Push ad networks: click fraud & invalid traffic
Push inventory is where invalid traffic concentrates — and where most tools have the thinnest coverage. ValidVisit scores every click per publisher.
Push advertising delivers your creative to notification subscribers, so its traffic quality is really a question about the subscriber list behind each feed. Lists are built in very different ways, and how a list was assembled largely determines how much of its click volume is genuine.
PropellerAdsWhere invalid traffic concentrates
The defining variable is how the subscriber base was acquired:
- Incentivised or deceptive opt-ins — subscribers who clicked "allow" to dismiss a prompt or to claim a reward, with no real interest in the ads that follow. Their clicks are reflexive rather than intentional.
- Bot-subscriber inflation — automated agents enrolled into a feed specifically to generate click volume on schedule.
- Stale or resold lists where the original consent context is long gone.
Quality varies sharply between feeds and sources within a single push network, so the meaningful unit is the source or feed id, not the network.
What ValidVisit scores
Push clicks are weighed against the same 100+ data points — network origin, device, and behaviour — combined into a single 0–100 quality score. Two of those signals are especially telling on push: timing regularity (bot-subscriber feeds often click in unnaturally even bursts aligned to send schedules) and device anomalies (automated agents that behave inconsistently with a real consumer browser). Each click's score is tied to the network's source/feed and click ids.
Pinpointing the sub-source
ValidVisit attributes each scored push click to its source or feed id, so a feed built on incentivised or bot subscribers is isolated from a clean one. You get a per-source breakdown of invalid-traffic rate to inform which feeds to keep buying — the evidence is reported; the buying decision stays with you.
How ValidVisit detects the fraud
Data points → one score
Every click is weighed against more than a hundred independent data points and reduced to a single, sortable 0–100 quality score.
Many angles, combined
Each data point is combined rather than checked in isolation, so a genuine human almost never trips enough of them to be flagged — and bots that beat one rarely beat the rest.
Proprietary, not a black box
The detection model is ours and stays that way. What you get is a clear verdict on every click — not a single brittle rule you can game, and not an unexplained number you can't act on.
Pinned to the source
Every verdict maps to the campaign, publisher and placement that sent the click — so you know exactly which source to cut.
Illustrative example — the same 0–100 score, per source, worst first.
Push networks — FAQ
Why is push traffic quality so variable?+
Because it inherits the quality of the subscriber list. A feed built from genuine, intentional opt-ins behaves very differently from one built on incentivised prompts or enrolled bots — and a single push network carries both. Scoring per source/feed is the only way to tell them apart.
What signals catch bot-subscriber push traffic?+
Unnaturally regular click timing aligned to notification send windows, server-farm or proxy origins behind supposedly consumer subscribers, and device behaviour consistent with automated browsers — among 100+ data points combined into a transparent 0–100 quality score.
Catch the fake clicks on Push networks.
See which campaigns and publishers send real, converting traffic vs bots — every click scored 0–100.
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