Autors: Petrova-Dimitrova, V. S., Budakova, D. V.
Title: OPMAX Learning Agent Optimizing Performance Max Campaigns Using Random Forest Algorithm
Keywords:

Abstract: In this paper a new OPMAX Learning Agent has been proposed, which was trained to find the most appropriate Target Return on Ad Spend (Target ROAS) for all Performance Max campaigns within a Google Ads account, based on specific requirements of a particular user. For this purpose, a new structure of the agent is proposed, and the Random Forest Algorithm is used for its training. The machine learning algorithms used in Google Ads relay on a lots of signals during in their analyzes and observation, but there are additional requirements from the advertisers that are hard to be reached by only adjusting the existing settings of the ad campaigns. The campaigns need to be updated with different values in order to meet these goals. It is very difficult and time consuming for a person to predict what should be the exact change in the Target ROAS setting, in order the campaign to meet user’s goal. The OPMAX Learning Agent will be used to predict the most appropriate value of the Target ROAS, based on the goals it has, and then it will update them in the Google Ads platform. It will work in parallel with smart bidding strategies of Google Ads, and it will make changes to the campaigns if the agent decide that an update is needed. Applying more appropriate Target ROAS improves the overall account performance and aims to achieve the desired return on ad spend.

References

  1. D. Mahakal, The Impact Of Artificial Intelligence AI in Digital Marketing. Journal of Global Economy, 19(2), 30-45, 2023
  2. N. Thilagavathy, E. Praveen Kumar., Artificial intelligence on digital marketing-An overview, Nveo-Natural Volatiles & Essential Oils Journal| NVEO: 9895-9908, 2021
  3. J.R. Saura, D. Ribeiro-Soriano, D. Palacios-Marqués, Setting B2B digital marketing in artificial intelligence-based CRMs: A review and directions for future research, Industrial Marketing Management 98: 161-178, 2021
  4. E. Mogaji, TO Soetan, TA Kieu, The implications of artificial intelligence on the digital marketing of financial services to vulnerable customers, Australasian Marketing Journal, 2020
  5. D. Dumitriu, M.A.M Popescu, Artificial intelligence solutions for digital marketing, Procedia Manufacturing: 630-636, 2020
  6. R. Almestarihi, A. Ahmad, R. Frangieh, I. Abu-AlSondos, K. Nser, Abdulkrim Ziani, Measuring the ROI of paid advertising campaigns in digital marketing and its effect on business profitability, Uncertain Supply Chain Management 12, no. 2: 1275-1284, 2024
  7. A. Bulut and A. Mahmoud, Generating Campaign Ads & Keywords for Programmatic Advertising in IEEE Access, vol. 11, pp. 43557-43565, doi: 10.1109/ACCESS.2023.3269505, 2023
  8. Yang, Xiao, Daren Sun, Ruiwei Zhu, Tao Deng, Zhi Guo, Zongyao Ding, Shouke Qin, and Yanfeng Zhu. "Aiads: Automated and intelligent advertising system for sponsored search." In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1881-1890, 2019
  9. M. Verma, M. Shaunak, Recommendation systems for ad creation: A view from the trenches, In Proceedings of the 16th ACM Conference on Recommender Systems, pp. 525-527, 2022
  10. L. Jin, L. Huang, R. Qin, A Novel Approach to Customer Lifetime Value Prediction Based on Recurrent Neural Networks, Information Sciences, 490, 193-202, 2019
  11. Campbell, Colin, Sean Sands, Carla Ferraro, Hsiu-Yuan Jody Tsao, and Alexis Mavrommatis. "From data to action: How marketers can leverage AI." Business horizons 63, no. 2: 227-243, 2020
  12. Peng, Yixuan, Sayed Fayaz Ahmad, Ahmad YA Bani Ahmad, Mustafa S. Al Shaikh, Mohammad Khalaf Daoud, and Fuad Mohammed Hussein Alhamdi, Riding the waves of artificial intelligence in advancing accounting and its implications for sustainable development goals, Sustainability 15, no. 19: 14165, 2023
  13. https://support.google.com/google-ads/answer/10724817?hl=en
  14. https://blog.google/products/ads-commerce/performance-max/
  15. https://support.google.com/google-ads/answer/10486637
  16. Rezvan, M. Reza, A. G. Sorkhi, J. Pirgazi, M. Mehdi Pourhashem Kallehbasti, AdvanceSplice: Integrating N-gram one-hot encoding and ensemble modeling for enhanced accuracy, Biomedical Signal Processing and Control 92: 106017, 2024
  17. Ch. Koller, G. Kauermann, X. Xiang Zhu, Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance in Earth Observation?, IEEE Transactions on Geoscience and Remote Sensing, 2023
  18. Pujol, Pere, D. Macho, C. Nadeu, On real-time mean-and-variance normalization of speech recognition features, In 2006 IEEE international conference on acoustics speech and signal processing proceedings, vol. 1, pp. I-I. IEEE, 2006
  19. H. Henderi, T. Wahyuningsih, E. Rahwanto, Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer, International Journal of Informatics and Information Systems 4, no. 1: 13-20, 2021
  20. G. Biau, E. Scornet, A random forest guided tour, TEST 25, 197-227, https://doi.org/10.1007/s11749-016-0481-7, 2016
  21. L. Breiman, Random Forests, Machine Learning 45, 5-32, https://doi.org/10.1023/A:1010933404324, 2001

Issue

AIP Conference Proceedings, vol. 3274, 2025, Albania, https://doi.org/10.1063/5.0258932

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus