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| customer churn analysis example: Fighting Churn with Data Carl S. Gold, 2020-12-22 The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether. Summary The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether. This hands-on guide is packed with techniques for converting raw data into measurable metrics, testing hypotheses, and presenting findings that are easily understandable to non-technical decision makers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Keeping customers active and engaged is essential for any business that relies on recurring revenue and repeat sales. Customer turnover—or “churn”—is costly, frustrating, and preventable. By applying the techniques in this book, you can identify the warning signs of churn and learn to catch customers before they leave. About the book Fighting Churn with Data teaches developers and data scientists proven techniques for stopping churn before it happens. Packed with real-world use cases and examples, this book teaches you to convert raw data into measurable behavior metrics, calculate customer lifetime value, and improve churn forecasting with demographic data. By following Zuora Chief Data Scientist Carl Gold’s methods, you’ll reap the benefits of high customer retention. What's inside Calculating churn metrics Identifying user behavior that predicts churn Using churn reduction tactics with customer segmentation Applying churn analysis techniques to other business areas Using AI for accurate churn forecasting About the reader For readers with basic data analysis skills, including Python and SQL. About the author Carl Gold (PhD) is the Chief Data Scientist at Zuora, Inc., the industry-leading subscription management platform. Table of Contents: PART 1 - BUILDING YOUR ARSENAL 1 The world of churn 2 Measuring churn 3 Measuring customers 4 Observing renewal and churn PART 2 - WAGING THE WAR 5 Understanding churn and behavior with metrics 6 Relationships between customer behaviors 7 Segmenting customers with advanced metrics PART 3 - SPECIAL WEAPONS AND TACTICS 8 Forecasting churn 9 Forecast accuracy and machine learning 10 Churn demographics and firmographics 11 Leading the fight against churn |
| customer churn analysis example: Discovering Knowledge in Data Daniel T. Larose, 2005-01-28 Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a white box methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online. |
| customer churn analysis example: Predictive Marketing Omer Artun, Dominique Levin, 2015-08-06 Make personalized marketing a reality with this practical guide to predictive analytics Predictive Marketing is a predictive analytics primer for organizations large and small, offering practical tips and actionable strategies for implementing more personalized marketing immediately. The marketing paradigm is changing, and this book provides a blueprint for navigating the transition from creative- to data-driven marketing, from one-size-fits-all to one-on-one, and from marketing campaigns to real-time customer experiences. You'll learn how to use machine-learning technologies to improve customer acquisition and customer growth, and how to identify and re-engage at-risk or lapsed customers by implementing an easy, automated approach to predictive analytics. Much more than just theory and testament to the power of personalized marketing, this book focuses on action, helping you understand and actually begin using this revolutionary approach to the customer experience. Predictive analytics can finally make personalized marketing a reality. For the first time, predictive marketing is accessible to all marketers, not just those at large corporations — in fact, many smaller organizations are leapfrogging their larger counterparts with innovative programs. This book shows you how to bring predictive analytics to your organization, with actionable guidance that get you started today. Implement predictive marketing at any size organization Deliver a more personalized marketing experience Automate predictive analytics with machine learning technology Base marketing decisions on concrete data rather than unproven ideas Marketers have long been talking about delivering personalized experiences across channels. All marketers want to deliver happiness, but most still employ a one-size-fits-all approach. Predictive Marketing provides the information and insight you need to lift your organization out of the campaign rut and into the rarefied atmosphere of a truly personalized customer experience. |
