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Author: René Michel Publisher: Springer Nature ISBN: 3030226255 Category : Business & Economics Languages : en Pages : 352
Book Description
This book explores all relevant aspects of net scoring, also known as uplift modeling: a data mining approach used to analyze and predict the effects of a given treatment on a desired target variable for an individual observation. After discussing modern net score modeling methods, data preparation, and the assessment of uplift models, the book investigates software implementations and real-world scenarios. Focusing on the application of theoretical results and on practical issues of uplift modeling, it also includes a dedicated chapter on software solutions in SAS, R, Spectrum Miner, and KNIME, which compares the respective tools. This book also presents the applications of net scoring in various contexts, e.g. medical treatment, with a special emphasis on direct marketing and corresponding business cases. The target audience primarily includes data scientists, especially researchers and practitioners in predictive modeling and scoring, mainly, but not exclusively, in the marketing context.
Author: René Michel Publisher: Springer Nature ISBN: 3030226255 Category : Business & Economics Languages : en Pages : 352
Book Description
This book explores all relevant aspects of net scoring, also known as uplift modeling: a data mining approach used to analyze and predict the effects of a given treatment on a desired target variable for an individual observation. After discussing modern net score modeling methods, data preparation, and the assessment of uplift models, the book investigates software implementations and real-world scenarios. Focusing on the application of theoretical results and on practical issues of uplift modeling, it also includes a dedicated chapter on software solutions in SAS, R, Spectrum Miner, and KNIME, which compares the respective tools. This book also presents the applications of net scoring in various contexts, e.g. medical treatment, with a special emphasis on direct marketing and corresponding business cases. The target audience primarily includes data scientists, especially researchers and practitioners in predictive modeling and scoring, mainly, but not exclusively, in the marketing context.
Author: Alexander Chernev Publisher: John Wiley & Sons ISBN: 1119906245 Category : Business & Economics Languages : en Pages : 441
Book Description
The ultimate marketing resource from the world’s leading scholars From the world’s #1 MBA marketing program comes the latest edition of Kellogg on Marketing, presented by Philip Kotler and Alexander Chernev. With hundreds of pages of brand-new material on timely topics, like creating value to disrupt markets, defensive marketing strategies, strategic customer management, building strong brands, and marketing in the metaverse, the book explores foundational and advanced topics in marketing management. You’ll discover a renewed focus on digital transformation and data analytics, as well as comprehensive explanations of the strategic and tactical aspects of effective marketing. From managing business growth to identifying target customers, developing a meaningful value proposition, and data-driven marketing, every area relevant to marketing professionals is covered by expert contributors possessing unique insights into their respective competencies. Readers will also find: Discussions of the unique challenges facing brands in designing and managing their image and techniques for building resilient brands Strategies for creating loyal customers and developing personalization at scale Strategies for designing effective omni-channel marketing platforms Strategies for crafting a successful cross-platform communications campaigns Discussions on the application of data analytics and artificial intelligence to the creation of successful marketing programs An indispensable resource for any professional expected to contribute to their organization’s marketing efforts or business growth, Kellogg on Marketing, Third Edition, also earn a place in curricula of the business school educating the next generation of business leaders.
Author: Eric Siegel Publisher: John Wiley & Sons ISBN: 1118416856 Category : Business & Economics Languages : en Pages : 467
Book Description
“Mesmerizing & fascinating...” —The Seattle Post-Intelligencer "The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive Analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated — and Hillary for America 2016 plans to calculate — the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 183 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.
Author: Petra Perner Publisher: Springer ISBN: 3319415611 Category : Computers Languages : en Pages : 446
Book Description
This book constitutes the refereed proceedings of the 16th Industrial Conference on Advances in Data Mining, ICDM 2016, held in New York, NY, USA, in July 2016. The 33 revised full papers presented were carefully reviewed and selected from 100 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine, and in process control, industry, and society.
Author: Mingxiu Hu Publisher: Springer Science & Business Media ISBN: 1461478464 Category : Medical Languages : en Pages : 344
Book Description
This volume presents 27 selected papers in topics that range from statistical applications in business and finance to applications in clinical trials and biomarker analysis. All papers feature original, peer-reviewed content. The editors intentionally selected papers that cover many topics so that the volume will serve the whole statistical community and a variety of research interests. The papers represent select contributions to the 21st ICSA Applied Statistics Symposium. The International Chinese Statistical Association (ICSA) Symposium took place between the 23rd and 26th of June, 2012 in Boston, Massachusetts. It was co-sponsored by the International Society for Biopharmaceutical Statistics (ISBS) and American Statistical Association (ASA). This is the inaugural proceedings volume to share research from the ICSA Applied Statistics Symposium.
Author: Rashed Haq Publisher: John Wiley & Sons ISBN: 1119665973 Category : Business & Economics Languages : en Pages : 368
Book Description
Enterprise Artificial Intelligence Transformation AI is everywhere. From doctor's offices to cars and even refrigerators, AI technology is quickly infiltrating our daily lives. AI has the ability to transform simple tasks into technological feats at a human level. This will change the world, plain and simple. That's why AI mastery is such a sought-after skill for tech professionals. Author Rashed Haq is a subject matter expert on AI, having developed AI and data science strategies, platforms, and applications for Publicis Sapient's clients for over 10 years. He shares that expertise in the new book, Enterprise Artificial Intelligence Transformation. The first of its kind, this book grants technology leaders the insight to create and scale their AI capabilities and bring their companies into the new generation of technology. As AI continues to grow into a necessary feature for many businesses, more and more leaders are interested in harnessing the technology within their own organizations. In this new book, leaders will learn to master AI fundamentals, grow their career opportunities, and gain confidence in machine learning. Enterprise Artificial Intelligence Transformation covers a wide range of topics, including: Real-world AI use cases and examples Machine learning, deep learning, and slimantic modeling Risk management of AI models AI strategies for development and expansion AI Center of Excellence creating and management If you're an industry, business, or technology professional that wants to attain the skills needed to grow your machine learning capabilities and effectively scale the work you're already doing, you'll find what you need in Enterprise Artificial Intelligence Transformation.
Author: Ulrich Harms Publisher: Springer Science & Business Media ISBN: 3540687785 Category : Science Languages : en Pages : 372
Book Description
This volume provides a review and synthesizes the accomplishments of the past decade of the International Continental Scientific Drilling Program. More importantly, it defines opportunities for scientific advancement through future drilling projects addressing a broad range of disciplines in the Earth Sciences. In addition there is a review of all past projects that were supported by the ICDP, as well as of technical aspects associated with continental drilling.
Author: Bettina Hüttenrauch Publisher: Springer ISBN: 3658145773 Category : Business & Economics Languages : en Pages : 343
Book Description
This study delivers insights on which external sources – e.g. website click behavior, surveys, or social media data – can and cannot be used for data augmentation. A case study is performed to test the suitability of different sources in order to create a generalized practical guide for data augmentation in marketing. Data augmentation is a beneficial tool for companies to use external data, if the internal data basis for targeting is not sufficient to reach the customers with the highest propensity. This study shows that augmenting data from feasible sources leads to significant conversion lifts.
Author: René Michel
Author: René Michel
Author: Alexander Chernev
Author: Eric Siegel
Author: Petra Perner
Author: Mingxiu Hu
Author: Rashed Haq
Author: John H. Goodrich
Author: Burkhard O. Dressler
Author: Ulrich Harms
Author: Bettina Hüttenrauch