Eric Siu shows how Muse agents pull data from Meta's Ad Library across Facebook, Instagram, and WhatsApp to find winning offer ideas. The agent spotted a company promising ROI in 90 days with a 4.5-month track record, then generated custom concepts like a revenue-per-employee scorecard that benchmarks against AI-native peers.
Muse agents are absolutely wild if you use it to scale your Meta ads. Because it's connected to Ad Library and it's connected to Meta ads, you have a lot of wiggle room here and it's able to cross-reference a lot of data from WhatsApp, from Facebook, from Instagram, and so on. So you can see here, I'm just looking at a few offers that it's calling out and it says, oh, this company over here, ROI in 90 days or you don't pay, and it's been running for 4.5 months. That's pretty good. Instead of me having to go into the Ad Library and then do all this, Analysis on my own, I can then say I can ask it more questions. I say, "Hey, you know what? Instead of having you draft it for me, I actually want you to come up with more ideas for my company based on what we sell." And so you can see over here. Oh, here's what we already have, right? So here's what we can build based on what we have already. The qualifier. Okay, a revenue per employee scorecard. Ten questions that benchmark the revenue per employee against AI native peers and spit out the gap. This is pretty helpful because then we can also build a very a nice data set. Around what typical revenue per employee looks like and how that's been going over time. And we can kind of become the de facto company that's scoring around that, right? And follow for more on AI, business, and marketing.