| customer churn analysis example: Business and Consumer Analytics: New Ideas Pablo Moscato, Natalie Jane de Vries, 2019-05-30 This two-volume handbook presents a collection of novel methodologies with applications and illustrative examples in the areas of data-driven computational social sciences. Throughout this handbook, the focus is kept specifically on business and consumer-oriented applications with interesting sections ranging from clustering and network analysis, meta-analytics, memetic algorithms, machine learning, recommender systems methodologies, parallel pattern mining and data mining to specific applications in market segmentation, travel, fashion or entertainment analytics. A must-read for anyone in data-analytics, marketing, behavior modelling and computational social science, interested in the latest applications of new computer science methodologies. The chapters are contributed by leading experts in the associated fields.The chapters cover technical aspects at different levels, some of which are introductory and could be used for teaching. Some chapters aim at building a common understanding of the methodologies and recent application areas including the introduction of new theoretical results in the complexity of core problems. Business and marketing professionals may use the book to familiarize themselves with some important foundations of data science. The work is a good starting point to establish an open dialogue of communication between professionals and researchers from different fields. Together, the two volumes present a number of different new directions in Business and Customer Analytics with an emphasis in personalization of services, the development of new mathematical models and new algorithms, heuristics and metaheuristics applied to the challenging problems in the field. Sections of the book have introductory material to more specific and advanced themes in some of the chapters, allowing the volumes to be used as an advanced textbook. Clustering, Proximity Graphs, Pattern Mining, Frequent Itemset Mining, Feature Engineering, Network and Community Detection, Network-based Recommending Systems and Visualization, are some of the topics in the first volume. Techniques on Memetic Algorithms and their applications to Business Analytics and Data Science are surveyed in the second volume; applications in Team Orienteering, Competitive Facility-location, and Visualization of Products and Consumers are also discussed. The second volume also includes an introduction to Meta-Analytics, and to the application areas of Fashion and Travel Analytics. Overall, the two-volume set helps to describe some fundamentals, acts as a bridge between different disciplines, and presents important results in a rapidly moving field combining powerful optimization techniques allied to new mathematical models critical for personalization of services. Academics and professionals working in the area of business anyalytics, data science, operations research and marketing will find this handbook valuable as a reference. Students studying these fields will find this handbook useful and helpful as a secondary textbook. |
| customer churn analysis example: 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 |
| customer churn analysis example: 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) |
| customer churn analysis example: Soft Computing: Theories and Applications Millie Pant, Tarun K. Sharma, Om Prakash Verma, Rajesh Singla, Afzal Sikander, 2020-02-24 The book focuses on soft computing and its applications to solve real-world problems in different domains, ranging from medicine and health care, to supply chain management, image processing and cryptanalysis. It includes high-quality papers presented at the International Conference on Soft Computing: Theories and Applications (SoCTA 2018), organized by Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India. Offering significant insights into soft computing for teachers and researchers alike, the book inspires more researchers to work in the field of soft computing. |
| customer churn analysis example: Introduction to Algorithmic Marketing Ilya Katsov, 2017-12 A comprehensive guide to advanced marketing automation for marketing strategists, data scientists, product managers, and software engineers. The book covers the main areas of marketing that require programmatic micro-decisioning - targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization. |
| customer churn analysis example: Analytics in a Big Data World Bart Baesens, 2014-04-15 The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities. |
| customer churn analysis example: Fundamentals of Predictive Analytics with JMP, Second Edition Ron Klimberg, B. D. McCullough, 2017-12-19 Going beyond the theoretical foundation, this step-by-step book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. -- |
| customer churn analysis example: 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 |
