversation of leveraging big data with Marketing Analytics: Essential Tools for Data-Driven Decisions, which updates and expands on the earlier book as we enter the 2020s. As they illustrate, big data analytics is the engine that drives marketing, providing a forward-looking, predictive perspective for marketing decision-making. The book presents actual cases and data, giving readers invaluable real-world instruction. The cases show how to identify relevant data, choose the best analytics technique, and investigate the link between marketing plans and customer behavior. These actual scenarios shed light on the most pressing marketing questions, such as setting the optimal price for one’s product or designing effective digital marketing campaigns. Big data is currently the most powerful resource to the marketing professional, and this book illustrates how to fully harness that power to effectively maximize marketing efforts. |
| cluster analysis customer segmentation: Marketing Intelligent Systems Using Soft Computing Jorge Casillas, Francisco J. Martínez-López, 2010-09-30 Dr. Jay Liebowitz Orkand Endowed Chair in Management and Technology University of Maryland University College Graduate School of Management & Technology 3501 University Boulevard East Adelphi, Maryland 20783-8030 USA jliebowitz@umuc. edu When I first heard the general topic of this book, Marketing Intelligent Systems or what I’ll refer to as Marketing Intelligence, it sounded quite intriguing. Certainly, the marketing field is laden with numeric and symbolic data, ripe for various types of mining—data, text, multimedia, and web mining. It’s an open laboratory for applying numerous forms of intelligentsia—neural networks, data mining, expert systems, intelligent agents, genetic algorithms, support vector machines, hidden Markov models, fuzzy logic, hybrid intelligent systems, and other techniques. I always felt that the marketing and finance domains are wonderful application areas for intelligent systems, and this book demonstrates the synergy between marketing and intelligent systems, especially soft computing. Interactive advertising is a complementary field to marketing where intelligent systems can play a role. I had the pleasure of working on a summer faculty f- lowship with R/GA in New York City—they have been ranked as the top inter- tive advertising agency worldwide. I quickly learned that interactive advertising also takes advantage of data visualization and intelligent systems technologies to help inform the Chief Marketing Officer of various companies. Having improved ways to present information for strategic decision making through use of these technologies is a great benefit. |
| cluster analysis customer segmentation: Segmentation in Social Marketing Timo Dietrich, Sharyn Rundle-Thiele, Krzysztof Kubacki, 2016-10-21 This book brings together current innovative methods and approaches to segmentation and outlines why segmentation is needed to support more effective social marketing program design. It presents a variety of segmentation approaches alongside case studies of their application in various social marketing contexts. The book extends the use of segmentation in social marketing, which will ultimately lead to more effective and better-tailored programs that deliver change for the better. As such, it offers a detailed handbook on how to conduct state-of-the-art segmentation, and provides a valuable resource for academics, social marketers, educators, and advanced students alike. |
| cluster analysis customer segmentation: Intelligent Computing Methodologies De-Shuang Huang, Zhi-Kai Huang, Abir Hussain, 2019-07-24 This two-volume set of LNCS 11643 and LNCS 11644 constitutes - in conjunction with the volume LNAI 11645 - the refereed proceedings of the 15th International Conference on Intelligent Computing, ICIC 2019, held in Nanchang, China, in August 2019. The 217 full papers of the three proceedings volumes were carefully reviewed and selected from 609 submissions. The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” Papers related to this theme are especially solicited, including theories, methodologies, and applications in science and technology. |
| cluster analysis customer segmentation: Innovations in Computer Science and Engineering Harvinder Singh Saini, Rishi Sayal, Rajkumar Buyya, Govardhan Aliseri, 2020-03-03 This book features a collection of high-quality, peer-reviewed research papers presented at the 7th International Conference on Innovations in Computer Science & Engineering (ICICSE 2019), held at Guru Nanak Institutions, Hyderabad, India, on 16–17 August 2019. Written by researchers from academia and industry, the book discusses a wide variety of industrial, engineering, and scientific applications of the emerging techniques in the field of computer science. |
| cluster analysis customer segmentation: Handbook of Market Segmentation Art Weinstein, 2004 This is a practical how-to guide to what marketers need to know about defining, segmenting and targeting business markets: assessing customer needs; gauging the competition; designing winning strategies; and maximising corporate resources. |
