Predictive analytics models and collaborative filtering

  • 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.

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Metadaten
Name:Heinzelmann, Matthieu; Bug, Peter
URN:urn:nbn:de:bsz:rt2-opus4-13651
Publisher:Reutlingen University
Place of publication:Reutlingen
Document Type:Book
Language:English
Year of Publication:2016
Tag:cluster analysis; clustering; collaborative filtering; predictive analytics; propensity models
Pagenumber:51
First Page:1
Last Page:51
Dewey Decimal Classification:330 Wirtschaft
380 Handel, Kommunikation, Verkehr
Open Access:Ja
Licence (German):License Logo  Creative Commons - Namensnennung, nicht kommerziell, keine Bearbeitung