How do you filter bots and fake clicks from gambling traffic sources?

[john1106]

Hook: Has anyone else noticed how a campaign can look amazing on day one… and then completely fall apart once you actually check the numbers? I’ve had days where clicks were flowing in, CTR looked healthy, and I thought I nailed it — until I saw zero meaningful engagement.

Pain Point: The biggest issue I’ve faced with gambling traffic is separating real users from bots and fake clicks. At first, I didn’t even realize how bad it was. I just assumed low conversions meant bad creatives or weak landing pages. But after running a few tests across different sources, I started noticing patterns that didn’t make sense — super short session times, identical device types, strange spikes at odd hours, and traffic from locations I wasn’t even targeting properly.

Personal Test / Insight: What really opened my eyes was digging into basic analytics instead of just looking at click numbers. I compared bounce rates, time on page, and scroll depth between traffic sources. One source sent tons of visitors, but almost all of them left within two seconds. No scrolling, no interaction, nothing. Another source sent fewer clicks, but people actually browsed the site. That told me more than any dashboard summary ever could.

I also started spacing out my budget instead of pushing everything at once. When you spread traffic throughout the day, it’s easier to see unnatural spikes. Bots tend to come in waves. Real users behave more randomly. I’m not saying this is scientific, but over time you start recognizing patterns. If 80% of clicks arrive in a tight 10-minute window every day, something’s off.

Soft Solution Hint: What helped me most was slowing down and testing small segments before scaling anything. I began whitelisting placements that showed actual engagement instead of trusting automatic optimization. I also began excluding suspicious IP ranges and cutting off sources that looked too “perfect.” If a campaign shows 0% bounce rate or 100% bounce rate consistently, I treat both as red flags. Real user behavior is messy and imperfect.

Another simple thing I do now is monitor device consistency. If traffic claims to be mixed but nearly everything comes from the same browser version or OS build, that’s usually not organic. I also compare conversion paths. Real users click around. Fake ones rarely do.

Helpful Insight:
When I was trying to understand how others approach filtering and evaluating gambling traffic, I realized most experienced buyers focus more on behavior metrics than surface-level numbers. Clicks don’t mean much on their own. Engagement tells the real story.

Another thing I learned the hard way is that ultra-cheap clicks often come with hidden costs. I used to chase low CPC aggressively. But if cheap traffic never converts or engages, it’s not cheap — it’s wasted budget. I now calculate cost per engaged visit instead. My rule is simple: if someone doesn’t stay at least 10–15 seconds, I don’t consider that click valuable.

I’ve also experimented with simple verification layers. Nothing aggressive — just enough friction to discourage obvious bots. Sometimes adding minor interaction steps on the landing page helps filter out non-human visits. Real users don’t mind clicking once or twice if the offer is relevant. Automated scripts usually drop off immediately.

Geo analysis is another underrated trick. If I’m targeting specific regions but see traffic from mismatched locations (or data centers), I pause immediately. Even small inconsistencies add up over time. It’s better to cut questionable traffic early than hope it “optimizes later.” In my experience, bad traffic rarely improves.

And honestly, tracking setup matters more than people think. Once I cleaned up my tracking parameters and started labeling campaigns clearly, I could compare traffic sources side by side. Patterns became obvious. Some placements consistently delivered engaged users. Others consistently delivered noise.

I don’t believe there’s a single magic filter that blocks all bots. It’s more about layering small checks: time-on-site, session depth, conversion timing, device spread, hourly distribution. When multiple metrics look unnatural at once, that’s usually enough evidence for me.

At this point, I treat traffic quality as an ongoing audit process rather than a one-time setup. I review numbers daily in the early stages of a campaign. Once a source proves stable, I relax a bit. But I never fully stop monitoring. Things can change quickly.

Anyway, that’s been my approach so far. Nothing fancy — just paying close attention to behavior instead of vanity metrics. Curious how others here handle fake clicks. Do you rely more on tracking tools, manual reviews, or platform filters?