| cluster analysis customer segmentation: Accelerating Customer Relationships Ronald S. Swift, 2001 Preface Corporations that achieve high customer retention and high customer profitability aim for: The right product (or service), to the right customer, at the right price, at the right time, through the right channel, to satisfy the customer's need or desire. Information Technology—in the form of sophisticated databases fed by electronic commerce, point-of-sale devices, ATMs, and other customer touch points—is changing the roles of marketing and managing customers. Information and knowledge bases abound and are being leveraged to drive new profitability and manage changing relationships with customers. The creation of knowledge bases, sometimes called data warehouses or Info-Structures, provides profitable opportunities for business managers to define and analyze their customers' behavior to develop and better manage short- and long-term relationships. Relationship Technology will become the new norm for the use of information and customer knowledge bases to forge more meaningful relationships. This will be accomplished through advanced technology, processes centered on the customers and channels, as well as methodologies and software combined to affect the behaviors of organizations (internally) and their customers/channels (externally). We are quickly moving from Information Technology to Relationship Technology. The positive effect will be astounding and highly profitable for those that also foster CRM. At the turn of the century, merchants and bankers knew their customers; they lived in the same neighborhoods and understood the individual shopping and banking needs of each of their customers. They practiced the purest form of Customer Relationship Management (CRM). With mass merchandising and franchising, customer relationships became distant. As the new millennium begins, companies are beginning to leverage IT to return to the CRM principles of the neighborhood store and bank. The customer should be the primary focus for most organizations. Yet customer information in a form suitable for marketing or management purposes either is not available, or becomes available long after a market opportunity passes, therefore CRM opportunities are lost. Understanding customers today is accomplished by maintaining and acting on historical and very detailed data, obtained from numerous computing and point-of-contact devices. The data is merged, enriched, and transformed into meaningful information in a specialized database. In a world of powerful computers, personal software applications, and easy-to-use analytical end-user software tools, managers have the power to segment and directly address marketing opportunities through well managed processes and marketing strategies. This book is written for business executives and managers interested in gaining advantage by using advanced customer information and marketing process techniques. Managers charged with managing and enhancing relationships with their customers will find this book a profitable guide for many years. Many of today's managers are also charged with cutting the cost of sales to increase profitability. All managers need to identify and focus on those customers who are the most profitable, while, possibly, withdrawing from supporting customers who are unprofitable. The goal of this book is to help you: identify actions to categorize and address your customers much more effectively through the use of information and technology, define the benefits of knowing customers more intimately, and show how you can use information to increase turnover/revenues, satisfaction, and profitability. The level of detailed information that companies can build about a single customer now enables them to market through knowledge-based relationships. By defining processes and providing activities, this book will accelerate your CRM learning curve, and provide an effective framework that will enable your organization to tap into the best practices and experiences of CRM-driven companies (in Chapter 14). In Chapter 6, you will have the opportunity to learn how to (in less than 100 days) start or advance, your customer database or data warehouse environment. This book also provides a wider managerial perspective on the implications of obtaining better information about the whole business. The customer-centric knowledge-based info-structure changes the way that companies do business, and it is likely to alter the structure of the organization, the way it is staffed, and, even, how its management and employees behave. Organizational changes affect the way the marketing department works and the way that it is perceived within the organization. Effective communications with prospects, customers, alliance partners, competitors, the media, and through individualized feedback mechanisms creates a whole new image for marketing and new opportunities for marketing successes. Chapter 14 provides examples of companies that have transformed their marketing principles into CRM