Anyone tried advanced targeting for Personal Dating Ads

[johncena140799]

I’ve been messing around with different ad setups lately, and one thing I keep circling back to is how much targeting affects results when running Personal Dating Ads. It’s funny because I used to think dating ads were pretty straightforward—pick an age range, pick a location, press go. But the more I tested, the more I felt like I was only scratching the surface. So I figured I’d post here and see if anyone else has experimented with deeper targeting tactics and what that experience looked like.

For me, it started with a simple frustration. I kept getting traffic, but the quality was all over the place. Some days the clicks looked promising, and other days it felt like the ads were being shown to completely random people. It was like trying to date in real life—you think you’re talking to someone who matches your vibe, and then five minutes later you realize you are absolutely not on the same page. That’s basically how my targeting felt. So I began wondering if there were smarter ways to scale without just throwing more budget at it.

At first, I did what most people probably do: widen the audience and hope the algorithm finds the right folks. Spoiler—it didn’t. Whenever I broadened things too much, the ad costs went up, the conversions dipped, and the leads felt… cold. Almost like the algorithm was guessing. I kept thinking, “There has to be more control here.”

So I tried tightening things instead. I experimented with interest groups, smaller locations, relationship-related behavior segments, and even micro age brackets. It helped a little, but it didn’t scale well. The moment I tried adding more budget, the results went weird again. That’s when I realized it wasn’t about more targeting or less targeting—it was about smarter targeting. The type that adjusts with behavior patterns instead of static filters.

One thing that surprised me was how effective layered behavior signals were. I’m not talking about complicated analytics or anything—just simple patterns. For example, people who recently searched for dating topics behaved differently from people who followed relationship advice pages. Testing both groups showed me that one was actively looking while the other was more “curious but not committed.” That little difference made a huge impact on the kind of clicks I got.

I also noticed that timing mattered more than I expected. Running Personal Dating Ads late at night or during weekends brought in a different crowd compared to early mornings or weekdays. I didn’t expect targeting by activity windows to matter, but it did. It’s almost like people have different emotional energy levels at different times of day. I guess we all do. Some of my best conversions came from showing ads when people were already in “scrolling and thinking about life” mode.

The more I played with it, the more I realized that advanced targeting isn’t one big trick—it’s a lot of small tweaks that add up. Like shaving off audiences that don’t match the vibe. Or splitting your campaigns so each group gets its own message. Or using lookalike audiences based on tiny but high-quality samples instead of bloated datasets. It’s not glamorous, and it’s definitely not plug-and-play, but it made my ads feel more aligned with the people who actually engage.

I also read up on some practical ideas that matched what I was already exploring. This article here — Targeting Strategies to Scale Personal Dating Ads — laid out a few simple concepts that weren’t overly technical. It talked about treating targeting like an ongoing adjustment rather than a one-time setup, which is exactly what I was coming to realize on my own. What helped me most was thinking in terms of “signals” instead of “settings.” Once I stopped obsessing over what to exclude and instead watched how people interacted, things felt clearer.

Another thing I’ll mention is experimenting with soft audience splits. Instead of making one giant campaign, I’d break it into small groups—like one for swipe-happy users, another for people interested in dating tips, and another for fresh singles. The funny thing is that each group responded differently to the same ad. Not drastically, but enough that adjusting the tone for each group made the numbers way smoother.

Did it solve everything? Not really. But it made scaling less chaotic. When things went off track, I could at least figure out why instead of guessing in the dark. And I think that’s the whole point of advanced targeting: not chasing the perfect setup, but learning how people behave inside each audience pocket.

If anyone here has tried other approaches—like niche interests, custom lists, small geos, or even timing-based segmentation—I’d honestly love to hear what worked for you. I’m still experimenting, and I get the feeling there are tons of little discoveries that only show up after a few messy tests.