Marketing Campaign Management Using Machine Learning Techniques: An Uplift Modeling Approach

Meltem Sanisoğlu*, Tolga Kaya, Şebnem Burnaz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


Companies regularly plan and implement marketing campaigns in order to increase their sales by communicating with the customers. Whether these marketing campaigns are effective and successful in persuading customers to engage in certain behaviors remain a question unanswered. Predicting customer behavior is not enough to drive and manage marketing actions optimally because it does not show the influence of the marketing action on the customers’ future behavior. Prescriptive analysis can offer better ways to guide marketing strategies. Uplift modeling promises a clear opportunity to diminish costs compared to traditional predictive analytics by simply maximizing the impact for any treatment decision where the objective is to apply an influence. The purpose of this paper is to suggest an uplift modeling framework in cross-sell marketing campaign management in telecommunications sector. Dataset used in this study includes demographic and behavioral characteristics of 21.439 customers comprising December 2020-January 2021 period. Using alternative machine learning techniques, we segmented the business customers of a leading telecommunication company into 4 groups as: persuadables, sure things, lost causes, do not disturbs.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference, Volume 2
EditorsCengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, A. Cagri Tolga, Selcuk Cebi
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Print)9783031091759
Publication statusPublished - 2022
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2022 - Izmir, Turkey
Duration: 19 Jul 202221 Jul 2022

Publication series

NameLecture Notes in Networks and Systems
Volume505 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Business to business marketing
  • Campaign management
  • Machine learning
  • Supervised learning
  • Uplift modelling


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