Anyone else confused by attribution in dating campaigns?

I’ve been running dating campaigns for a while now, and one thing that’s always tripped me up is attribution. You’d think it’s just a matter of tracking clicks and conversions, right? But when I started digging deeper into postback URLs and how they tie into attribution models, things got messy fast.At first, I assumed the postback would solve all my tracking headaches. It’s supposed to “post back” conversion data to the ad network automatically, so you know exactly where every lead or signup came from. But after running a few campaigns, I noticed some numbers just weren’t adding up. The dashboard said one thing, the ad network showed another, and my partner program gave me a completely different count.That’s when I realised — there are gaps in how attribution models interpret data in dating campaigns, especially when postbacks are involved.Where it all started going wrongWhen you’re promoting dating offers, you’re usually juggling multiple sources — social ads, native platforms, programmatic placements, maybe even email traffic. Each of these uses its own tracking logic. The ad network says it’s first-click, the affiliate system claims last-click, and your internal tool might credit based on engagement.So when I connected my postback URLs, I expected all those conversions to align neatly. Instead, I started seeing weird attribution overlaps. One lead was being counted twice, another was disappearing completely, and some were credited to traffic that didn’t even look active.It became clear that while postbacks are great for passing event data, they don’t magically fix attribution logic. They’re only as good as the rules you’ve set behind them.The confusing bit about attribution modelsThis is where I really started reading up. There’s first-click, last-click, linear, time-decay… all fancy terms that sound like a maths class. But in practice, they basically decide who gets credit for a conversion.In dating campaigns, users often browse around before committing. Someone might click on an ad, then disappear for a few days, come back through an organic search, and finally convert through a retargeting ad. Depending on the attribution model, the “winning” click could be from that first ad or the retargeting one.Now, if your postback URL isn’t set to recognise how your network credits conversions, you’ll see data mismatches. That’s what happened to me. The postback was returning conversions to the wrong source because it didn’t “know” which model to trust.What I tried to fix itI went through the usual panic phase — checking pixels, redoing my tracking links, even thinking maybe I’d messed up the macros. But eventually, I found out it was less about a broken setup and more about interpretation.Different platforms talk to each other in slightly different “languages.” One platform might send back an event ID or timestamp that doesn’t line up exactly with what your ad network expects. The result? Conversions fall into a data gap.To narrow it down, I started mapping my conversion path. Literally listing out each traffic source and what attribution logic they used. Then, I adjusted my postback to match the one I trusted most — in my case, last-click attribution via my affiliate network. Once I did that, the numbers started lining up much better.I also tested delaying postback firing slightly (by a few seconds) to make sure the event data had time to sync properly. It’s not something many talk about, but timing can really affect whether the postback gets caught or lost in the shuffle.A simple realisationThe biggest takeaway for me was this: postbacks don’t decide attribution — they just report it. The real issue is how you define which source deserves credit. Once I accepted that, I stopped chasing the “perfect” model and focused on consistency.Now, I keep my postback setup as clean as possible. I make sure every campaign follows the same logic, and I review discrepancies weekly instead of daily. That way, I can spot if a partner’s system or ad source is drifting off.It’s still not perfect — sometimes a few leads go untracked or double-counted — but at least I know why. And honestly, in dating campaigns where traffic comes from so many directions, expecting 100% accuracy is just setting yourself up for frustration.If you’re facing similar confusion, I’d recommend reading this post:  Attribution Model Gaps in Dating Campaign with Postback URL . It helped me make sense of how small data delays and misaligned models can create those annoying tracking gaps.Final thoughtsAttribution in dating campaigns isn’t a plug-and-play situation. Postbacks are powerful tools, but only if you know what logic they’re following. My advice? Pick one model, stick with it across all your platforms, and use postbacks to reinforce consistency — not to solve inconsistencies.Once you understand that the “gap” often comes from mismatched models rather than missing data, you’ll spend less time troubleshooting and more time optimising.Sometimes, being a little less obsessed with perfect attribution can actually help you see the bigger picture — like which campaigns are building real, long-term user engagement instead of just showing numbers on a screen.