widget in AdskeeperValidVisit scores each widget_id; stop the bad widgets in Adskeeper's Widgets tab (or blacklist the whole site).
Adskeeper is a self-serve native-teaser network in the MGID family, distributing recommendation widgets across a broad base of publisher sites. Like other native inventory, quality varies sharply by widget, and the CPC teaser model gives some placements an incentive to maximise clicks over outcomes. Adskeeper passes a {widget_id} on every click — the specific widget/site placement, and the highest-resolution unit you can block — plus {teaser_id}, {campaign_id} and a {click_id}. ValidVisit captures those on arrival and grades each click on a 0–100 scale, weighing 100+ independent data points — the network it came from, the device behind it and how the visitor actually behaves — into one quality score, so genuine humans pass and bots stand out. The result is reported as invalid traffic per widget, so you can act on the exact placements rather than the network as a whole.
https://yoursite.com/landing?utm_source=adskeeper&utm_medium=native&vv_campaign_id={campaign_id}&vv_creative_id={teaser_id}&vv_publisher_id={widget_id}&vv_click_id={click_id}Native teaser traffic on Adskeeper carries IVT shaped by the supply chain, not the format. The dominant pattern is widget-level arbitrage: a publisher acquires cheap visitors from pop or push sources, routes them onto a content page to build widget-impression volume, and a share of the resulting teaser clicks come from low-intent visitors or automated sessions. Because these arrive through a real browser on a real publisher URL, a crude IP blocklist misses them — ValidVisit reads each click against a far wider set of signals the moment it lands, so server-farm ranges and proxy exit nodes surface even when the page looks ordinary.
A second pattern is automated clicking from scripted HTTP clients or stripped-down browser builds, which stands out on native because the click path runs through several hops and those tools leave a behavioural and technical trail that a real browser session does not. A third is low-intent human traffic bounced onto teaser pages who click reflexively; the per-click quality score separates this from automation, because the remedy differs — a bot-heavy {widget_id} warrants a block, a low-intent one may warrant a bid cut. All of it is attributed to the {widget_id}.
Rank active {widget_id} values by quality score and by the share of clicks in the suspicious/bad tier. Widgets above your campaign baseline are block candidates in the Widgets tab.
Arbitrage widgets often route through residential or hosting proxies. ValidVisit ties each network-origin finding to the {widget_id} so you block the offending placements without losing widgets that deliver real users.
When a single {widget_id} shows a spike in clicks that score low despite a plausible browser footprint, that points to scripted clickers rather than real people — the kind of pattern that only emerges once you weigh device and behaviour together, not the user-agent alone.
The quality score distinguishes automation from involuntary human arrival, so you can block genuinely bot-heavy widgets while only reducing bids on the merely low-intent ones.
Each Adskeeper macro maps to a normalized parameter, so every scored click is pinned to the right campaign, creative and publisher.
| Token | Adskeeper macro | Maps to | Identifies |
|---|---|---|---|
| Campaign ID | {campaign_id} | campaign_id | campaign |
| Teaser (Creative) | {teaser_id} | creative_id | creative |
| Widget (Publisher) | {widget_id} | publisher_id | publisher |
| Click ID | {click_id} | click_id | click |
{campaign_id}{teaser_id}{widget_id}{click_id}Adskeeperitself isn’t the problem — bots and invalid traffic concentrate in a handful of its sub-sources: the publisher, site or zone, and the placement or widget within it. So we roll the score up by those Adskeeper tokens, not by creative (which says nothing about whether a click was human).
Illustrative example — Adskeeper traffic scored 0–100 per sub-source, worst first.
See your own Adskeeper sub-sources scored this way.
Bot / invalid-traffic score broken down by:
{widget_id}The widget / site the click came from — the unit you block in the campaign Widgets tab.Per-click id: Adskeeper passes a unique click id, so we also run velocity, deduplication and repeat-source checks on every click.
Compare bot & invalid-traffic breakdown across every ad network →Every click is weighed against more than a hundred independent data points and reduced to a single, sortable 0–100 quality score.
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.
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.
Every verdict maps to the campaign, publisher and placement that sent the click — so you know exactly which source to cut.
Add ValidVisit's script to your landing page and append Adskeeper's macros — {widget_id}, {teaser_id}, {campaign_id} and {click_id} — to your destination URL. The pixel captures them on arrival and stores a scored verdict per click, segmented by widget and campaign, with nothing on the click path.
Yes. Because {widget_id} is on every click, ValidVisit ranks your widgets by quality and by what is dragging their scores down, and you stop the offenders in the campaign Widgets tab (or blacklist the whole site). ValidVisit reports the evidence; the block is applied in your Adskeeper account.
No. The score is built from 100+ objective data points about the click's origin, the device and the visitor's behaviour — not from engagement proxies that are naturally low for teaser traffic. A real, briefly-engaged visitor looks completely different across those signals than a scripted client or a proxy IP, so honest cheap clicks still land in the passing range.
See which campaigns and publishers send real, converting traffic vs bots — every click scored 0–100.
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