spot in ClickAdillaValidVisit flags the bad [SPOT_ID] placement; add it to your ClickAdilla campaign blacklist.
ClickAdilla is a self-serve, multi-format SSP — popunder, push, in-page, banner and video — connecting advertisers to a wide publisher base. That breadth makes it scalable but uneven: a small number of bot-heavy sites can absorb a disproportionate share of budget while campaign-level metrics stay ambiguous. ClickAdilla exposes a {site_id} and a {spot_id} (the specific ad spot/zone) on every click, alongside {campaign_id} and a {click_id} — and the site is the unit you blacklist in the campaign. ValidVisit captures these as each click lands, weighs it against 100+ independent data points covering the source network, the device and the visitor's behavior, and condenses them into one 0–100 quality score — then reports which sites and spots are carrying non-human traffic.
https://yoursite.com/landing?utm_source=clickadilla&utm_medium=pop&vv_campaign_id=[CAMPAIGN_ID]&vv_placement_id=[SPOT_ID]&vv_click_id=[CLICK_ID]ClickAdilla's headline formats (popunder, in-page push) fire without a deliberate click, so the format offers no quality filter — automated page-loaders can trigger events at scale. The IVT patterns ValidVisit surfaces map onto the network's site/spot structure. First, infrastructure clustering by site: clicks that trace back to server farms or proxy/VPN exit points pile up inside particular {site_id} values rather than spreading evenly across the campaign.
Second, traffic that doesn't behave like a real browser: across that bundle of 100+ data points, a bot reveals itself through technical and behavioral mismatches that a clean user-agent string can't paper over. Third, clients that load the page but never act like people: sites with a disproportionate share of visitors who pull the HTML yet show none of the runtime behavior a genuine person produces are a reliable tell for automated loaders. Because {site_id} and {spot_id} ride on every click, ValidVisit attributes all of these to the individual site or spot, so one bad source is separable from a campaign that is otherwise clean.
Sort active {site_id} values by quality and by the share of clicks landing in the suspicious/bad tier. Sites running above your baseline are blacklist candidates.
Where a site is mostly clean but one {spot_id} is bad, the spot breakdown lets you cut the placement without losing the site.
Pop and push clicks that originate from cloud hosting or proxy/VPN infrastructure rarely convert. ValidVisit ties each such finding back to the {site_id} so you blacklist the offending sources cleanly.
When a single site shows a heavy concentration of low 0–100 scores, that points to automated loaders — and the verdict rests on signals far harder to fake than a user-agent string.
Each ClickAdilla macro maps to a normalized parameter, so every scored click is pinned to the right campaign, creative and publisher.
| Token | ClickAdilla macro | Maps to | Identifies |
|---|---|---|---|
| Campaign ID | [CAMPAIGN_ID] | campaign_id | campaign |
| Spot / Zone ID | [SPOT_ID] | placement_id | placement |
| Click ID | [CLICK_ID] | click_id | click |
[CAMPAIGN_ID][SPOT_ID][CLICK_ID]ClickAdillaitself 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 ClickAdilla tokens, not by creative (which says nothing about whether a click was human).
Illustrative example — ClickAdilla traffic scored 0–100 per sub-source, worst first.
See your own ClickAdilla sub-sources scored this way.
Bot / invalid-traffic score broken down by:
[SPOT_ID]The specific ad spot / zone within a site.Per-click id: ClickAdilla 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 ClickAdilla's macros — {site_id}, {spot_id}, {campaign_id} and {click_id} — to your destination URL. The pixel captures them as the visitor arrives and stores a scored verdict per click, segmented by site and spot, with nothing on the click path.
Yes. Because {site_id} is on every click, ValidVisit ranks your sources by quality and by what is dragging their scores down, and you blacklist the offenders in your ClickAdilla campaign. ValidVisit surfaces the evidence; the blacklist is applied in your account.
No. The score is built from 100+ technical and behavioral data points about the source, device and visitor — not from time-on-page or scroll depth, which are naturally low for pop. A genuine person and an automated loader on a hosting IP look very different across those data points, regardless of how briefly the human stays.
See which campaigns and publishers send real, converting traffic vs bots — every click scored 0–100.
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