A gateway toward truly responsive customers: using the uplift modeling to increase the performance of a B2B marketing campaign

Meltem Sanisoglu (), Sebnem Burnaz and Tolga Kaya
Additional contact information
Meltem Sanisoglu: Istanbul Technical University
Sebnem Burnaz: Istanbul Technical University
Tolga Kaya: Istanbul Technical University

Journal of Marketing Analytics, 2024, vol. 12, issue 4, No 12, 909-924

Abstract: Abstract It is evident that companies invest considerable resources in creating and implementing marketing initiatives mainly to attract customers and thereby increase their market presence. Determining if these marketing efforts are effective in convincing customers to take desired actions is challenging since companies need to ensure that their marketing campaigns are feasible in order to target the right customers who are most likely to respond to the marketing treatment. Prescriptive analytics provide better means to measure the impact of a marketing campaign on customer behavior when compared to conventional predictive analytics. Uplift modeling presents an opportunity to maximize the incremental impact of marketing treatment by determining the most responsive customer segment who will take positive action only because of receiving the treatment. This paper aims to suggest an uplift modeling approach in a marketing campaign in B2B context for better evaluation of performance which has not gained enough attention as B2C in literature. By applying three uplift modeling techniques to a real-world B2B cross-sell campaign, it is demonstrated that the campaign effectiveness can be increased significantly by determining the customers who are truly responsive to the related campaign.

Keywords: Machine learning; Uplift modeling; Supervised learning; B2B; Marketing campaign management (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41270-023-00254-2 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:12:y:2024:i:4:d:10.1057_s41270-023-00254-2

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

DOI: 10.1057/s41270-023-00254-2

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 ().