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Targeting Uplift

An Introduction to Net Scores

Igor Schnakenburg, Tobias von Martens, René Michel, et al.

PDF
ca. 96,29 €
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Springer International Publishing img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik

Beschreibung

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. 


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Schlagwörter

gross scoring, incremental response, campaign management, personalized treatment learning, uplift modelling, predictive modelling, net scoring, software implementation of net scoring algorithms, scoring algorithm, applications of net scoring in markerting, enhanced scoring