Targeting Uplift An Introduction to Net Scores
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
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ing Uplift An Introduction to Net Scores
Targeting Uplift
René Michel • Igor Schnakenburg • Tobias von Martens
Targeting Uplift An Introduction to Net Scores
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René Michel Deutsche Bank AG Frankfurt am Main, Germany
Igor Schnakenburg DeTeCon International GmbH Berlin, Germany
Tobias von Martens Deutsche Bank AG Frankfurt am Main, Germany
ISBN 978-3-030-22624-4 ISBN 978-3-030-22625-1 (eBook) https://doi.org/10.1007/978-3-030-22625-1 Mathematics Subject Classification (2010): 62F03, 62-02, 62-07, 62P20, 90B60, 91G70 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Uplift modeling is a data mining approach to a specific challenge that occurs in various areas of application: predicting the impact of a treatment and selecting those items (such as customers, patients, or machines) on whom the predicted impact is (most) beneficial. Consequently, uplift modeling maximizes the effectiveness of treatments. The topic has received considerable attention by researchers and practitioners. In 2015, a Gartner study (see [1]) mentioned it as one of the upcoming hot topics of advanced analytics with much potential ahead. Currently, most of the relevant articles and white papers are focused on methods and applications of uplift modeling in direct marketing, churn prevention, and medicine. This book aims at covering all relevant aspects of uplift modeling from a general point of view, such as data preparation, modeling methods, assessment of uplift models, software implementations, and potential specific areas of application. The audience targeted by this book comprises data scientists, especially researches and practitioners in predictive modeling and s
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