practices and are engaging more and more customers in long-term satisfaction and higher per-customer profitability. In the title of this book and throughout its pages I have used the phrase Relationship Technologies to describe the increasingly sophisticated data warehousing and business intelligence technologies that are helping companies create lasting customer relationships, therefore improving business performance. I want to acknowledge that this phrase was created and protected by NCR Corporation and I use this trademark throughout this book with the company's permission. Special thanks and credit for developing the Relationship Technologies concept goes to Dr. Stephen Emmott of NCR's acclaimed Knowledge Lab in London. As time marches on, there is an ever-increasing velocity with which we communicate, interact, position, and involve our selves and our customers in relationships. To increase your Return on Investment (ROI), the right information and relationship technologies are critical for effective Customer Relationship Management. It is now possible to: know who your customers are and who your best customers are stimulate what they buy or know what they won't buy time when and how they buy learn customers' preferences and make them loyal customers define characteristics that make up a great/profitable customer model channels are best to address a customer's needs predict what they may or will buy in the future keep your best customers for many years This book features many companies using CRM, decision-support, marketing databases, and data-warehousing techniques to achieve a positive ROI, using customer-centric knowledge-bases. Success begins with understanding the scope and processes involved in true CRM and then initiating appropriate actions to create and move forward into the future. Walking the talk differentiates the perennial ongoing winners. Reinvestment in success generates growth and opportunity. Success is in our ability to learn from the past, adopt new ideas and actions in the present, and to challenge the future. Respectfully, Ronald S. Swift Dallas, Texas June 2000 |
| cluster analysis customer segmentation: Practical Guide to Cluster Analysis in R Alboukadel Kassambara, 2017-08-23 Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. Part I provides a quick introduction to R and presents required R packages, as well as, data formats and dissimilarity measures for cluster analysis and visualization. Part II covers partitioning clustering methods, which subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. Partitioning clustering approaches include: K-means, K-Medoids (PAM) and CLARA algorithms. In Part III, we consider hierarchical clustering method, which is an alternative approach to partitioning clustering. The result of hierarchical clustering is a tree-based representation of the objects called dendrogram. In this part, we describe how to compute, visualize, interpret and compare dendrograms. Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Among the chapters covered here, there are: Assessing clustering tendency, Determining the optimal number of clusters, Cluster validation statistics, Choosing the best clustering algorithms and Computing p-value for hierarchical clustering. Part V presents advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering. |
| cluster analysis customer segmentation: Marketing Research Marcus J. Schmidt, Svend Hollensen, 2006 Marketing Research: An International Approach is a comprehensive text written with the decision-maker in mind. It is written from the perspective of the firm conducting marketing research in the national and international markets irrespective of its country of origin. This tools-oriented book shows how international marketing managers can transform existing (Secondary) and newly collected (primary) data into useful information. This is a comprehensive and advanced marketing research book that offers an analytical and decision-oriented framework of the subject. This book looks at firms conducting market research in the national and international markets irrespective of its country of origin. This book is written for advanced undergraduate and graduate students studying Marketing Research. It is also appropriate for practitioners who wish to keep abreast of the most recent developments in the field. |
| cluster analysis customer segmentation: Clustering Algorithms John A. Hartigan, 1975 Shows how Galileo, Newton, and Einstein tried to explain gravity. Discusses the concept of microgravity and NASA's research on gravity and microgravity. |
| cluster analysis customer segmentation: Statistics for Marketing and Consumer Research Mario Mazzocchi, 2008-05-22 Balancing simplicity with technical rigour, this practical guide to the statistical techniques essential to research in marketing and related fields, describes each method as well as showing how they are applied. The book is accompanied by two real data sets to replicate examples and with exercises to solve, as well as detailed guidance on the use of appropriate software including: - 750 powerpoint slides with lecture notes and step-by-step guides to run analyses in SPSS (also includes screenshots) - 136 multiple choice questions for tests This is augmented by in-depth discussion of topics including: - Sampling - Data management and statistical packages - Hypothesis testing - Cluster analysis - Structural equation modelling |
