spot / site in TrafficStarsValidVisit ranks the bad spot / site; blacklist them in TrafficStars (or an auto-rule).
TrafficStars is a self-serve adult and mainstream network where pop and popunder placements dominate the inventory. The channel mechanic matters for understanding IVT risk: a pop load is triggered by a publisher-side event, not a deliberate user click. That means a bot operating on TrafficStars inventory does not need to simulate a mouse gesture — it simply needs to load a page that carries the pop tag. The barrier to generating a "session" is structurally lower than on display or native formats, which is why pop inventory across most networks attracts a higher share of automated traffic. TrafficStars surfaces two primary attribution dimensions — {site_id} for the publisher and {adspot_id} for the individual ad spot — and ValidVisit uses both to pin invalid sessions to the exact inventory unit responsible, so you can act at the spot level rather than blacklisting an entire publisher.
https://yoursite.com/landing?utm_source=trafficstars&utm_medium=pop&vv_click_id={click_id}&vv_campaign_id={campaign_id}&vv_campaign_name={campaign}&vv_creative_id={creative_id}&vv_placement_id={adspot_id}&vv_publisher_id={site_id}&vv_keyword={keywords}Pop traffic on TrafficStars reaches your landing page through a forced load, so the IVT patterns you encounter are shaped by that mechanic rather than by click-intent signals.
The most direct pattern is automation hiding behind borrowed IP space. Bot operators acquire pop inventory on specific publisher sites, then route sessions through commercial proxy pools or rented server space to make their traffic look like ordinary home connections. ValidVisit evaluates each arriving click against more than 100 independent data points — covering the network path it took, the device on the other end and the way the visitor actually behaves — and rolls them into one 0-100 quality score, which it then attaches to the {site_id} and {adspot_id} that generated the session. Because this kind of masked traffic tends to concentrate on the same small set of spots rather than distributing naturally across a publisher's inventory, the {adspot_id} dimension often reveals the contamination faster than the publisher-level view.
A second pattern is sessions that arrive looking nothing like the real browser they claim to be. Scripted clients opening pop URLs behave and present themselves in ways that genuine human visits simply do not, and on pop inventory — where the session is opened programmatically by the publisher page rather than by a human navigation choice — those tells are especially common. ValidVisit weighs all of it into the per-click score and surfaces the low-scoring sessions tied to the originating spot.
A third pattern is arbitrage-driven volume from publishers who re-purchase cheaper pop sources and resell the impressions. These sessions arrive in concentrated bursts, show no scroll or interaction depth, and produce a behavioral profile well above your campaign baseline in terms of zero-engagement rate — measurably different from low-intent human visitors. Because both {site_id} and {adspot_id} are captured on every scored session, you can isolate whether a burst is coming from one spot inside an otherwise clean publisher, or from the publisher domain as a whole, which determines how narrowly you can scope your exclusion.
Within a single TrafficStars publisher, individual ad spots often carry very different IVT profiles depending on page position and context. Sorting your ValidVisit report by {adspot_id} lets you exclude high-IVT spots while keeping the rest of a publisher's inventory — a more precise action than a full-domain block.
If a publisher's aggregate IVT share rises sharply relative to your prior sessions from that {site_id}, it often signals that the publisher has added a new traffic source or changed their pop tag placement. A sudden shift is more actionable than an absolute rate, because baseline IVT varies by vertical and offer type.
Pop campaigns disproportionately attract residential proxy services that make bot sessions look like home broadband users. ValidVisit weighs the network path of every click as part of its 0-100 quality score, giving you a read that plain IP-reputation lists alone miss — and linking the low-scoring sessions back to the {adspot_id} where they concentrate.
Automated clients that open pop URLs tend to score poorly because they look and act unlike the real browser they claim to be. When those low-quality sessions cluster on the same {adspot_id}, it points to a specific inventory unit being targeted by scripted openers rather than a broad network-wide pattern.
Each TrafficStars macro maps to a normalized parameter, so every scored click is pinned to the right campaign, creative and publisher.
| Token | TrafficStars macro | Maps to | Identifies |
|---|---|---|---|
| Click ID | {click_id} | click_id | click |
| Campaign ID | {campaign_id} | campaign_id | campaign |
| Campaign Name | {campaign} | campaign_name | campaign |
| Creative ID | {creative_id} | creative_id | creative |
| Ad Spot ID | {adspot_id} | placement_id | placement |
| Site ID | {site_id} | publisher_id | publisher |
| Keywords | {keywords} | keyword | keyword |
{click_id}{campaign_id}{campaign}{creative_id}{adspot_id}{site_id}{keywords}TrafficStarsitself 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 TrafficStars tokens, not by creative (which says nothing about whether a click was human).
Illustrative example — TrafficStars traffic scored 0–100 per sub-source, worst first.
See your own TrafficStars sub-sources scored this way.
Bot / invalid-traffic score broken down by:
{site_id}The publisher site identifier where the ad was shown.Bot / invalid-traffic score broken down by:
{adspot_id}Ad spot (placement / position) identifier on the publisher site (passed as source in the default URL).Per-click id: TrafficStars 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.
Append the TrafficStars macros as query parameters on your destination URL before the landing page loads. The minimum set for useful IVT analysis is {click_id} (for session-level logging), {site_id} (publisher), and {adspot_id} (ad spot). ValidVisit reads these values on arrival and ties the quality score to each dimension. {campaign_id} is useful for segmenting across campaigns but does not add IVT attribution resolution, because invalid traffic on pop is a function of where in the inventory the session originates, not which campaign requested it.
ValidVisit surfaces which {site_id} and {adspot_id} values are generating flagged sessions and shows the quality score behind each one. Exclusions are made manually: you take the flagged spot or publisher IDs from your ValidVisit report and enter them into TrafficStars' targeting exclusions in the campaign settings. There is no automated push to the TrafficStars platform — the workflow is score, review, exclude. That said, having exact IDs rather than vague 'low-quality traffic' feedback makes each exclusion decision fast and defensible.
Network-side filters are calibrated to protect inventory quality across all advertisers on the platform — they are not tuned to your specific offer, funnel, or conversion baseline. ValidVisit scores every session that reaches your landing page independently, weighing more than 100 data points about the network it came from, the device behind it and how the visitor behaves into a single 0-100 quality score, so genuine humans pass and bots stand out. That per-session read, attributed to the exact {adspot_id}, is what lets you make targeted exclusions rather than broad campaign pauses.
See which campaigns and publishers send real, converting traffic vs bots — every click scored 0–100.
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