User-level incremental conversion ranking without A/B testing

Zhuli Xie and Yong Liu ()
Additional contact information
Zhuli Xie: Oath Inc.
Yong Liu: Oath Inc.

Journal of Marketing Analytics, 2018, vol. 6, issue 2, No 4, 62-68

Abstract: Abstract The traditional approach to build incremental conversion prediction model has to rely on A/B test results. However, under certain intense business environment, A/B testing can be limited by technical support, platform, or budget, and become not practically available. In this paper, we propose an algorithm to rank users by incremental conversions resulted from advertising effects, which is based on user’s conversion history and the output from a conversion prediction model. By appropriately defining who an active user is, this algorithm is proven to work well with real data. In case where an A/B test is not available and incremental conversion-based user targeting is desired, this algorithm offers a practical solution.

Keywords: User targeting; Incremental conversion; Machine learning; Business intelligence; Big data (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41270-018-0031-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jmarka:v:6:y:2018:i:2:d:10.1057_s41270-018-0031-0

Ordering information: This journal article can be ordered from
http://www.springer. ... gement/journal/41270

DOI: 10.1057/s41270-018-0031-0

Access Statistics for this article

Journal of Marketing Analytics is currently edited by Maria Petrescu and Anjala Krishnen

More articles in Journal of Marketing Analytics from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().