November 6, 2025 3:56 AM PST
I’ve been playing around with different targeting methods for gambling advertising lately, and one thing that keeps popping up in discussions is “lookalike audiences.” I’d heard of them before — mostly from Facebook Ads and Google campaigns — but I never really understood how much difference they could make until I actually tried them out. So I figured I’d share my experience here in case anyone else is wondering whether it’s worth the effort.
When I first started running gambling ads, my main challenge was getting quality players, not just clicks. I was driving traffic, sure, but the conversions were all over the place. Some campaigns would get tons of signups but almost no deposits. Others would have decent ROI but crazy high CPAs. I felt like I was constantly chasing an audience that didn’t actually exist.
Someone on another forum mentioned that lookalike audiences helped them narrow down their targeting, so I decided to give it a go. The idea sounded almost too simple — take a list of your best-performing players and let the platform find new people who “look” like them based on behavior and interests. I mean, if it’s that easy, why wasn’t everyone doing it already?
At first, I was skeptical. Gambling audiences are tricky — platforms restrict a lot of targeting options, and lookalikes can be hit or miss depending on your source data. So my initial setup was small. I took a list of about 1,000 users who had deposited more than once and uploaded it to Facebook Ads Manager. I told Facebook to find a 1% lookalike audience in the UK.
The first few days didn’t look promising. The CPC was higher than usual, and CTR was lower. I almost paused the campaign. But by the end of the first week, I noticed something interesting: while clicks were fewer, the conversion rate was much higher. Instead of getting tons of tire-kickers, I was seeing more players who actually deposited and stayed active.
After a couple of weeks, I tested a 2% lookalike as well — slightly broader reach, a bit cheaper traffic — and the ROI stayed stable. That’s when it clicked for me that the secret wasn’t just “using lookalikes,” but choosing the right source audience.
If you’re feeding bad data into the system (like everyone who just clicked your ad but never signed up), you’ll get more of that same behavior. But if you upload a list of players who’ve shown genuine interest — deposits, engagement, retention — the algorithm has better data to work with. I even started segmenting by player type: one list for casino players, one for sports bettors, and one for poker users. The results were night and day compared to my old “throw everything at the wall” approach.
Now, I’m not saying lookalike audiences will magically fix every campaign. They’re more like a shortcut to better targeting — especially when you’re past the testing phase and want to scale. They do need some patience and constant tweaking. I’ve noticed performance can fluctuate if you don’t refresh your seed lists every few weeks. Also, I avoid overlapping audiences because that can make the campaigns compete with each other and drive up costs.
Something else I learned along the way: lookalikes work best when paired with custom audiences. I started excluding my existing depositors from new campaigns so I wasn’t wasting ad spend showing them signup promos they didn’t need. This simple exclusion improved my ROI by around 20% in one campaign.
For anyone on the fence about trying it, I’d say start small — use your best customer list as your base, and let the system learn. If it doesn’t perform right away, give it time. I’ve had campaigns that looked like flops in week one but turned into solid performers by week three once the algorithm had enough data.
If you want a good walkthrough on how to set it up properly, this article breaks it down pretty clearly: Use Lookalike Audiences for Higher ROI. It helped me understand a few nuances I didn’t catch the first time, like which audience sizes work best for gambling and how to track performance metrics beyond just CTR.
At the end of the day, lookalike audiences aren’t some secret hack — they’re just another tool that becomes powerful when you feed it the right data. For me, it took the guesswork out of scaling campaigns and helped focus my budget on players who actually convert.
Curious if anyone else here has experimented with this? Did you see similar results, or did it flop for your niche? Always keen to swap notes on what’s working lately in gambling advertising.
I’ve been playing around with different targeting methods for gambling advertising lately, and one thing that keeps popping up in discussions is “lookalike audiences.” I’d heard of them before — mostly from Facebook Ads and Google campaigns — but I never really understood how much difference they could make until I actually tried them out. So I figured I’d share my experience here in case anyone else is wondering whether it’s worth the effort.
When I first started running gambling ads, my main challenge was getting quality players, not just clicks. I was driving traffic, sure, but the conversions were all over the place. Some campaigns would get tons of signups but almost no deposits. Others would have decent ROI but crazy high CPAs. I felt like I was constantly chasing an audience that didn’t actually exist.
Someone on another forum mentioned that lookalike audiences helped them narrow down their targeting, so I decided to give it a go. The idea sounded almost too simple — take a list of your best-performing players and let the platform find new people who “look” like them based on behavior and interests. I mean, if it’s that easy, why wasn’t everyone doing it already?
At first, I was skeptical. Gambling audiences are tricky — platforms restrict a lot of targeting options, and lookalikes can be hit or miss depending on your source data. So my initial setup was small. I took a list of about 1,000 users who had deposited more than once and uploaded it to Facebook Ads Manager. I told Facebook to find a 1% lookalike audience in the UK.
The first few days didn’t look promising. The CPC was higher than usual, and CTR was lower. I almost paused the campaign. But by the end of the first week, I noticed something interesting: while clicks were fewer, the conversion rate was much higher. Instead of getting tons of tire-kickers, I was seeing more players who actually deposited and stayed active.
After a couple of weeks, I tested a 2% lookalike as well — slightly broader reach, a bit cheaper traffic — and the ROI stayed stable. That’s when it clicked for me that the secret wasn’t just “using lookalikes,” but choosing the right source audience.
If you’re feeding bad data into the system (like everyone who just clicked your ad but never signed up), you’ll get more of that same behavior. But if you upload a list of players who’ve shown genuine interest — deposits, engagement, retention — the algorithm has better data to work with. I even started segmenting by player type: one list for casino players, one for sports bettors, and one for poker users. The results were night and day compared to my old “throw everything at the wall” approach.
Now, I’m not saying lookalike audiences will magically fix every campaign. They’re more like a shortcut to better targeting — especially when you’re past the testing phase and want to scale. They do need some patience and constant tweaking. I’ve noticed performance can fluctuate if you don’t refresh your seed lists every few weeks. Also, I avoid overlapping audiences because that can make the campaigns compete with each other and drive up costs.
Something else I learned along the way: lookalikes work best when paired with custom audiences. I started excluding my existing depositors from new campaigns so I wasn’t wasting ad spend showing them signup promos they didn’t need. This simple exclusion improved my ROI by around 20% in one campaign.
For anyone on the fence about trying it, I’d say start small — use your best customer list as your base, and let the system learn. If it doesn’t perform right away, give it time. I’ve had campaigns that looked like flops in week one but turned into solid performers by week three once the algorithm had enough data.
If you want a good walkthrough on how to set it up properly, this article breaks it down pretty clearly: Use Lookalike Audiences for Higher ROI. It helped me understand a few nuances I didn’t catch the first time, like which audience sizes work best for gambling and how to track performance metrics beyond just CTR.
At the end of the day, lookalike audiences aren’t some secret hack — they’re just another tool that becomes powerful when you feed it the right data. For me, it took the guesswork out of scaling campaigns and helped focus my budget on players who actually convert.
Curious if anyone else here has experimented with this? Did you see similar results, or did it flop for your niche? Always keen to swap notes on what’s working lately in gambling advertising.