mukeshsharma1106
Member
I’ve been thinking about something lately and figured this forum might be the best place to throw it out there. Has anyone else noticed how hit or miss igaming ppc can feel when you rely only on basic targeting? Some weeks the numbers make sense, and other weeks it feels like the platform is just guessing. That inconsistency pushed me to finally experiment with lookalike audiences, something I’d been ignoring for way too long.
My hesitation mostly came from not being sure whether lookalikes would even work in a niche like ours. Igaming traffic can be unpredictable, and I always assumed the audience signals would be too messy. Plus, I’d heard mixed experiences. A couple of people said lookalikes helped stabilize their CPA, while others said it didn’t move the needle at all. So I kept going back and forth about whether I should try them or just continue tweaking my usual campaigns.
Eventually the pain point got big enough. I hit a stretch where my conversion rate dipped for no clear reason. Same creatives, same landing pages, same GEOs. I’m sure many of you have been there—those weeks where nothing has changed but everything suddenly feels off. That pushed me to dig around and figure out what other people were doing differently. That’s when I circled back to lookalikes. I had enough first-party data sitting unused, and it felt like a waste to keep ignoring it.
My first attempt was rough. I made the classic mistake of building a lookalike from a very broad audience segment. I basically threw all my depositors from different GEOs into one bucket and hoped the platform would magically separate them. It didn’t. The initial campaigns felt too wide, impressions scattered everywhere, and the traffic didn’t really look any better than my regular interest-based campaigns. At that point I almost gave up on the whole idea.
But I kept tweaking out of curiosity. What finally started to make sense was breaking down seed lists by actual behavior instead of generic “players.” I made separate lists for high-value depositors, people who completed a registration but didn’t deposit, and those who had at least engaged with my landing page for more than a few seconds. I didn’t expect the “engaged” list to help, but it turned out to be a more stable seed than the huge mixed group I started with.
Another thing I noticed is that lookalikes behave differently depending on how warm your seed is. When I used my “low-intent” signals, the audience felt too broad, like the platform was pulling in random users who kind of looked similar but didn’t really act similar. When I used a smaller but more committed seed, results were noticeably steadier. Not perfect, but definitely less chaotic than before.
I also learned not to rely on just a single lookalike percentage. Starting too wide (like 5% or 10%) usually made the traffic too loose. A tighter 1% or 2% range felt more predictable, especially in top GEOs. Once those performed decently, expanding outward felt safer. I guess the trick for me was thinking of lookalikes as something to explore slowly rather than flipping a switch.
After a few weeks of testing, I found that combining lookalikes with simple A/B creative changes helped even more. For some reason, the audience responded differently depending on how “casual” or “direct” the creative looked. It wasn’t that the ads were better or worse—it was more about how aligned they felt with the behavior of people in the seed. That’s something I never paid attention to before.
Around this time, I came across a breakdown that explained lookalikes in the igaming context in a way that actually made sense to me. It pointed out how behavior-based seeds usually outperform demographic seeds in niches where intent matters more than the user profile. It’s not a big revelation, but when I read it, things finally clicked. If anyone wants to read it, here’s the link I found helpful: boost iGaming PPC ROI with lookalikes.
To be clear, lookalikes didn’t magically fix everything. I still get fluctuations, and some GEOs respond better than others. But the general trend has been more predictable. When something dips now, it doesn’t dip as sharply. And when something performs well, it holds steady for a bit longer than before. I think that alone makes the testing worth it.
So if anyone else here is debating whether to try lookalikes in igaming ppc, my take is: use them, but don’t rely on them blindly. Start small, choose seed lists that actually reflect valuable actions, and don’t expect instant wins. For me, they worked best as a stabilizer rather than a magic solution. Curious if others here had the same experience or if your results were totally different.
My hesitation mostly came from not being sure whether lookalikes would even work in a niche like ours. Igaming traffic can be unpredictable, and I always assumed the audience signals would be too messy. Plus, I’d heard mixed experiences. A couple of people said lookalikes helped stabilize their CPA, while others said it didn’t move the needle at all. So I kept going back and forth about whether I should try them or just continue tweaking my usual campaigns.
Eventually the pain point got big enough. I hit a stretch where my conversion rate dipped for no clear reason. Same creatives, same landing pages, same GEOs. I’m sure many of you have been there—those weeks where nothing has changed but everything suddenly feels off. That pushed me to dig around and figure out what other people were doing differently. That’s when I circled back to lookalikes. I had enough first-party data sitting unused, and it felt like a waste to keep ignoring it.
My first attempt was rough. I made the classic mistake of building a lookalike from a very broad audience segment. I basically threw all my depositors from different GEOs into one bucket and hoped the platform would magically separate them. It didn’t. The initial campaigns felt too wide, impressions scattered everywhere, and the traffic didn’t really look any better than my regular interest-based campaigns. At that point I almost gave up on the whole idea.
But I kept tweaking out of curiosity. What finally started to make sense was breaking down seed lists by actual behavior instead of generic “players.” I made separate lists for high-value depositors, people who completed a registration but didn’t deposit, and those who had at least engaged with my landing page for more than a few seconds. I didn’t expect the “engaged” list to help, but it turned out to be a more stable seed than the huge mixed group I started with.
Another thing I noticed is that lookalikes behave differently depending on how warm your seed is. When I used my “low-intent” signals, the audience felt too broad, like the platform was pulling in random users who kind of looked similar but didn’t really act similar. When I used a smaller but more committed seed, results were noticeably steadier. Not perfect, but definitely less chaotic than before.
I also learned not to rely on just a single lookalike percentage. Starting too wide (like 5% or 10%) usually made the traffic too loose. A tighter 1% or 2% range felt more predictable, especially in top GEOs. Once those performed decently, expanding outward felt safer. I guess the trick for me was thinking of lookalikes as something to explore slowly rather than flipping a switch.
After a few weeks of testing, I found that combining lookalikes with simple A/B creative changes helped even more. For some reason, the audience responded differently depending on how “casual” or “direct” the creative looked. It wasn’t that the ads were better or worse—it was more about how aligned they felt with the behavior of people in the seed. That’s something I never paid attention to before.
Around this time, I came across a breakdown that explained lookalikes in the igaming context in a way that actually made sense to me. It pointed out how behavior-based seeds usually outperform demographic seeds in niches where intent matters more than the user profile. It’s not a big revelation, but when I read it, things finally clicked. If anyone wants to read it, here’s the link I found helpful: boost iGaming PPC ROI with lookalikes.
To be clear, lookalikes didn’t magically fix everything. I still get fluctuations, and some GEOs respond better than others. But the general trend has been more predictable. When something dips now, it doesn’t dip as sharply. And when something performs well, it holds steady for a bit longer than before. I think that alone makes the testing worth it.
So if anyone else here is debating whether to try lookalikes in igaming ppc, my take is: use them, but don’t rely on them blindly. Start small, choose seed lists that actually reflect valuable actions, and don’t expect instant wins. For me, they worked best as a stabilizer rather than a magic solution. Curious if others here had the same experience or if your results were totally different.
231 Nguyễn Văn Cừ, Phường Chợ Quán, TP. Hồ Chí Minh
