Targeting Using Augmented Data in Database Marketing Decision Factors for Evaluating External Sources /

• Κύριος συγγραφέας: Hüttenrauch, geb. Krämer, Bettina (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Gabler, 2016.
• Θέματα: Business.; Marketing.; Business and Management.

 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. Contents • Introduction and strategic motivation to data augmentation in database marketing • Literature review on data augmentation • Methodological framework for data augmentation • Test design for evaluating different source characteristics • Analysis of data augmentation KPIs, case study results and test of hypotheses • Limitations of data augmentation and outlook Target Groups • Researchers and students in the area of CRM, data science, database marketing, and direct marketing • Practicioners in the area of CRM, data science, database marketing, and direct marketing The Author Dr. Bettina Hüttenrauch obtained her doctorate degree at the Johannes Gutenberg Universität Mainz. She currently works as a project manager at a German airline and is responsible for building up the "Analytics Factory" for an advanced analytics program. 

 Introduction and strategic motivation to data augmentation in database marketing -- Literature review on data augmentation -- Methodological framework for data augmentation -- Test design for evaluating different source characteristics -- Analysis of data augmentation KPIs, case study results and test of hypotheses -- Limitations of data augmentation and outlook.