Learning to Advertise: Reinforcement Learning for Automated Ad Campaign Optimization for Small Businesses

Abstract Small businesses often encounter significant hurdles in digital marketing due to limited resources and expertise. To tackle these challenges, we introduce Learning to Advertise (L2A), a framework that harnesses reinforcement learning (RL) for automated ad campaign optimization. This approach customizes advertising strategies to suit individual business needs, aiming to enhance campaign effectiveness and reduce costs. The L2A framework consists of an RL agent that engages with the advertising ecosystem, receiving feedback on critical performance metrics, including click-through and conversion rates. Through a process of iterative learning, the agent gains insights into optimal budget distribution, audience targeting, and creative ad selection. We validate our framework using real-world advertising data from a variety of small businesses. Our findings demonstrate that L2A surpasses traditional optimization techniques, resulting in improved engagement and conversion metrics. Furthermore, the intuitive user interface allows users without extensive marketing knowledge to easily deploy successful ad campaigns, making L2A a practical solution for small businesses seeking to enhance their advertising effectiveness.