There is a growing satisfaction in creating seamless customer experiences, which is reflected in customers' high expectations for brands and companies. When switching from being consumers to product builders, this focus on customer experience can be lost, but it's essential to remember that customers never forget their satisfaction levels. Leveraging machine learning and AI-powered optimizations can help take marketing performance to new heights. AI can predict users' next shopping stage, making it easier for them to convert. This is achieved by segmenting and targeting users who are more likely to purchase through remarketing campaigns. Traditional customer segmentation methods often miss outliers, such as "ghost users," who can become a source of revenue if effectively reached. A more nuanced approach using machine learning analyzes browsing activity and history to identify patterns among users, calculating their probability of converting at an individual level. This results in a new criterion for targeting users: converters vs. non-converters. By comparing these probabilities, the model can help identify which shopping stage the customer is likely to enter, allowing for targeted engagement with users based on the stage they're most likely about to enter.