Anyone tracking the right metrics for matchmaking ads?

I’ve always been fascinated by how some matchmaking ads just click while others barely get noticed. You know the ones I’m talking about — those ads that make singles stop scrolling because they actually feel relatable or thoughtful. For a long time, I couldn’t figure out why some campaigns hit the mark and others didn’t, even when they had similar visuals or taglines.That’s when I started paying attention to something I’d been ignoring: metrics. Not the usual “how many clicks did I get” kind, but deeper stuff like engagement time, conversation starts, and match completions. Sounds a bit nerdy, I know, but it made me realize most of us are optimizing for the wrong numbers.The struggle of guessing what worksWhen I first began running matchmaking ads, I honestly thought creativity alone would do the job. A clever headline here, a heartwarming image there, and boom — instant engagement. That was the plan. Reality? Not so much.Some ads got plenty of impressions but didn’t drive sign-ups. Others brought in clicks but no one stayed on the landing page long enough to register. It was frustrating. I’d tweak colors, rewrite captions, and change call-to-actions, but the results were unpredictable. It felt like shooting in the dark.The worst part was realizing that I didn’t even know which part of the ad was failing. Was it the targeting? The message? Or maybe the people seeing the ad just weren’t the right audience? Without the right metrics, I couldn’t tell.What changed things for meAfter wasting weeks juggling A/B tests and random tweaks, I decided to actually sit down and study which numbers mattered. Instead of focusing only on CTR or impressions, I started tracking things like:

• Engagement rate: How many people were actually interacting with the ad (liking, commenting, or sharing).
• Conversion quality: Were the sign-ups genuine or just random clicks?
• Time on page: Did visitors spend time reading, or did they bounce right away?
• Cost per message start: A more telling metric for matchmaking campaigns than just cost per click.

Once I started paying attention to those, patterns began to emerge. I noticed that ads using specific emotional tones (like “finding someone who gets you”) got more quality sign-ups than flashy, bold promises (“Find your soulmate now!”).Also, audience segmentation played a big role. The metrics showed that mature singles responded better to softer imagery and conversational language, while younger audiences preferred humor and energy. These insights were buried in the data the whole time — I just wasn’t looking for them.Small tweaks, big differenceOne thing I stopped doing was chasing “viral” appeal. Instead, I aimed for relevance. An ad might have fewer likes, but if those few interactions lead to actual matches or sign-ups, that’s a win. I started treating metrics like a compass rather than a scoreboard.Over time, this mindset helped me refine not just my ad creatives but also my landing pages and audience targeting. I wasn’t making random changes anymore; I was making informed ones. And that made a huge difference.If you’re still figuring out which metrics to track or how to read them, this post helped me a lot:  Use these metrics to create matchmaking ads . It breaks down the specific numbers that matter most for matchmaking ad performance.What I’d suggest to othersIf I had to sum up my takeaway, it’d be this: stop chasing vanity metrics. Big numbers might look good on paper, but they don’t always mean your campaign is doing well. Instead, dig into how your audience behaves after they see your ad.Do they engage? Do they sign up? Do they start conversations? Those are the real signs of success.You don’t need a fancy analytics setup to get started. Even basic ad dashboards can tell you a lot if you know what to look for. Keep testing, stay curious, and don’t be afraid to ditch what isn’t working — even if it’s something you really liked.At the end of the day, good matchmaking ads aren’t just about looks or clever lines. They’re about understanding what your audience responds to and using that feedback to keep improving. Once I started doing that, the guesswork faded, and the results started to make sense.