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| cluster analysis customer segmentation: Handbook of Market Research Christian Homburg, Martin Klarmann, Arnd Vomberg, 2021-12-03 In this handbook, internationally renowned scholars outline the current state-of-the-art of quantitative and qualitative market research. They discuss focal approaches to market research and guide students and practitioners in their real-life applications. Aspects covered include topics on data-related issues, methods, and applications. Data-related topics comprise chapters on experimental design, survey research methods, international market research, panel data fusion, and endogeneity. Method-oriented chapters look at a wide variety of data analysis methods relevant for market research, including chapters on regression, structural equation modeling (SEM), conjoint analysis, and text analysis. Application chapters focus on specific topics relevant for market research such as customer satisfaction, customer retention modeling, return on marketing, and return on price promotions. Each chapter is written by an expert in the field. The presentation of the material seeks to improve the intuitive and technical understanding of the methods covered. |
| cluster analysis customer segmentation: Data Analytics in Bioinformatics Rabinarayan Satpathy, Tanupriya Choudhury, Suneeta Satpathy, Sachi Nandan Mohanty, Xiaobo Zhang, 2021-01-20 Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more. |
| cluster analysis customer segmentation: Discriminant Analysis and Clustering Ram Gnanadesikan, 1988-01-01 |
| cluster analysis customer segmentation: Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation Cengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari, 2021-08-23 This book presents recent research in intelligent and fuzzy techniques. Emerging conditions such as pandemic, wars, natural disasters and various high technologies force people for significant changes in business and social life. The adoption of digital technologies to transform services or businesses, through replacing non-digital or manual processes with digital processes or replacing older digital technology with newer digital technologies through intelligent systems is the main scope of this book. It focuses on revealing the reflection of digital transformation in our business and social life under emerging conditions through intelligent and fuzzy systems. The latest intelligent and fuzzy methods and techniques on digital transformation are introduced by theory and applications. The intended readers are intelligent and fuzzy systems researchers, lecturers, M.Sc. and Ph.D. students studying digital transformation. Usage of ordinary fuzzy sets and their extensions, heuristics and metaheuristics from optimization to machine learning, from quality management to risk management makes the book an excellent source for researchers. |
| cluster analysis customer segmentation: The Handbook of Marketing Research Rajiv Grover, Marco Vriens, 2006-06-23 The Handbook of Marketing Research comprehensively explores the approaches for delivering market insights for fact-based decision making in a market-oriented firm. |
| cluster analysis customer segmentation: Database Marketing Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin, 2010-02-26 Database marketing is at the crossroads of technology, business strategy, and customer relationship management. Enabled by sophisticated information and communication systems, today’s organizations have the capacity to analyze customer data to inform and enhance every facet of the enterprise—from branding and promotion campaigns to supply chain management to employee training to new product development. Based on decades of collective research, teaching, and application in the field, the authors present the most comprehensive treatment to date of database marketing, integrating theory and practice. Presenting rigorous models, methodologies, and techniques (including data collection, field testing, and predictive modeling), and illustrating them through dozens of examples, the authors cover the full spectrum of principles and topics related to database marketing. This is an excellent in-depth overview of both well-known and very recent topics in customer management models. It is an absolute must for marketers who want to enrich their knowledge on customer analytics. (Peter C. Verhoef, Professor of Marketing, Faculty of Economics and Business, University of Groningen) A marvelous combination of relevance and sophisticated yet understandable analytical material. It should be a standard reference in the area for many years. (Don Lehmann, George E. Warren Professor of Business, Columbia Business School) The title tells a lot about the book's approach—though the cover