Which ActiveRevenue zones send bots and invalid traffic?
Not all ActiveRevenue traffic is equal. ValidVisit scores every visit 0–100 and pins it to the exact zone that sent it — so you can tell real humans from bots and invalid clicks, worst zones first.
source in ActiveRevenueThe buyer pastes bad source IDs into the campaign’s Micro Targeting sources field set to Blacklist mode — sub-source IDs formatted with a leading dot (e.g. ".12456" for {source_subid}) and publisher feed IDs ({pubfeed}/{source}) as-is — or lets the CPA-goal Auto Blacklist add underperformers automatically.
ValidVisit reports the device, OS, browser — down to the version — plus the language and ISP behind every flagged visit, and ActiveRevenue supports OS, browser version, device type and connection type targeting. The segments we flag are segments you can exclude.
How invalid traffic slips in through ActiveRevenue zones.
ActiveRevenue sells pop, push, native and banner inventory sourced largely through publisher feeds, and its click parameters mirror that structure: a {conversion} token carrying the unique click identifier, a {campaign}, a {source_subid} for the sub-source, a {banner} for the creative, and a {keyword} recording the term the visit was matched against. Two of those are unusual, and both change how quality has to be read. The {keyword} means traffic here frequently arrives through a search or feed match rather than a fixed placement, so intent can differ enormously between two clicks from the same source. And the source itself is two-tiered — a publisher feed sits above the sub-sources inside it, and the campaign’s Micro Targeting blacklist distinguishes the two by syntax, with sub-source IDs written with a leading dot and feed IDs written plain. Judging quality across that structure needs a measurement finer than the feed. ValidVisit scores every inbound visit against 100+ data points covering the network the click arrived from, the device behind it, and how the session behaves on the page, producing one 0–100 quality score per visit that attaches to both the {source_subid} and the {keyword} that produced it.
The distinctive risk on feed-sourced inventory is keyword-matched arbitrage, where the term attached to a click is not evidence of what the visitor wanted. A feed partner acquires visitors cheaply — from pop inventory, from parked domains, from a toolbar or extension prompt — and matches them against commercially valuable terms before passing the click on. The {keyword} that reaches your report reads like high intent while the person behind it never typed it, and because the match happened upstream there is nothing in the keyword itself to give this away. It shows up instead as a mismatch between the value the term implies and how the session actually behaves: a click matched to an expensive commercial keyword whose visit carries none of the deliberateness that a genuine search for it would.
The second pattern follows from the two-tier structure: a feed’s aggregate can look acceptable while one sub-source carries almost all of the invalid volume. Publisher feeds bundle many sub-sources, and their quality is rarely uniform — an established feed will often contain a handful of sub-sources reselling acquired traffic alongside a majority sending real people. Judging at the feed level therefore produces the two worst outcomes available: block the feed and you lose the good sub-sources with the bad, keep it and you keep paying for the bad ones. Scoring at the {source_subid} level is what makes the proportionate decision possible, and it is also the level the blacklist can address.
The third is automation on pop and push inventory, where the format offers cover. A pop click arrives with no deliberate intent from anyone, real or otherwise, so a short shallow session proves nothing on its own. What continues to separate the two is internal consistency — whether the connection characteristics, the device profile and the on-page activity agree with each other and with the browser the session claims to be running. ActiveRevenue reports the device, browser and connection address behind its clicks, though not the OS version or language, so several of those cross-checks are available from the platform side and a couple are not; the score weighs what the page measures directly either way.
What to watch on ActiveRevenue
{source_subid} inside one feedGroup your ValidVisit report by publisher feed, then break each feed down by {source_subid}. A feed whose blended numbers look tolerable but whose volume concentrates in two or three very weak sub-sources is the most common shape here, and it is the one that argues for sub-source blacklisting rather than dropping the feed. This is also the comparison the platform’s own reporting makes hardest to see.
Segment by {keyword} and compare each term’s score profile against how commercially valuable that term is supposed to be. Genuine search intent and quality scores generally move together; a term that reads as high-intent while its visits score poorly is the signature of upstream matching rather than real demand. That combination is worth more than either dimension alone, and it is specific to feed-sourced traffic.
The Micro Targeting field distinguishes the two tiers by formatting, and getting it wrong silently fails to block anything. Sub-source IDs from {source_subid} take a leading dot — ".12456" — while publisher feed IDs go in plain. Before assuming a block did not work, check that the entry matches the tier you meant to cut; a sub-source ID entered without the dot will not be read as one.
