Targeting Uplift - An Introduction to Net Scores (Hardcover, 1st ed. 2019)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Release date:	September 2019
First published:	2019
Authors:	Rene Michel • Igor Schnakenburg • Tobias Von Martens
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	352
Edition:	1st ed. 2019
ISBN-13:	978-3-03-022624-4
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Business & Economics > Business & management > Sales & marketing > General Books > Business & Economics > Business & management > Business mathematics & systems > General Books > Computing & IT > Applications of computing > Databases > Data mining
LSN:	3-03-022624-7
Barcode:	9783030226244

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