reads, database, the content is mostly about customers and that's where the real-world action is. Most enjoyable is the comprehensive story – in case after case – which clearly explains what the analysis and concepts really mean. This is an essential read for those interested in database marketing, customer relationship management and customer optimization. (Richard Hochhauser, President and CEO, Harte-Hanks, Inc.) In this tour de force of careful scholarship, the authors canvass the ever expanding literature on database marketing. This book will become an invaluable reference or text for anyone practicing, researching, teaching or studying the subject. (Edward C. Malthouse, Theodore R. and Annie Laurie Sills Associate Professor of Integrated Marketing Communications, Northwestern University) |
| cluster analysis customer segmentation: Data Mining: Concepts and Techniques Jiawei Han, Micheline Kamber, Jian Pei, 2011-06-09 Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data |
| cluster analysis customer segmentation: Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering Laith Mohammad Qasim Abualigah, 2018-12-18 This book puts forward a new method for solving the text document (TD) clustering problem, which is established in two main stages: (i) A new feature selection method based on a particle swarm optimization algorithm with a novel weighting scheme is proposed, as well as a detailed dimension reduction technique, in order to obtain a new subset of more informative features with low-dimensional space. This new subset is subsequently used to improve the performance of the text clustering (TC) algorithm and reduce its computation time. The k-mean clustering algorithm is used to evaluate the effectiveness of the obtained subsets. (ii) Four krill herd algorithms (KHAs), namely, the (a) basic KHA, (b) modified KHA, (c) hybrid KHA, and (d) multi-objective hybrid KHA, are proposed to solve the TC problem; each algorithm represents an incremental improvement on its predecessor. For the evaluation process, seven benchmark text datasets are used with different characterizations and complexities. Text document (TD) clustering is a new trend in text mining in which the TDs are separated into several coherent clusters, where all documents in the same cluster are similar. The findings presented here confirm that the proposed methods and algorithms delivered the best results in comparison with other, similar methods to be found in the literature. |
| cluster analysis customer segmentation: DAX Patterns Marco Russo, Alberto Ferrari, 2020-08-10 A pattern is a general, reusable solution to a frequent or common challenge. This book is the second edition of the most comprehensive collection of ready-to-use solutions in DAX, that you can use in Microsoft Power BI, Analysis Services Tabular, and Power Pivot for Excel. The book includes the following patterns: Time-related calculations, Standard time-related calculations, Month-related calculations, Week-related calculations, Custom time-related calculations, Comparing different time periods, Semi-additive calculations, Cumulative total, Parameter table, Static segmentation, Dynamic segmentation, ABC classification, New and returning customers, Related distinct count, Events in progress, Ranking, Hierarchies, Parent-child hierarchies, Like-for-like comparison, Transition matrix, Survey, Basket analysis, Currency conversion, Budget. |
| cluster analysis customer segmentation: Market Segmentation Malcolm McDonald, Ian Dunbar, 2004-10 * McDonald and Dunbar are the leading author team in this area * Segmentation and marketing mapping are core areas of the marketing syllabus, and there is much that is new as a result of the new segmentation possibilities from e-marketing and e-business * The book has a textbook feel, which highlights the diagrams and market maps (key elements of the book) This is a key book, in a vital area. The Butterworth-Heinemann edition of what was previously published by MacMillian, is a thoroughly revised and updated version. * Highly developed and well illustrated treatment of a key marketing technique * Usable by students and executives, for whom the practical, step-by-step approach is designed * Leading author team in the field |
| cluster analysis customer segmentation: Applied Unsupervised Learning with R Alok Malik, Bradford Tuckfield, 2019-03-27 Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data. Key FeaturesBuild state-of-the-art algorithms that can solve your business' problemsLearn how to find hidden patterns in your dataRevise key concepts with hands-on exercises using real-world datasetsBook Description Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection. What you will learnImplement clustering methods such as k-means, agglomerative, and divisiveWrite code in R to analyze market segmentation and consumer behaviorEstimate distribution and probabilities of different outcomesImplement dimension reduction using principal component analysisApply anomaly detection methods to identify fraudDesign algorithms with R and learn how to edit or improve codeWho this book is for Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including exponents, square roots, means, and medians. |
