I launched LiveFaceSwap AI a few weeks ago. It’s a real-time AI face swap product that runs in the cloud, so users don’t need a powerful local GPU.
So far, it has reached:
477 signups
10 paying users
Around 100 visits per day
What surprised me most wasn’t the total traffic — it was how differently each channel converted.
My top signup sources were:
Direct: 227
GitHub: 32
Bing: 32
Google: 32
Toolify: 29
ChatGPT: 21
YouTube: 9
But the 10 paying users came from:
Direct: 5
YouTube: 2
ChatGPT: 1
GitHub: 1
Google: 1
The interesting one is YouTube.
It only brought 9 attributed signups, but 2 of them paid.
Meanwhile, some channels brought much more traffic without producing the same level of conversion.
It’s still a tiny sample size, so I don’t want to overfit the data. But it changed how I think about acquisition.
I used to pay much more attention to traffic and signup numbers. Now I’m starting to care more about:
Where did the paying users actually come from?
For this product, my next focus will probably be SEO + YouTube, while continuing to test smaller channels.
Product: https://livefaceswap.ai
For other indie hackers: have you also found that your highest-traffic channel and highest-converting channel are completely different?
so great
No real channel data yet on my end, still pre-launch. But this makes me want to set up per-channel tracking from day one instead of bolting it on later. Did you have attribution in place before launch, or did you add it after you started seeing signups come in?
Really interesting seeing YouTube bring so few signups but such a high share of paying users. Definitely shows why conversion quality matters more than raw traffic. Would be great to see an update once you hit 1,000+ signups.
This is a really good example of why raw traffic can be a misleading metric. 9 YouTube signups producing 2 paying users is way more interesting than 32 signups from a channel with zero or one conversion. I’d definitely keep tracking this before drawing conclusions though — with only 10 paying users, one or two conversions can completely change the picture. Curious whether you’re able to track what happens after the first visit too, since that could reveal even more about which channels bring users who actually stick around.
The fastest lie detector I've found for paid traffic is the device mix. My first campaign had 8% CTR at nine cents a click and I thought I'd cracked it. Then I noticed 39% of clicks came from tablets and zero from desktop, for a product people research at a desk. Those were accidental taps on game banners, not customers. Rebuilt as plain search and the mix went 85% mobile, 14% desktop, 2% tablet, which is what actual humans look like. Curious whether your 477 skew anywhere weird, it's usually the first place a channel confesses.
Worth digging one layer into that "Direct" bucket, it's often people who saw you somewhere else and typed the URL later, not organic brand recognition. Tag every off-platform mention with a short link for a month and I'd bet Direct splits into YouTube-influenced, Twitter-influenced, and word of mouth. The real signal is that YouTube converted at a far higher rate than everything else, that means the content is pre-qualifying buyers before they click, worth doubling down on before you scale spend anywhere else.
Two reads on your numbers: (1) 2/9 from YouTube looks great but n=9 — track cost per paying user per channel for another month or two before shifting budget; at small samples that ratio re-ranks everything. (2) Your 'Direct: 5 payers' bucket is probably hiding some AI-search traffic — ChatGPT visits often arrive direct and only a few surfaces get attributed, so the gap between direct and attributed is where the interesting channel actually lives. We built analytics that attributes chat referrals separately for exactly this reason (https://amami.dev).
Answering your closing question with data from my side: yes, and the gap gets wider the deeper you measure. I run free browser tools. Search brings by far the most sessions, but the metric that actually changed my decisions was the percentage of sessions that complete a full run of the tool (and channels re-rank hard on that axis). Traffic sources send visitors, but only some send people who came to do the job. One caution on your table though: 10 payers is a small enough n that one YouTube video with three buyers flips the ranking. I'd let it triple before trusting the order.
Interesting numbers. Curious what the biggest surprise was on the acquisition side — was it which channel actually converted, or how different the quality of users felt across channels?
The gap between signup volume and paid conversion is much more revealing here than the traffic numbers alone. YouTube standing out with only 9 attributed signups is a particularly interesting signal.
High-traffic ≠ high-converting is one of the most common traps in early acquisition, and your numbers show it cleanly. The channel that fills the top of the funnel is rarely the one that produces paying users — traffic builds reach, but conversion needs trust. YouTube is likely under-attributed here: people watch, forget, then come back through direct traffic weeks later, which probably inflates your Direct line more than you think. The question isn't which channel brought signups — it's which one earned the attention that made the buying decision easier. Thanks for sharing the real breakdown, most people only post the impressive numbers.