Lookalike audiences can be useful for finding new people who share similar traits with those who already converted. After getting enough data from your original audience, you can create a lookalike group based on their behavior, location, or interests. This can help target new users who are more likely to take the same actions. It’s often used in platforms that support data-driven targeting, like Facebook or Google Display. The key is to feed the system with high-quality data so the audience it builds is more accurate. Testing different lookalike levels, like one percent or five percent, can also show which one performs better. Has anyone seen better results using lookalike over broad targeting in display?