| cluster analysis customer segmentation: Fuzzy Data Analysis Hans Bandemer, Wolfgang Näther, 2012-12-06 Fuzzy data such as marks, scores, verbal evaluations, imprecise observations, experts' opinions and grey tone pictures, are quite common. In Fuzzy Data Analysis the authors collect their recent results providing the reader with ideas, approaches and methods for processing such data when looking for sub-structures in knowledge bases for an evaluation of functional relationship, e.g. in order to specify diagnostic or control systems. The modelling presented uses ideas from fuzzy set theory and the suggested methods solve problems usually tackled by data analysis if the data are real numbers. Fuzzy Data Analysis is self-contained and is addressed to mathematicians oriented towards applications and to practitioners in any field of application who have some background in mathematics and statistics. |
| cluster analysis customer segmentation: Market Segmentation; Concepts and Applications James F. Engel, Henry F. Fiorillo, Murray Alexander Cayley, 1972 |
| cluster analysis customer segmentation: Industrial Engineering in the Internet-of-Things World Fethi Calisir, 2021-08-07 This book gathers extended versions of the best papers presented at the Global Joint Conference on Industrial Engineering and Its Application Areas (GJCIE), organized virtually on August 14–15, 2020, by Istanbul Technical University. It covers a wide range of topics, including decision analysis, supply chain management, systems modelling and quality control. Further, special emphasis is placed on cutting-edge applications of industrial Internet-of-Things. Technological, economic and business challenges are discussed in detail, presenting effective strategies that can be used to modernize current structures, eliminating the barriers that are keeping industries from taking full advantage of IoT technologies. The book offers an important link between technological research and industry best practices, and covers various disciplinary areas such as manufacturing, healthcare and service engineering, among others. |
| cluster analysis customer segmentation: From Data to Knowledge Wolfgang A. Gaul, Dietmar Pfeifer, 2013-03-12 The subject of this book is the incorporation and integration of mathematical and statistical techniques and information science topics into the field of classification, data analysis, and knowledge organization. Readers will find survey papers as well as research papers and reports on newest results. The papers are a combination of theoretical issues and applications in special fields: Spatial Data Analysis, Economics, Medicine, Biology, and Linguistics. |
| cluster analysis customer segmentation: Education and Management Mark Zhou, 2011-07-22 This four-volume-set (CCIS 208, 209, 210, 211) constitutes the refereed proceedings of the International Symposium on Applied Economics, Business and Development, ISAEBD 2011, held in Dalian, China, in August 2011. The papers address issues related to Applied Economics, Business and Development and cover various research areas including Economics, Management, Education and its Applications. |
| cluster analysis customer segmentation: Computer Supported Cooperative Work and Social Computing Yuqing Sun, Tun Lu, Zhengtao Yu, Hongfei Fan, Liping Gao, 2019-11-13 This book constitutes the refereed proceedings of the 14th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2019, held in Kunming, China, in August 2019. The 52 revised full papers and 10 short papers were carefully reviewed and selected from 169 submissions. The papers of this volume are organized in topical sections on: collaborative models, approaches, algorithms, and systems; social computing (online communities, crowdsourcing, recommendation, sentiment analysis, etc.); AI for CSCW and social computing. |
| cluster analysis customer segmentation: Finding Groups in Data Leonard Kaufman, Peter J. Rousseeuw, 1990-03-22 Partitioning around medoids (Program PAM). Clustering large applications (Program CLARA). Fuzzy analysis (Program FANNY). Agglomerative Nesting (Program AGNES). Divisive analysis (Program DIANA). Monothetic analysis (Program MONA). Appendix. |
| cluster analysis customer segmentation: Computational Methods and Data Engineering Vijayan K. Asari, Vijendra Singh, Rajkumar Rajasekaran, R. B. Patel, 2022-09-08 The book features original papers from International Conference on Computational Methods and Data Engineering (ICCMDE 2021), organized by School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India, during November 25–26, 2021. The book covers innovative and cutting-edge work of researchers, developers, and practitioners from academia and industry working in the area of advanced computing. |
Cluster - Group sharing for friends & family. The antidote to social …
Cluster gives you a private space to share photos and memories with the people you choose, away from social media. Make your own groups and share pics, videos, comments, and chat!
