Which Kadam publishers send bots and invalid traffic?
Not all Kadam traffic is equal. ValidVisit scores every visit 0–100 and pins it to the exact publisher that sent it — so you can tell real humans from bots and invalid clicks, worst publishers first.
site in KadamThe buyer pastes site IDs (one per line) into the campaign’s site blacklist field — the list activates in ~10-15 minutes; alternatively clicks "Add to Blacklist" next to a site ID in the statistics report.
ValidVisit reports the device, OS, browser — down to the version — plus the language and ISP behind every flagged visit, and Kadam supports OS version, browser, language, device type and connection type targeting. The segments we flag are segments you can exclude.
How invalid traffic slips in through Kadam publishers.
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 visit 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.
Kadam’s CIS-heavy publisher base runs on a simple economic gap: a native-teaser site earns per click, while the pop and push traffic needed to fill that site can be bought for a fraction of the payout. That spread is what pulls sites into buying visitors instead of earning them, and it’s the biggest driver of invalid traffic on the network — not a rogue bot operator, just a publisher chasing margin. The bought traffic shows up as an ordinary teaser click on a real browser at a real {site_id}, which is exactly why blocking by IP address alone does nothing: ValidVisit instead reconstructs the connection’s actual origin, and the residential-proxy pools these sites often route through read as unremarkable consumer IPs to anything less thorough.
Two other signatures turn up alongside the bought traffic. Scripted clients and stripped browser builds leave a trail across the click’s several hops that a person sitting at a keyboard never produces — that’s straightforward automation. And some of the volume is real people who landed on the teaser page more or less by accident and clicked out of reflex rather than interest. ValidVisit’s score keeps that group separate from the bot traffic on purpose, because the fix differs — a {site_id} full of bots gets blacklisted outright, one that’s merely full of bored humans is often better handled with a bid cut. Whichever bucket a click falls into, it’s the {site_id} that the finding attaches to.
What to watch on Kadam
Rank active {site_id} values by quality score and by the share of visits in the suspect/invalid tier. Sites well above your campaign baseline are blacklist candidates before you scale into them.
Sites buying in cheap pop/push volume tend to route it through residential or hosting proxy pools to keep the source looking ordinary. ValidVisit ties each of those origin reads back to the {site_id}, so you can blacklist the offending publishers without losing the sites that are sending you real people.
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.
Pinpoint the bot publishers & placements in Kadam.
Kadam itself 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).
Bought as one Kadam line, a buy reads as a single number. Scored per sub-source, a spread like this illustration runs from 83 down to 18 — the worst is nearly all bots. That’s the leak a blended average hides.
Illustrative: Kadam traffic scored 0–100 per sub-source, worst first — down to the placement you buy.
Bot / invalid-traffic score broken down by:
- Site ID
{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 visit.
Compare bot & invalid-traffic breakdown across every ad network →See your own Kadam sub-sources scored this way.
How ValidVisit attributes Kadam traffic
Each Kadam macro maps to a normalized parameter, so every scored click is pinned to the right campaign, creative and publisher.
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}| 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 |
- Kadam macro
{campaign_id}- Maps to
- campaign_id
- Identifies
- campaign
- Kadam macro
{site_id}- Maps to
- publisher_id
- Identifies
- publisher
- Kadam macro
{ad_id}- Maps to
- ad_id
- Identifies
- ad
- Kadam macro
{click_id}- Maps to
- click_id
- Identifies
- click
How the detection works.
Data points → one score
Every visit 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 visit — 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.
How ValidVisit helps you cut fraud and bad sites on Kadam.
Scoring and attribution are the means — the point is cutting the Kadam traffic that wastes your spend. Here’s how ValidVisit gets you a list you can act on.
- Score
See what’s actually landing
You buy Kadam clicks; what arrives are visits. ValidVisit scores each one 0–100 so real humans stand out from bots and invalid traffic — one script, no funnel hop, no fingerprinting.
- Attribute
Pin the fraud to its source
Every scored visit is tied to the exact Kadam site and zone via the network’s own tokens — so the bad traffic has an address, not just a headline percentage.
- Cut
Take the sites off your buy
You get the worst offenders as a ready-to-use list plus postbacks to your tracker — so you can exclude them in Kadam and put your next dollar behind the traffic that converts.
Kadam traffic quality, answered.
What’s involved in wiring ValidVisit up to a Kadam campaign?
Add ValidVisit’s script to your landing page, then carry Kadam’s macros — {site_id}, {ad_id}, {campaign_id} and {click_id} — through on your destination URL. The pixel reads them the moment the click lands and stores a scored verdict broken out by site and campaign. Nothing runs on the click itself — scoring happens entirely after the visitor has already arrived.
How do I identify which Kadam publisher sites to cut?
That’s the main output of the report. {site_id} is captured on every click, so ValidVisit ranks your publisher sites by quality score and flags what’s pulling the weak ones down. You take that list and add the offenders to Kadam’s site blacklist yourself — ValidVisit hands you the evidence, and the blacklist action happens in your Kadam account.
Teaser clicks are cheap by nature — doesn’t that mean real visits score badly too?
No — cost-per-click has nothing to do with the score. It’s built from 100+ data points covering where the click came from, the device behind it and how the visitor moves through the page, not from engagement metrics that are naturally thin for teaser traffic anyway. A real visitor who glances at the page for a few seconds produces a completely different signal profile than a proxy-routed click or an automated session, so genuine cheap clicks still land in the passing range.
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See which publishers and placements send real buyers vs bots — every visit scored 0–100, worst first.
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