| customer churn analysis example: Heuristics in Analytics Carlos Andre Reis Pinheiro, Fiona McNeill, 2014-03-03 Employ heuristic adjustments for truly accurate analysis Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of proper analytical tools, the book describes the analytical process from exploratory analysis through model developments, to deployments and possible outcomes. Beginning with an introduction to heuristic concepts, readers will find heuristics applied to statistics and probability, mathematics, stochastic, and artificial intelligence models, ending with the knowledge applications that solve business problems. Case studies illustrate the everyday application and implication of the techniques presented, while the heuristic approach is integrated into analytical modeling, graph analysis, text analytics, and more. Robust analytics has become crucial in the corporate environment, and randomness plays an enormous role in business and the competitive marketplace. Failing to account for randomness can steer a model in an entirely wrong direction, negatively affecting the final outcome and potentially devastating the bottom line. Heuristics in Analytics describes how the heuristic characteristics of analysis can be overcome with problem design, math and statistics, helping readers to: Realize just how random the world is, and how unplanned events can affect analysis Integrate heuristic and analytical approaches to modeling and problem solving Discover how graph analysis is applied in real-world scenarios around the globe Apply analytical knowledge to customer behavior, insolvency prevention, fraud detection, and more Understand how text analytics can be applied to increase the business knowledge Every single factor, no matter how large or how small, must be taken into account when modeling a scenario or event—even the unknowns. The presence or absence of even a single detail can dramatically alter eventual outcomes. From raw data to final report, Heuristics in Analytics contains the information analysts need to improve accuracy, and ultimately, predictive, and descriptive power. |
| customer churn analysis example: Customer Education Adam Avramescu, 2019-01-10 Today's software companies can't afford to be passive with their customers. As software moves to the web and becomes more consumerized, software companies can only grow if their current customers renew and grow over time. Otherwise those customers will leave, creating a leaky bucket of revenue.So, what are smart, innovative companies doing before they end up with severe churn problems? Forward-thinking companies invest in Customer Education early as a way to drive customer growth and maximize lifetime value in a scalable way. Over time, this function has the potential to differentiate a company in the market.Consider this book a survival guide to investing in a Customer Education function, including: -How to drive a Customer Education strategy across your customer lifecycle-Tips for creating killer content that will actually lead to customer performance-What tools to implement as part of your technology stack-Measurement strategies for improving your content and showing ROI-And more... |
| customer churn analysis example: Customer Experience 3.0 John A. Goodman, 2014-08-12 Customer Experience 3.0 provides firsthand guidance on what works, what doesn't--and the revenue and word-of-mouth payoff of getting it right. Between smartphones, social media, mobile connectivity, and a plethora of other technological innovations changing the way we do almost everything these days, your customers are expecting you to be taking advantage of it all to enhance their customer service experience far beyond the meeting-the-minimum experiences of days past. Unfortunately, many companies are failing to take advantage of and properly manage these service-enhancing tools that now exist, and in return they deliver a series of frustrating, disjointed transactions that end up driving people away and into the pockets of businesses getting it right. Having managed more than 1,000 separate customer service studies, author John A. Goodman has created an innovative customer-experience framework and step-by-step roadmap that shows you how to: Design and deliver flawless services and products while setting honest customer expectations Create and implement an effective customer access strategy Capture and leverage the voice of the customer to set priorities and improve products, services and marketing Use CRM systems, cutting-edge metrics, and other tools to deliver customer satisfaction Companies who get customer service right can regularly provide seamless experiences, seeming to know what customers want even before they know it themselves…while others end up staying generic, take stabs in the dark to try and fix the problem, and end up dropping the ball. Customer Experience 3.0 reveals how to delight customers using all the technological tools at their disposal. |
| customer churn analysis example: Proceedings of International Conference on Data Science and Applications Mukesh Saraswat, Sarbani Roy, Chandreyee Chowdhury, Amir H. Gandomi, 2021-11-23 This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2021), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from April 10 to 11, 2021. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing. |
