Creating an Intelligent Social Media Campaign Decision-Support Method

Amir Gabay, Adir Solomon, Ido Guy, Bracha Shapira

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

Abstract

Predicting the success of marketing campaigns on social media can help improve campaign managers' decision-making (e.g., deciding to stop a marketing campaign) and thus increase their profits. Most research in the field of online marketing has focused on analyzing users' behavior rather than improving campaign manager decision-making. Furthermore, determining the success of marketing campaigns is quite challenging due to the large number of possible metrics that must be analyzed daily. In this study, we suggest a method that incorporates machine learning models with traditional business rules to provide daily decision recommendations, based on the various metrics and considerations, and aimed at achieving the campaign's goals. We evaluate our approach on a unique dataset collected from the most popular social networks, Facebook and Instagram. Our evaluation demonstrates the proposed method's ability to outperform an expert-based method and the machine learning baselines examined, and dramatically increase the campaign managers' profits.

Original languageEnglish
Title of host publicationUMAP 2024 - Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages149-158
Number of pages10
ISBN (Electronic)9798400704338
DOIs
StatePublished - 22 Jun 2024
Event32nd Conference on User Modeling, Adaptation and Personalization, UMAP 2024 - Cagliari, Italy
Duration: 1 Jul 20244 Jul 2024

Publication series

NameUMAP 2024 - Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference32nd Conference on User Modeling, Adaptation and Personalization, UMAP 2024
Country/TerritoryItaly
CityCagliari
Period1/07/244/07/24

Bibliographical note

Publisher Copyright:
© 2024 ACM.

Keywords

  • campaign management
  • datasets
  • decision support
  • machine learning
  • social networks

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Safety, Risk, Reliability and Quality
  • Media Technology
  • Modeling and Simulation

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