Anyone using lookalike targeting for dating campaigns?

[datingads]

I’ve been running dating campaigns for a while now, and one thing I’ve realized is how tricky it can be to keep growing once you’ve already found your initial audience. At first, you get those early wins—good CTRs, a few conversions here and there—but eventually, the numbers start to stall. It’s like you’ve already reached everyone who’s likely to click, and suddenly, your ads stop feeling fresh.

That’s where I started wondering if lookalike targeting could help. I’d heard a few people mention it, but I wasn’t sure how it actually played out for dating offers, especially since dating traffic tends to be super specific and competitive.

At first, I’ll admit, I was skeptical. The idea of letting an algorithm find “similar” people sounded great in theory, but I worried it might just pull in random audiences who didn’t actually convert. Plus, I wasn’t sure how much data I needed before creating a lookalike audience that made sense.

My first attempt wasn’t perfect. I created a lookalike based on a small email list from one of my campaigns—maybe a few hundred verified users—and launched it on a new ad network. The engagement looked promising, but conversions were all over the place. It felt like the system didn’t have enough info to really understand who my ideal users were.

Then I made a few tweaks. Instead of using raw sign-up data, I filtered out only the high-value users—the ones who actually interacted with the platform for a few days or upgraded to a premium plan. That shift made a huge difference. The algorithm had better input to learn from, and the traffic suddenly started behaving more predictably.

Once I optimized the seed audience, I noticed something interesting: the ads didn’t just perform better, they reached segments I hadn’t even considered before. My main campaign used to attract users in tier-1 regions only, but after testing lookalikes, I started getting solid conversions from smaller, but engaged, markets. It’s like the targeting “expanded” my reach in a smart way—without me doing much extra work.

One thing I learned the hard way, though, is that you can’t treat lookalikes as a plug-and-play fix. If your base audience is messy, your lookalike will just copy that mess. Garbage in, garbage out, basically. You’ve got to clean your data and really understand what kind of users you want before scaling it up.

For example, I had a campaign for a casual dating site that was doing okay in one region. I used lookalike targeting based on all users who signed up. Sounds fine, right? But it ended up attracting tons of window shoppers—people who clicked but never signed up or interacted. When I rebuilt the audience using only people who spent more than five minutes on-site or completed a specific action, the quality went way up.

Also, don’t be afraid to experiment with different percentages. I started with a 1% lookalike audience (closest match) and gradually tested up to 5%. The broader ones worked surprisingly well for scaling once I had a strong base campaign running.

So yeah, from what I’ve seen, lookalike targeting works well for dating campaigns—but only if you feed it the right signals. It’s not a shortcut; it’s more like a way to stretch what’s already working and reach new people who behave similarly to your best users.

If anyone here is stuck at that “plateau” stage where campaigns aren’t growing, I’d honestly recommend giving this a try. You can read more about it here: Lookalike Targeting Unlocks Next Stage of Dating Campaign Growth.

The cool part is that once you get it right, it feels like the campaign starts running itself more efficiently. Fewer wasted clicks, more consistent sign-ups, and a better sense of who your real audience is.

Of course, it’s not a one-size-fits-all strategy. I’ve had friends who said it didn’t help much for their niche dating offers because the base data wasn’t large enough. So, if you’re just starting out, you might want to focus on building your core audience first before diving into lookalikes. But once you’ve got a decent data pool, it’s absolutely worth testing.

Overall, I’d say lookalike targeting feels like a quiet growth engine. It doesn’t give you overnight success, but it steadily expands your reach in a way that feels natural. And in a space like dating ads—where competition is high and users can be unpredictable—that kind of steady, data-driven expansion can make a big difference.