| customer churn analysis example: The Best Service is No Service Bill Price, David Jaffe, 2011-09-14 In this groundbreaking book, Bill Price and David Jaffe offer a new, game-changing approach, showing how managers are taking the wrong path and are using the wrong metrics to measure customer service. Customer service, they assert, is only needed when a company does something wrong—eliminating the need for service is the best way to satisfy customers. To be successful, companies need to treat service as a data point of dysfunction and figure what they need to do to eliminate the demand. The Best Service Is No Service outlines these seven principles to deliver the best service that ultimately leads to no service: Eliminate dumb contacts Create engaging self-service Be proactive Make it easy to contact your company Own the actions across the company Listen and act Deliver great service experiences |
| customer churn analysis example: 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. |
| customer churn analysis example: Marketing Metrics Neil Bendle, Paul W. Farris, Phillip Pfeifer, David Reibstein, 2020-08-23 Your Definitive, Up-to-Date Guide to Marketing Metrics—Choosing Them, Implementing Them, Applying Them This award-winning guide will help you accurately quantify the performance of all your marketing investments, increase marketing ROI, and grow profits. Four renowned experts help you apply today's best practices for assessing everything from brand equity to social media, email performance, and rich media interaction. This updated edition shows how to measure costly sponsorships, explores links between marketing and financial metrics for current and aspiring C-suite decision-makers; presents better ways to measure omnichannel marketing activities; and includes a new section on accountability and standardization in marketing measurement. As in their best-selling previous editions, the authors present pros, cons, and practical guidance for every technique they cover. Measure promotions, advertising, distribution, customer perceptions, competitor power, margins, pricing, product portfolios, salesforces, and more Apply web, online, social, and mobile metrics more effectively Build models to optimize planning and decision-making Attribute purchase decisions when multiple channels interact Understand the links between search and distribution, and use new online distribution metrics Evaluate marketing's impact on a publicly traded firm's financial objectives Whatever your marketing role, Marketing Metrics will help you choose the right metrics for every task—and capture data that's valid, reliable, and actionable. |
| customer churn analysis example: Lean Analytics Alistair Croll, Benjamin Yoskovitz, 2024-02-23 Whether you're a startup founder trying to disrupt an industry or an entrepreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without. Understand Lean Startup, analytics fundamentals, and the data-driven mindset Look at six sample business models and how they map to new ventures of all sizes Find the One Metric That Matters to you Learn how to draw a line in the sand, so you'll know it's time to move forward Apply Lean Analytics principles to large enterprises and established products |
| customer churn analysis example: The Great Mental Models, Volume 1 Shane Parrish, Rhiannon Beaubien, 2024-10-15 Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage. |
| customer churn analysis example: Kellogg on Branding Alice M. Tybout, Tim Calkins, 2011-01-07 The Foreword by renowned marketing guru Philip Kotler sets the stage for a comprehensive review of the latest strategies for building, leveraging, and rejuvenating brands. Destined to become a marketing classic, Kellogg on Branding includes chapters written by respected Kellogg marketing professors and managers of successful companies. It includes: The latest thinking on key branding concepts, including brand positioning and design Strategies for launching new brands, leveraging existing brands, and managing a brand portfolio Techniques for building a brand-centered organization Insights from senior managers who have fought branding battles and won This is the first book on branding from the faculty of the Kellogg School, the respected resource for dynamic marketing information for today's ever-changing and challenging environment. Kellogg is the brand that executives and marketing managers trust for definitive information on proven approaches for solving marketing dilemmas and seizing marketing opportunities. |
