Springer 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 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.  From the Back Cover 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.  About the Author René Michel studied mathematics and received his Ph.D. with a focus on statistics from the University of Würzburg, Germany. After working for a consulting company (Altran) for eight years, he is currently a senior analyst and team leader at Deutsche Bank. The core area of his work is data mining in customer relationship management, especially performance measurement for marketing campaigns. He is also a certified SAS trainer and co-author of an introductory book on statistics.After finishing his PhD at the Department of Mathematics, King’s College, London, UK, and his work as a lecturer, Igor Schnakenburg focused on investigating analytical and strategic connections, in particular in the marketing and banking area. He has held various consulting positions and developed prediction models in Germany and abroad. He is an accredited SAS trainer and has taught several courses over the past few years.Tobias von Martens completed a degree in business informatics at the Technical University Dresden, Germany, where he also received his Ph.D. for a dissertation on revenue management and customer value. He has worked for several years as a senior consultant for analytical customer relationship management, mainly for financial service providers, and has focused his consulting and research on the development of scoring models and customer segmentation.