1. AiSA: Keyword Bidding and ASA Campaign Optimization Model
Apple Search Ads (ASA) is a vital tool for app developers to enhance their app’s visibility in the Apple App Store. By effectively managing ASA campaigns, developers can ensure their apps appear at the top of search results, thereby increasing visibility and potential downloads. Traditional campaign management strategies have relied on manual input and fixed rules, limiting flexibility and adaptability in a rapidly changing online market. Powered by Reinforcement Learning, this project marks a significant advancement towards fully automated ASA campaign management, capable of making informed decisions on budget allocation, CPI targets, and keyword bids without manual oversight.
Reinforcement Learning Model
Training the Model with PPO
AiSA employs the Proximal Policy Optimization (PPO) algorithm for training our reinforcement learning model. This choice was driven by PPO’s effectiveness in balancing the exploration-exploitation trade-off, crucial for learning robust bidding strategies. We fine-tuned hyperparameters such as the learning rate, entropy coefficient, and batch size to optimize the model’s learning process.
The reinforcement learning model operates in our custom simulation environment, ASASim, designed to emulate the ASA bidding ecosystem. It incorporates realistic keyword distributions, budget constraints, and competitor behavior, providing a comprehensive learning space for the model. By simulating a variety of bidding scenarios, the model learns to adjust bids dynamically, aiming to maximize ROI based on real-world ASA data.
Reward Function
Our training approach includes a sophisticated reward function, split into immediate and end-of-day (EOD) rewards. Immediate rewards are dispensed for each keyword bid adjustment, considering the keyword’s performance and its impact on the overall campaign. EOD rewards are based on collective outcomes from all keywords, focusing on the balance between total installs achieved and the budget expended. This dual-reward system encourages the model to optimize both individual bids and overall campaign performance.
Real-world Application and Results
Deployed in real ASA campaigns, AiSA demonstrated its effectiveness through a significant increase in app acquisitions (+44%) and a reduction in cost per acquisition (CPA) by -13%. These results highlight the system’s ability to optimize campaign performance, ensuring a higher return on investment by making data-driven bidding decisions.
Conclusion
AiSA represents a breakthrough in ASA campaign management, leveraging AI to adapt to the dynamic online advertising landscape. By automating the decision-making process, our system not only enhances campaign performance but also significantly reduces the manual effort required in traditional campaign management. Our project sets the stage for future developments in AI-driven advertising strategies, promising even greater efficiency and effectiveness in online marketing campaigns.
References
- Apple Search Ads. (2024).
- Sutton & Barto. (2018). Reinforcement Learning: An Introduction.
- Jeunen, Murphy, & Allison. (2022). Learning to Bid with AuctionGym.
- Géron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow.
- Canva Engineering Team. (n.d.). End-to-end Keyword Bidding for Apple Search Ads.