ActiveRevenue can add underperformers to the blacklist automatically against a CPA goal, which decides from outcomes rather than traffic quality. Reviewing those automatic cuts against ValidVisit scores separates the two reasons a source can miss a CPA target: one that scores well was sending real people your offer did not convert, and cutting it was a targeting decision disguised as a quality one.
Pinpoint the bot publishers & placements in ActiveRevenue.
ActiveRevenue 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 ActiveRevenue tokens, not by creative (which says nothing about whether a click was human).
Bought as one ActiveRevenue line, a buy reads as a single number. Scored per sub-source, a spread like this illustration runs from 84 down to 19 — the worst is nearly all bots. That’s the leak a blended average hides.
Illustrative: ActiveRevenue traffic scored 0–100 per sub-source, worst first — down to the placement you buy.
Bot / invalid-traffic score broken down by:
- Source Sub ID
{source_subid}
Per-click id: ActiveRevenue 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 ActiveRevenue sub-sources scored this way.
How ValidVisit attributes ActiveRevenue traffic
Each ActiveRevenue 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=activerevenue&utm_medium=pop&vv_click_id={conversion}&vv_campaign_id={campaign}&vv_publisher_id={source_subid}&vv_creative_id={banner}&vv_keyword={keyword}| Token | ActiveRevenue macro | Maps to | Identifies |
|---|---|---|---|
| Unique Click ID | {conversion} | click_id | click |
| Campaign ID | {campaign} | campaign_id | campaign |
| Source Sub ID | {source_subid} | publisher_id | publisher |
| Banner/Creative ID | {banner} | creative_id | creative |
| Keyword | {keyword} | keyword | keyword |
- ActiveRevenue macro
{conversion}- Maps to
- click_id
- Identifies
- click
- ActiveRevenue macro
{campaign}- Maps to
- campaign_id
- Identifies
- campaign
- ActiveRevenue macro
{source_subid}- Maps to
- publisher_id
- Identifies
- publisher
- ActiveRevenue macro
{banner}- Maps to
- creative_id
- Identifies
- creative
- ActiveRevenue macro
{keyword}- Maps to
- keyword
- Identifies
- keyword
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 sources on ActiveRevenue.
Scoring and attribution are the means — the point is cutting the ActiveRevenue 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 ActiveRevenue 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 ActiveRevenue source and zone via the network’s own tokens — so the bad traffic has an address, not just a headline percentage.
- Cut
Take the sources 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 ActiveRevenue and put your next dollar behind the traffic that converts.
ActiveRevenue traffic quality, answered.
Does ValidVisit blacklist ActiveRevenue sources automatically, or is it manual?
It is manual. ValidVisit scores every visit and surfaces the {source_subid} values and publisher feeds carrying weak quality scores. You apply the block yourself in the campaign editor’s Micro Targeting step, which sits after the main targeting and holds both an IP-range field and a Sources field that can run in whitelist or blacklist mode. Mind the formatting: sub-source IDs go in with a leading dot, publisher feed IDs go in plain. There is no automated push from ValidVisit into ActiveRevenue. The workflow is: score in ValidVisit, identify the problem tier, enter it in Micro Targeting with the right syntax.
The {keyword} on a click looks like strong intent. Why would ValidVisit score that visit badly?
Because the keyword records what the click was matched against upstream, not what the person wanted. On feed-sourced inventory the match can happen well before the visitor reaches you — traffic acquired cheaply somewhere else gets associated with a commercially valuable term and passed on — so the term describes the transaction between the feed and the network rather than the visitor’s intent. ValidVisit scores what actually arrives: the network the click came from, the device behind it, and how the session behaves on your page. When a high-value keyword consistently produces low-scoring visits, that gap between the implied intent and the observed one is the finding, and it is one of the more reliable ways to spot feed arbitrage.
Which ActiveRevenue tokens give the most useful segmentation in ValidVisit?
The {source_subid} does the most work, because it identifies the sub-source inside a publisher feed and it is the finest tier the Micro Targeting blacklist can address — so the score lands on the same unit as the control. The {conversion} token, despite the name, is the unique click identifier: it gives per-click identity, ties each scored visit back to an ActiveRevenue click record, and is what carries a server-to-server postback. The {keyword} is the one that makes this network different from most, since comparing implied intent against measured quality is a check the other tokens cannot give you. Adding {campaign} and {banner} lets you confirm whether a weak pattern is sub-source-wide or specific to one creative being served there.
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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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