CLUSTER Definition & Meaning - Merriam-Webster
The meaning of CLUSTER is a number of similar things that occur together. How to use cluster in a sentence.
CLUSTER | English meaning - Cambridge Dictionary
CLUSTER definition: 1. a group of similar things that are close together, sometimes surrounding something: 2. a group…. Learn more.
Cluster - Wikipedia
Cluster analysis, a set of techniques for grouping a set of objects based on intrinsic similarities; Cluster sampling, a sampling technique used when "natural" groupings are evident in a …
An Overview of Cluster Computing - GeeksforGeeks
An Overview of Cluster Computing - GeeksforGeeks
What is a cluster? - Princeton Research Computing
The computational systems made available by Princeton Research Computing are, for the most part, clusters. Each computer in the cluster is called a node (the term "node" comes from …
CLUSTER definition and meaning | Collins English Dictionary
A cluster of people or things is a small group of them close together. ...clusters of men in formal clothes. There's no town here, just a cluster of shops, cabins and motels at the side of the …
What does cluster mean? - Definitions.net
Definition of cluster in the Definitions.net dictionary. Meaning of cluster. What does cluster mean? Information and translations of cluster in the most comprehensive dictionary definitions …
Cluster - definition of cluster by The Free Dictionary
Define cluster. cluster synonyms, cluster pronunciation, cluster translation, English dictionary definition of cluster. n. 1. A group of the same or similar elements gathered or occurring …
Computer Clusters, Types, Uses and Applications - Baeldung
Mar 18, 2024 · In simple terms, a computer cluster is a set of computers (nodes) that work together as a single system. We can use clusters to enhance the processing power or …
Cluster - Group sharing for friends & family. The antidote to social …
Cluster gives you a private space to share photos and memories with the people you choose, away from social media. Make your own groups and share pics, videos, comments, and chat!
CLUSTER Definition & Meaning - Merriam-Webster
The meaning of CLUSTER is a number of similar things that occur together. How to use cluster in a sentence.
CLUSTER | English meaning - Cambridge Dictionary
CLUSTER definition: 1. a group of similar things that are close together, sometimes surrounding something: 2. a group…. Learn more.
Cluster - Wikipedia
Cluster analysis, a set of techniques for grouping a set of objects based on intrinsic similarities; Cluster sampling, a sampling technique used when "natural" groupings are evident in a …
An Overview of Cluster Computing - GeeksforGeeks
An Overview of Cluster Computing - GeeksforGeeks
What is a cluster? - Princeton Research Computing
The computational systems made available by Princeton Research Computing are, for the most part, clusters. Each computer in the cluster is called a node (the term "node" comes from …
CLUSTER definition and meaning | Collins English Dictionary
A cluster of people or things is a small group of them close together. ...clusters of men in formal clothes. There's no town here, just a cluster of shops, cabins and motels at the side of the …
What does cluster mean? - Definitions.net
Definition of cluster in the Definitions.net dictionary. Meaning of cluster. What does cluster mean? Information and translations of cluster in the most comprehensive dictionary definitions …
Cluster - definition of cluster by The Free Dictionary
Define cluster. cluster synonyms, cluster pronunciation, cluster translation, English dictionary definition of cluster. n. 1. A group of the same or similar elements gathered or occurring …
Computer Clusters, Types, Uses and Applications - Baeldung
Mar 18, 2024 · In simple terms, a computer cluster is a set of computers (nodes) that work together as a single system. We can use clusters to enhance the processing power or …
