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Abstract
The rise of digital advertising has transformed the way businesses interact with consumers, making platforms like Meta Ads a cornerstone of marketing strategies. However, achieving optimal efficiency in Meta Ads remains challenging due to the complexity of campaign setups and budget allocation. This study addresses the issue by examining key configurations at three levels: campaigns, ad sets, and individual ads. The research explores how advertisers can tailor campaigns to specific objectives, such as driving traffic or increasing sales, while leveraging ad set customization for audience targeting, placement optimization, and A/B testing. To improve ad performance, this study emphasizes the importance of refining content at the ad level, ensuring alignment with campaign goals. Budget management is also highlighted, contrasting Campaign Budget Optimization (CBO) with Ad Set Budget Optimization (ABO), and offering insights into leveraging these tools to maximize returns. The study further recommends adjusting budgets based on audience behavior patterns, such as spikes in purchasing activity during twin dates or paydays. By providing actionable strategies for configuring Meta Ads, this study contributes to the field of digital marketing by bridging practical implementation and theoretical insights. Evaluation of these strategies is supported through examples of best practices, with recommendations for advertisers to enhance their Meta Ads efficiency through continual testing and strategic budgeting.
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References
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References
Ahmadi, I., Abou Nabout, N., Skiera, B., Maleki, E., & Fladenhofer, J. (2024). Overwhelming targeting options: Selecting audience segments for online advertising. International Journal of Research in Marketing, 41(1), 24–40. https://doi.org/https://doi.org/10.1016/j.ijresmar.2023.08.004
Aiolfi, S., Bellini, S., & Pellegrini, D. (2021). Data-driven digital advertising: benefits and risks of online behavioral advertising. International Journal of Retail & Distribution Management, 49(7), 1089–1110. https://doi.org/10.1108/IJRDM-10-2020-0410
Birch. (n.d.). Facebook campaign structure best practices for more profit. Retrieved May 25, 2025, from https://bir.ch/blog/facebook-campaign-structure
Cvirka, D., Rudienė, E., & Morkūnas, M. (2022). Investigation of Attributes Influencing the Attractiveness of Mobile Commerce Advertisements on the Facebook Platform. Economies, 10(2). https://doi.org/10.3390/economies10020052
Deng, Y., Golrezaei, N., Jaillet, P., Liang, J. C. N., & Mirrokni, V. (2023). Multi-channel Autobidding with Budget and ROI Constraints. https://arxiv.org/abs/2302.01523
Hicham, N., Nassera, H., & Karim, S. (2023). Strategic Framework for Leveraging Artificial Intelligence in Future Marketing Decision-Making. In Journal of Intelligent Management Decision. https://doi.org/https://doi.org/10.56578/jimd020304
Intlum. (n.d.). A/B Testing – Everything You Need to Know for Website Improvement – website design company in Kolkata, India – Intlum. Retrieved May 25, 2025, from https://www.intlum.com/blog/ab-testing/
Lee, C. (n.d.). What You Need To Know About Meta Ads. Retrieved May 25, 2025, from https://www.calvyn.com/what-you-need-to-know-about-meta-ads/
Lee, S.-Y., Runge, J., Yoo, D., Bart, Y., Gyurak, A., & Schneider, J. W. (2024). COVID-19 Demand Shocks Revisited: Did Advertising Technology Help Mitigate Adverse Consequences for Small and Midsize Businesses? https://arxiv.org/abs/2307.09035
Nuara, A., Trovò, F., Gatti, N., & Restelli, M. (2022). Online joint bid/daily budget optimization of Internet advertising campaigns. Artificial Intelligence, 305, 103663. https://doi.org/https://doi.org/10.1016/j.artint.2022.103663
Romero Leguina, J., Cuevas Rumin, Á., & Cuevas Rumin, R. (2021). Optimizing the Frequency Capping: A Robust and Reliable Methodology to Define the Number of Ads to Maximize ROAS. Applied Sciences, 11(15). https://doi.org/10.3390/app11156688
Taylor, C. R., & Carlson, L. (2021). The future of advertising research: new directions and research needs. Journal of Marketing Theory and Practice, 29(1), 51–62. https://doi.org/10.1080/10696679.2020.1860681
Tomlinson, M. (n.d.). Meta (Facebook) Ads Manager Made Simple. Retrieved March 25, 2025, from https://www.conjura.com/
Wang, H., Du, C., Fang, P., He, L. I., Wang, L., & Zheng, B. (2023). Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2314–2325. https://doi.org/10.1145/3580305.3599254
Wang, H., Du, C., Fang, P., Yuan, S., He, X., Wang, L., & Zheng, B. (2022). ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4021–4031. https://doi.org/10.1145/3534678.3539211
Wang, Y., Su, Z., & Yan, M. (2023). Social Metaverse: Challenges and Solutions. IEEE Internet of Things Magazine, 6(3), 144–150. https://doi.org/10.1109/IOTM.001.2200266
Xing, Y., Zhang, Z., Zheng, Z., Yu, C., Xu, J., Wu, F., & Chen, G. (2023). Truthful Auctions for Automated Bidding in Online Advertising. In E. Elkind (Ed.), Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23 (pp. 2915–2922). International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2023/325
Yang, Y., & Li, H. (2023). Keyword decisions in sponsored search advertising: A literature review and research agenda. Information Processing & Management, 60(1), 103142. https://doi.org/https://doi.org/10.1016/j.ipm.2022.103142
Zhang, Q., Liao, X., Liu, Q., Xu, J., & Zheng, B. (2022). Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, 1368–1376. https://doi.org/10.1145/3488560.3498479
Zhu, Y., Liu, Y., Xie, R., Zhuang, F., Hao, X., Ge, K., Zhang, X., Lin, L., & Cao, J. (2021). Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 4005–4013. https://doi.org/10.1145/3447548.3467093