I’ve been running dating campaigns for a while now, and honestly, targeting used to drive me crazy. You can have the best visuals, the most engaging copy, and a strong landing page—but if your targeting is off, you’re just wasting clicks. For months, I was stuck trying to figure out why some ads performed great for a few days and then just tanked.
At first, I blamed it on timing, budget, or maybe even ad fatigue. But deep down, I knew my targeting wasn’t sharp enough. I was using broad interest targeting, thinking the bigger the audience, the better the chances. Turns out, that’s one of the easiest traps to fall into when promoting dating offers.
So I started digging into what other advertisers were doing differently. I read forum posts, watched a few case study breakdowns, and even tested some weird niche combinations just to see what stuck. After a few months of trial and error, I started noticing a pattern. The biggest improvements came not from the creatives, but from how specific and layered my targeting got.
One of the first changes I made was narrowing down the intent of my audience. Instead of targeting generic “singles” or “dating app” interests, I began looking at behavior-based segments—like users who interacted with relationship-related content or followed influencers in the dating and self-improvement space. These people were more likely to engage because they were already in that “looking to connect” mindset.
Then I got into lookalike audiences. I had ignored them for a while, thinking they were too much work to set up. But once I fed data from my existing signups into the ad platform, the results were night and day. The conversion rate almost tripled over two weeks. The algorithm did a way better job of finding people similar to my best leads than I ever could manually.
I also experimented with geo-targeting. Instead of blasting my ads across entire countries, I started focusing on specific cities or regions where engagement was naturally higher. For example, targeting metro areas where dating apps are more popular gave me better results than going broad across all regions. It’s a smaller pool, but it converts way better.
Another thing I noticed was timing. Dating traffic behaves differently throughout the week. Weekends and evenings performed better, especially around 7–11 PM local time. That’s when people are more relaxed and scrolling through their phones. It sounds obvious, but it made a noticeable difference when I adjusted my ad schedule.
After tweaking all this, my campaigns finally started performing the way I wanted. CTRs went up, signups became more consistent, and cost per lead dropped significantly. I wouldn’t say there’s one secret formula—it’s more about layering the right targeting methods together until you find your sweet spot.
If you’re running into the same issue, maybe check out this post I found helpful: Targeting Methods that Can 3x Your Dating Campaign Conversions. It breaks down a few strategies in a pretty easy-to-follow way without the usual marketing fluff.
What really changed my approach was realizing that not all traffic is created equal. A smaller, well-targeted audience can outperform a massive one that’s only vaguely interested. Once you stop chasing volume and focus on quality users, your campaigns start to feel a lot more predictable.
Of course, it’s not perfect. Sometimes even great targeting won’t fix a poor landing page or weak offer, but it definitely gives you a stronger starting point. These days, I spend way less time stressing about “why conversions dropped overnight” and more time refining what’s already working.
If you’re struggling to scale your dating campaigns, I’d say play around with custom audiences, lookalikes, and behavioral segments. It’s not flashy advice, but it’s what’s made the biggest difference for me. And don’t be afraid to test small—sometimes your best insights come from those tiny experiments you weren’t even sure about.
Has anyone else noticed that layering multiple targeting filters actually helps stabilize performance over time? I’d be curious to hear if others found something similar.