TY  - BOOK
U1  - Buch
A1  - Heinzelmann, Matthieu
A1  - Bug, Peter
T1  - Predictive analytics models and collaborative filtering
N2  - In times of e-commerce and digitalization, new markets are opening, young companies have the possibility to grow and new perspectives arise in terms of customer relationship. Customers require more possibilities of personalization. In the same time, companies have access to new and especially more information about the customer. Seems like it was a correlation that could evolve greatly if there weren't privacy issues. Vast amount of data about consumers are collected in Big Data warehouses. These shall be analyzed via predictive analytics and customers shall be classified by algorithms like clustering models, propensity models or collaborative filtering. All these subjects are growing in importance, as they are shaping the global marketing landscape. Marketers develop together with IT scientists new ways of analyzing customer databases and benefit from more accurate segmentation methods as that have been used until now. The following paper shall provide a literature review on new methods of consumer segmentation regarding the high inflow of new information via e-commerce. It will introduce readers in the subject of predictive analytics and will discuss several predictive models. The writing of the paper is not based on own empirical researches, but shall serve as a reference text for further researches. A conclusion will complete the paper.
KW  - predictive analytics
KW  - collaborative filtering
KW  - cluster analysis
KW  - propensity models
KW  - clustering
Y1  - 2016
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-13651
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-13651
SP  - 1
EP  - 51
S1  - 51
PB  - Hochschule Reutlingen
CY  - Reutlingen
ER  -