site in KadamValidVisit flags the bad site_id; add it to Kadam's campaign site blacklist.
Kadam is a self-serve native-teaser and push network with a wide publisher base across global and CIS markets. Like other native inventory, its quality varies sharply by publisher, and the CPC teaser model gives some sites an incentive to maximise clicks over outcomes. Kadam exposes a {site_id} on every click — the publisher site the click came from, and the unit you blacklist in the campaign — plus {ad_id} and {campaign_id}, and a {click_id} for conversion matching. ValidVisit captures those on arrival and judges each click against more than 100 independent data points — the network it came through, the device on the other end and how the visitor actually behaves — folding them into one 0–100 quality score, then reports invalid traffic per site so you can act on the specific publishers rather than the network as a whole.
https://yoursite.com/landing?utm_source=kadam&utm_medium=native&vv_campaign_id={campaign_id}&vv_publisher_id={site_id}&vv_ad_id={ad_id}&vv_click_id={click_id}Native teaser traffic on Kadam carries IVT shaped by the supply chain rather than the format. The dominant pattern is publisher arbitrage: a site acquires cheap visitors from pop or push sources, uses them to build teaser-impression volume, and a share of the resulting clicks come from low-intent visitors or automated sessions. Because these arrive through a real browser on a real publisher URL, IP-only filters miss them — ValidVisit doesn't lean on the address alone but reads the full picture of where the click originated and what's behind it, which matters because arbitrage sites often route through residential proxy pools that look ordinary on paper.
A second pattern is automated clicking from scripts and modified browser builds, easier to surface on native because the click path runs through several hops and the machinery behind the request rarely matches a genuine person sitting at a screen. A third is low-intent human traffic bounced onto teaser pages who click reflexively; the score separates this from automation, because the remedy differs — a bot-heavy {site_id} warrants a blacklist, a low-intent one may warrant a bid cut. All of it is attributed to the {site_id}.
Rank active {site_id} values by quality score and by the share of clicks in the suspicious/bad tier. Sites well above your campaign baseline are blacklist candidates before you scale into them.
Arbitrage sites frequently route through residential or hosting proxies. ValidVisit ties each of those origin findings to the {site_id} so you blacklist the offending publishers without losing sites that deliver real users.
When a single {site_id} shows a cluster of clicks whose underlying technical and behavioural signals don't add up to a real browser, that points to automated clickers rather than people — patterns that hold up even when the user-agent looks plausible.
The quality score distinguishes automation from involuntary human arrival, so you can blacklist genuinely bot-heavy sites while only reducing bids on the merely low-intent ones.
Each Kadam macro maps to a normalized parameter, so every scored click is pinned to the right campaign, creative and publisher.
| Token | Kadam macro | Maps to | Identifies |
|---|---|---|---|
| Campaign ID | {campaign_id} | campaign_id | campaign |
| Site ID | {site_id} | publisher_id | publisher |
| Ad ID | {ad_id} | ad_id | ad |
| Click ID | {click_id} | click_id | click |
{campaign_id}{site_id}{ad_id}{click_id}Kadamitself 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 Kadam tokens, not by creative (which says nothing about whether a click was human).
Illustrative example — Kadam traffic scored 0–100 per sub-source, worst first.
See your own Kadam sub-sources scored this way.
Bot / invalid-traffic score broken down by:
{site_id}Publisher site the click came from — the unit you blacklist in the campaign.Per-click id: Kadam 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 Kadam's macros — {site_id}, {ad_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 site and campaign, with nothing on the click path.
Yes. Because {site_id} is on every click, ValidVisit ranks your publisher sites by quality and by what is dragging the low scorers down, and you add the offenders to your Kadam campaign site blacklist. ValidVisit surfaces the evidence; the blacklist is applied in your Kadam account.
No. The score rests on more than 100 technical and behavioural data points — where the click came from, the device behind it and how the visitor moves through the page — not on engagement proxies that are naturally low for teaser traffic. A real, briefly-engaged visitor looks completely different from an automated session or a proxy-routed click, so genuine human traffic isn't penalised.
See which campaigns and publishers send real, converting traffic vs bots — every click scored 0–100.
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