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 language | English |
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Title of host publication | UMAP 2024 - Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization |
Publisher | Association for Computing Machinery, Inc |
Pages | 149-158 |
Number of pages | 10 |
ISBN (Electronic) | 9798400704338 |
DOIs | |
State | Published - 22 Jun 2024 |
Event | 32nd Conference on User Modeling, Adaptation and Personalization, UMAP 2024 - Cagliari, Italy Duration: 1 Jul 2024 → 4 Jul 2024 |
Publication series
Name | UMAP 2024 - Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization |
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Conference
Conference | 32nd Conference on User Modeling, Adaptation and Personalization, UMAP 2024 |
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Country/Territory | Italy |
City | Cagliari |
Period | 1/07/24 → 4/07/24 |
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