| customer churn analysis example: Sales Engagement Manny Medina, Max Altschuler, Mark Kosoglow, 2019-03-12 Engage in sales—the modern way Sales Engagement is how you engage and interact with your potential buyer to create connection, grab attention, and generate enough interest to create a buying opportunity. Sales Engagement details the modern way to build the top of the funnel and generate qualified leads for B2B companies. This book explores why a Sales Engagement strategy is so important, and walks you through the modern sales process to ensure you’re effectively connecting with customers every step of the way. • Find common factors holding your sales back—and reverse them through channel optimization • Humanize sales with personas and relevant information at every turn • Understand why A/B testing is so incredibly critical to success, and how to do it right • Take your sales process to the next level with a rock solid, modern Sales Engagement strategy This book is essential reading for anyone interested in up-leveling their game and doing more than they ever thought possible. |
| customer churn analysis example: 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. |
| customer churn analysis example: Profit Driven Business Analytics Wouter Verbeke, Bart Baesens, Cristian Bravo, 2017-10-09 Maximize profit and optimize decisions with advanced business analytics Profit-Driven Business Analytics provides actionable guidance on optimizing the use of data to add value and drive better business. Combining theoretical and technical insights into daily operations and long-term strategy, this book acts as a development manual for practitioners seeking to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the author team draws upon their recent research to share deep insight about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this guide provides invaluable guidance for practitioners seeking to reap the advantages of true business analytics. Despite widespread discussion surrounding the value of data in decision making, few businesses have adopted advanced analytic techniques in any meaningful way. This book shows you how to delve deeper into the data and discover what it can do for your business. Reinforce basic analytics to maximize profits Adopt the tools and techniques of successful integration Implement more advanced analytics with a value-centric approach Fine-tune analytical information to optimize business decisions Both data stored and streamed has been increasing at an exponential rate, and failing to use it to the fullest advantage equates to leaving money on the table. From bolstering current efforts to implementing a full-scale analytics initiative, the vast majority of businesses will see greater profit by applying advanced methods. Profit-Driven Business Analytics provides a practical guidebook and reference for adopting real business analytics techniques. |
| customer churn analysis example: Proceedings of International Conference on Wireless Communication Hari Vasudevan, Zoran Gajic, Amit A. Deshmukh, 2022-01-16 This book comprises selected papers presented at the International Conference on Wireless Communication (ICWiCOM 2021), which is organized by the Department of Electronics and Telecommunication Engineering, D. J. Sanghvi College of Engineering, Mumbai, India, during October 8–9, 2021. The book focuses on specific topics of wireless communication, like signal and image processing applicable to wireless domains, networking, microwave and antenna design, and telemedicine systems. Covering three main areas – Antenna Design, Networking & Signal Processing, Embedded Systems and Internet of Things (IoT) – it is a valuable resource for postgraduate and doctoral students. |
| customer churn analysis example: Practical Time Series Analysis Dr. Avishek Pal, Dr. PKS Prakash, 2017-09-28 Step by Step guide filled with real world practical examples. About This Book Get your first experience with data analysis with one of the most powerful types of analysis—time-series. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide Who This Book Is For This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods. What You Will Learn Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project Develop an understanding of loading, exploring, and visualizing time-series data Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series Take advantage of exponential smoothing to tackle noise in time series data Learn how to use auto-regressive models to make predictions using time-series data Build predictive models on time series using techniques based on auto-regressive moving averages Discover recent advancements in deep learning to build accurate forecasting models for time series Gain familiarity with the basics of Python as a powerful yet simple to write programming language In Detail Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. Style and approach This book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases. |
