Q: We got banned from a network for 'non-compliant creatives' — what one change got us reinstated?
A: Network compliance rules cover the claims and design in your ads, not just your traffic. One affiliate running a weight-loss offer got banned and assumed it was traffic quality. It wasn't.
The network's compliance team listed the violations:
— A 'lose 10kg in a week' claim with no disclaimer (prohibited medical claim)
— A fake news-site layout mimicking a real publication's logo (trademark + deception)
— A countdown timer that reset on reload (fake scarcity)
The affiliate didn't argue. They rebuilt one compliant lander: realistic claims, a clear 'advertisement' label, no borrowed logos, and an honest (non-resetting) timer.
Result: reinstated in 9 days, and the compliant lander converted only 7% lower than the banned one — a small price for keeping the account alive. The 'aggressive' creative wasn't even worth the risk it carried.
Short version: most creative bans are claims and fake-scarcity violations, not traffic; a compliant lander usually converts within a few points of the risky one.
Still stuck? Drop your case in the comments.
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Больше про due diligence checklist — @TheDealDesk
A: Network compliance rules cover the claims and design in your ads, not just your traffic. One affiliate running a weight-loss offer got banned and assumed it was traffic quality. It wasn't.
The network's compliance team listed the violations:
— A 'lose 10kg in a week' claim with no disclaimer (prohibited medical claim)
— A fake news-site layout mimicking a real publication's logo (trademark + deception)
— A countdown timer that reset on reload (fake scarcity)
The affiliate didn't argue. They rebuilt one compliant lander: realistic claims, a clear 'advertisement' label, no borrowed logos, and an honest (non-resetting) timer.
Result: reinstated in 9 days, and the compliant lander converted only 7% lower than the banned one — a small price for keeping the account alive. The 'aggressive' creative wasn't even worth the risk it carried.
Short version: most creative bans are claims and fake-scarcity violations, not traffic; a compliant lander usually converts within a few points of the risky one.
Still stuck? Drop your case in the comments.
—
Больше про due diligence checklist — @TheDealDesk
Why did my network reject leads without telling me why?
Q: My affiliate manager just says "quality" and won't elaborate. What's actually going on?
A: Networks rarely spell out rejection logic because doing so hands a roadmap to anyone gaming the system. But the silence isn't random. The most common hidden triggers are:
— Post-conversion behavior on the advertiser side: the lead never picked up a verification call, never funded, or churned in under 24 hours.
— Duplicate fingerprint: same device or hashed email already hit the offer through another source.
— Geo or device mismatch between your tracker and the advertiser's server-side log.
The fix isn't arguing. Ask for the rejection cohort instead: "Can you tell me which sub-IDs or which day the rejects clustered on?" That's a question managers can answer without exposing detection rules, and it points you at the leaky placement.
KYC here means Know Your Customer — the advertiser's identity check after the lead lands. Most "quality" rejects are KYC failures you never see.
Short version: stop asking why, start asking which. Cluster data is shareable; detection logic isn't.
Still stuck? Drop your case in the comments.
Q: My affiliate manager just says "quality" and won't elaborate. What's actually going on?
A: Networks rarely spell out rejection logic because doing so hands a roadmap to anyone gaming the system. But the silence isn't random. The most common hidden triggers are:
— Post-conversion behavior on the advertiser side: the lead never picked up a verification call, never funded, or churned in under 24 hours.
— Duplicate fingerprint: same device or hashed email already hit the offer through another source.
— Geo or device mismatch between your tracker and the advertiser's server-side log.
The fix isn't arguing. Ask for the rejection cohort instead: "Can you tell me which sub-IDs or which day the rejects clustered on?" That's a question managers can answer without exposing detection rules, and it points you at the leaky placement.
KYC here means Know Your Customer — the advertiser's identity check after the lead lands. Most "quality" rejects are KYC failures you never see.
Short version: stop asking why, start asking which. Cluster data is shareable; detection logic isn't.
Still stuck? Drop your case in the comments.
My traffic is real, so why is my fraud score high?
Q: I drive genuine humans, but the platform flags 40% as suspicious. How?
A: A fraud score isn't a verdict on whether a click is a bot — it's a probability built from dozens of weak signals stacked together. Real traffic can score high when those signals correlate badly:
— Datacenter or VPN IPs: privacy-conscious users on commercial VPNs look identical to fraud farms.
— Outdated or spoofed user agents: a real user on a 5-year-old phone trips "stale device" rules.
— Timezone-to-IP mismatch: a traveler, or a misconfigured app, reads as geo-faking.
— Click-to-install time too fast: an impatient user can look automated.
None of these mean fraud. They mean ambiguity, and the scorer plays it safe.
Your job is to reduce ambiguity, not hide it. Pass clean referrer data, don't strip user agents, and segment VPN-heavy placements so they're judged on their own merits instead of dragging your whole average down.
Short version: a high score is the model admitting uncertainty, not catching you. Feed it cleaner context.
Still stuck? Drop your case in the comments.
Q: I drive genuine humans, but the platform flags 40% as suspicious. How?
A: A fraud score isn't a verdict on whether a click is a bot — it's a probability built from dozens of weak signals stacked together. Real traffic can score high when those signals correlate badly:
— Datacenter or VPN IPs: privacy-conscious users on commercial VPNs look identical to fraud farms.
— Outdated or spoofed user agents: a real user on a 5-year-old phone trips "stale device" rules.
— Timezone-to-IP mismatch: a traveler, or a misconfigured app, reads as geo-faking.
— Click-to-install time too fast: an impatient user can look automated.
None of these mean fraud. They mean ambiguity, and the scorer plays it safe.
Your job is to reduce ambiguity, not hide it. Pass clean referrer data, don't strip user agents, and segment VPN-heavy placements so they're judged on their own merits instead of dragging your whole average down.
Short version: a high score is the model admitting uncertainty, not catching you. Feed it cleaner context.
Still stuck? Drop your case in the comments.