| customer churn analysis example: Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020) Ajith Abraham, Yukio Ohsawa, Niketa Gandhi, M.A. Jabbar, Abdelkrim Haqiq, Seán McLoone, Biju Issac, 2021-04-15 This book highlights the recent research on soft computing and pattern recognition and their various practical applications. It presents 62 selected papers from the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020) and 35 papers from the 16th International Conference on Information Assurance and Security (IAS 2020), which was held online, from December 15 to 18, 2020. A premier conference in the field of artificial intelligence, SoCPaR-IAS 2020 brought together researchers, engineers and practitioners whose work involves intelligent systems, network security and their applications in industry. Including contributions by authors from 40 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering. |
| customer churn analysis example: Analyzing Analytics Rajesh Bordawekar, Bob Blainey, Ruchir Puri, 2022-05-31 This book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice. This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads, impact on programming models and runtime systems, and designing analytics systems. |
| customer churn analysis example: Public Policy Analytics Ken Steif, 2021-08-18 Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government. |
| customer churn analysis example: Lean B2B Étienne Garbugli, 2022-03-22 Get from Idea to Product/Market Fit in B2B. The world has changed. Nowadays, there are more companies building B2B products than there’s ever been. Products are entering organizations top-down, middle-out, and bottom-up. Teams and managers control their budgets. Buyers have become savvier and more impatient. The case for the value of new innovations no longer needs to be made. Technology products get hired, and fired faster than ever before. The challenges have moved from building and validating products to gaining adoption in increasingly crowded and fragmented markets. This, requires a new playbook. The second edition of Lean B2B is the result of years of research into B2B entrepreneurship. It builds off the unique Lean B2B Methodology, which has already helped thousands of entrepreneurs and innovators around the world build successful businesses. In this new edition, you’ll learn: - Why companies seek out new products, and why they agree to buy from unproven vendors like startups - How to find early adopters, establish your credibility, and convince business stakeholders to work with you - What type of opportunities can increase the likelihood of building a product that finds adoption in businesses - How to learn from stakeholders, identify a great opportunity, and create a compelling value proposition - How to get initial validation, create a minimum viable product, and iterate until you're able to find product/market fit This second edition of Lean B2B will show you how to build the products that businesses need, want, buy, and adopt. |
| customer churn analysis example: Data Mining Techniques Michael J. A. Berry, Gordon S. Linoff, 2004-04-09 Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information. |
| customer churn analysis example: Knowledge Discovery in Databases: PKDD 2004 Jean-Francois Boulicaut, Floriana Esposito, Fosca Giannotti, Dino Pedreschi, 2004-09-10 This book constitutes the refereed proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2004, held in Pisa, Italy, in September 2004 jointly with ECML 2004. The 39 revised full papers and 9 revised short papers presented together with abstracts of 5 invited talks were carefully reviewed and selected from 194 papers submitted to PKDD and 107 papers submitted to both, PKDD and ECML. The papers present a wealth of new results in knowledge discovery in databases and address all current issues in the area. |
| customer churn analysis example: Business Intelligence Ramesh Sharda, Dursun Delen, Efraim Turban, 2017-01-13 For courses on Business Intelligence or Decision Support Systems. A managerial approach to understanding business intelligence systems. To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice. |
| customer churn analysis example: Key Business Analytics Bernard Marr, 2016-02-10 Key Business Analytics will help managers apply tools to turn data into insights that help them better understand their customers, optimize their internal processes and identify cost savings and growth opportunities. It includes analysis techniques within the following categories: Financial analytics – cashflow, profitability, sales forecasts Market analytics – market size, market trends, marketing channels Customer analytics – customer lifetime values, social media, customer needs Employee analytics – capacity, performance, leadership Operational analytics – supply chains, competencies, environmental impact Bare business analytics – sentiments, text, correlations Each tool will follow the bestselling Key format of being 5-6 pages long, broken into short sharp advice on the essentials: What is it? When should I use it? How do I use it? Tips and pitfalls Further reading This essential toolkit also provides an invaluable section on how to gather original data yourself through surveys, interviews, focus groups, etc. |
| customer churn analysis example: Statistical Methods in Customer Relationship Management V. Kumar, J. Andrew Petersen, 2012-07-26 Statistical Methods in Customer Relationship Management focuses on the quantitative and modeling aspects of customer management strategies that lead to future firm profitability, with emphasis on developing an understanding of Customer Relationship Management (CRM) models as the guiding concept for profitable customer management. To understand and explore the functioning of CRM models, this book traces the management strategies throughout a customer’s tenure with a firm. Furthermore, the book explores in detail CRM models for customer acquisition, customer retention, customer acquisition and retention, customer churn, and customer win back. Statistical Methods in Customer Relationship Management: Provides an overview of a CRM system, introducing key concepts and metrics needed to understand and implement these models. Focuses on five CRM models: customer acquisition, customer retention, customer churn, and customer win back with supporting case studies. Explores each model in detail, from investigating the need for CRM models to looking at the future of the models. Presents models and concepts that span across the introductory, advanced, and specialist levels. Academics and practitioners involved in the area of CRM as well as instructors of applied statistics and quantitative marketing courses will benefit from this book. |
| customer churn analysis example: Handbook of Marketing Decision Models Berend Wierenga, 2008-09-05 Marketing models is a core component of the marketing discipline. The recent developments in marketing models have been incredibly fast with information technology (e.g., the Internet), online marketing (e-commerce) and customer relationship management (CRM) creating radical changes in the way companies interact with their customers. This has created completely new breeds of marketing models, but major progress has also taken place in existing types of marketing models. Handbook of Marketing Decision Models presents the state of the art in marketing decision models. The book deals with new modeling areas, such as customer relationship management, customer value and online marketing, as well as recent developments in other advertising, sales promotions, sales management, and competition are dealt with. New developments are in consumer decision models, models for return on marketing, marketing management support systems, and in special techniques such as time series and neural nets. |
| customer churn analysis example: Product-Led Growth Bush Wes, 2019-05 Product-Led Growth is about helping your customers experience the ongoing value your product provides. It is a critical step in successful product design and this book shows you how it's done. - Nir Eyal, Wall Street Journal Bestselling Author of Hooked |
| customer churn analysis example: The Outsiders William Thorndike, 2012 It's time to redefine the CEO success story. Meet eight iconoclastic leaders who helmed firms where returns on average outperformed the S&P 500 by more than 20 times. |
| customer churn analysis example: Developing Churn Models Using Data Mining Techniques and Social Network Analysis Klepac, Goran, 2014-07-31 This book provides an in-depth analysis of attrition modeling relevant to business planning and management, offering insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytic tools--Provided by publisher. |
| customer churn analysis example: Big Data Analytics Saumyadipta Pyne, B.L.S. Prakasa Rao, S.B. Rao, 2016-10-12 This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics. |
consumer、customer、client 有何区别？ - 知乎
对于customer和consumer，我上marketing的课的时候区分过这两个定义。 customer behavior：a broad term that covers individual consumers who buy goods and services for their own use …
Consumer与customer有区别吗？具体作什么区别？ - 知乎
Mar 18, 2014 · 一般把 customer 翻译做 “客户“ 比如你是杜蕾斯的生产商，那么中国总代，上海曼伦商贸有限公司，就是你的customer，然后从曼伦进货的全家就是曼伦的customer，然后隔 …
Windows 10 business 和 consumer 中的专业版有什么不同？ - 知乎
Mar 14, 2020 · Windows10 有business editions 和 consumer editions 版。其中每个都有 专业工作站版，可这2个专业工作…
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手机电脑打开都是这样 我想用文献检索 不想用作者检索啊啊啊啊啊
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CRM（Customer Relationship Management），即客户关系管理系统.。 是指利用软件、硬件和网络技术，为企业建立一个客户信息收集、管理、分析和利用的信息系统。通俗地讲， CRM就 …
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KYC看着高端，其实我们每个人都经历过。例如，当你去银行开户的时候，都必须要提交身份证件，甚至有时候还要提交家庭住址证明。这便是一个最简单的KYC。(也叫做CIP - Customer …
什么是SCRM？为什么企业要做SCRM？ - 知乎
SCRM翻译后的全程是：Social Customer Relationship Management ，可以看到这里的“S”原来是“Social”，也就是“社交”的意思。 尽管只是多了一个S，却将原先CRM呈现的客户管理行为转 …
什么是跨境电商，你们了解多少？ - 知乎
跨境电子商务是指不同国度或地域的买卖双方经过互联网以邮件或者快递等方式通关，将传统贸易中的展现、洽谈和成交环节数字化，完成产品进口的的新型贸易方式，当前主流的跨境电商形 …
有大神公布一下Nature Communications从投出去到Online的审稿 …
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
新媒体的KOL、KOC是什么? - 知乎
KOC有双重身份，即Customer和Creator，KOC是消费者的同时也是创作者，是对消费者的消费决策起到关键作用的群体。 KOL与KOC在本质上截然不同，是两个群体。前者是推，而KOC是 …
