Which BIGO Ads placements send bots and invalid traffic?
Not all BIGO Ads traffic is equal. ValidVisit scores every visit 0–100 and pins it to the exact placement that sent it — so you can tell real humans from bots and invalid clicks, worst placements first.
placement in BIGO AdsThe buyer switches from automatic to manual placement and unchecks entire media channels (Likee, imo, or the affiliate/audience-network bucket) — there is no documented field to paste a list of bad site/app/sub-source IDs, so a bad-source list can only justify turning off a whole channel (or excluding uploaded GAID/IDFA device-ID lists via Library > Audience > Create a Custom Audience, where supported).
ValidVisit reports the device, OS, browser — down to the version — plus the language and ISP behind every flagged visit, and BIGO Ads supports OS version, language and connection type targeting. The segments we flag are segments you can exclude.
How invalid traffic slips in through BIGO Ads placements.
BIGO Ads sells your creative into in-app inventory across BIGO’s own live-streaming and short-video properties and a wider audience network of third-party apps, all surfacing impressions under the same self-serve buying layer. The supply behind those placements is broad: first-party live-stream and feed slots sit alongside a long tail of partner apps, and every one of them books clicks through the same campaign wrapper. BIGO Ads exposes a {placement_id} on each click that maps to the specific app slot the visit came from, plus a {click_id} that ties the session back to your campaign record. The difficulty is that quality across in-app inventory varies sharply — a rewarded or interstitial slot inside a low-oversight partner app behaves nothing like a genuine viewer tapping through from a live stream, yet both arrive under one wrapper. ValidVisit approaches the split differently: every inbound visit is measured against 100+ independent data points that span the network the click came over, the device behind it, and how the visitor actually behaves once the app hands off to your page, and all of that collapses into one 0–100 quality score for that single visit. Real viewers clear the bar; automated and low-quality sessions surface against it. Because each visit carries its own score, you can separate a placement whose audience is merely disengaged from one with a structural invalid-traffic problem.
In-app inventory on BIGO Ads carries invalid-traffic patterns shaped by how the placement supply actually operates, not by the ad format on the surface. The most persistent issue is incentivized and rewarded placements: some partner apps sit visits inside rewarded flows — tap for in-app currency, an extra life, or to skip a wait — so the click is a reflex to earn a reward rather than any interest in your offer. That inventory produces high click volume with near-zero downstream intent, and because the tap comes from a real device inside a real app session, click-count filters rarely separate it from a genuine viewer. ValidVisit reads how the session behaves once the app hands off to your page, which is where reward-driven traffic gives itself away: the arrival is real, but the engagement past the tap is shallow and mechanical in a way genuine interest is not.
A second pattern distinct to app networks is automation that doesn’t move like a person: a meaningful slice of bot traffic on in-app inventory comes from emulated devices and automated app builds running out of rented infrastructure — the connection characteristics, the device profile, and the on-page activity simply don’t line up with a human viewer on the OS and app they claim to be. In-app click paths thread through several hops before landing on your page, and the longer that journey, the more places the mismatch has to show up.
A third, lower-volume pattern is placement context that doesn’t match the traffic: a slot booked as premium live-stream or feed inventory whose sessions behave like a low-oversight rewarded wall. ValidVisit’s scoring tells this apart from outright automation — the session may read as human across the network and device signals, but its depth and the texture of its engagement read as involuntary arrival rather than genuine interest. The distinction matters because the remedy differs: a bot-heavy {placement_id} warrants exclusion, while a low-intent-but-human {placement_id} may warrant a bid reduction rather than a full block.
What to watch on BIGO Ads
{placement_id} IVT concentrationSegment your ValidVisit report by BIGO Ads’ {placement_id} token. Placements driving a disproportionate share of your click volume alongside low quality scores are the primary signal of app-level invalid traffic. A single {placement_id} whose score profile sits well below your campaign baseline warrants manual exclusion in BIGO Ads’ placement blocklist before that slot’s volume distorts your campaign optimization.
{placement_id}For each placement, look at whether a visit’s poor score is driven mostly by where it came from on the network side or by how it behaved once the app handed off. A {placement_id} whose low scores trace almost entirely to its traffic source points to a structural sourcing problem — the slot is being fed sessions through proxy or datacenter routes. One where the weakness shows up mainly in on-page behavior suggests reward-driven or automated tapping inside the app rather than a wholesale sourcing problem, and may be manageable with a lower bid rather than full exclusion.
{app_id}Compare scores between placements inside the same partner app. If one slot draws a disproportionately low-quality click share while another slot in the same {app_id} draws valid traffic, the issue is more likely the incentivized context of that specific placement than the app as a whole. This comparison helps you exclude the offending {placement_id} without cutting inventory in that app that still performs.
Automated in-app click activity often concentrates outside normal viewing hours, when real audiences are not watching or scrolling. If a high-volume {placement_id} shows quality scores well below your campaign baseline in low-traffic windows but looks valid during peak hours, that time-pattern is itself a diagnostic signal worth including in your manual review before deciding on exclusion.
Pinpoint the bot publishers & placements in BIGO Ads.
BIGO Ads 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 BIGO Ads tokens, not by creative (which says nothing about whether a click was human).
Bought as one BIGO Ads line, a buy reads as a single number. Scored per sub-source, a spread like this illustration runs from 83 down to 17 — the worst is nearly all bots. That’s the leak a blended average hides.
Illustrative: BIGO Ads traffic scored 0–100 per sub-source, worst first — down to the placement you buy.
Bot / invalid-traffic score broken down by:
- Account ID
__ACCOUNT_ID__
Per-click id: BIGO Ads 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 BIGO Ads sub-sources scored this way.
How ValidVisit attributes BIGO Ads traffic
Each BIGO Ads 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=bigo-ads&utm_medium=social&vv_click_id=__SID__&vv_campaign_id=__CAMPAIGN_ID__&vv_campaign_name=__CAMPAIGN_NAME__&vv_publisher_id=__ACCOUNT_ID__&vv_adset_id=__AD_GROUP_ID__&vv_ad_id=__AD_ID__| Token | BIGO Ads macro | Maps to | Identifies |
|---|---|---|---|
| Click ID (SID) | __SID__ | click_id | click |
| Campaign ID | __CAMPAIGN_ID__ | campaign_id | campaign |
| Campaign Name | __CAMPAIGN_NAME__ | campaign_name | campaign |
| Account ID | __ACCOUNT_ID__ | publisher_id | publisher |
| Ad Group ID | __AD_GROUP_ID__ | adset_id | adset |
| Ad ID | __AD_ID__ | ad_id | ad |
- BIGO Ads macro
__SID__- Maps to
- click_id
- Identifies
- click
- BIGO Ads macro
__CAMPAIGN_ID__- Maps to
- campaign_id
- Identifies
- campaign
- BIGO Ads macro
__CAMPAIGN_NAME__- Maps to
- campaign_name
- Identifies
- campaign
- BIGO Ads macro
__ACCOUNT_ID__- Maps to
- publisher_id
- Identifies
- publisher
- BIGO Ads macro
__AD_GROUP_ID__- Maps to
- adset_id
- Identifies
- adset
- BIGO Ads macro
__AD_ID__- Maps to
- ad_id
- Identifies
- ad
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 placements on BIGO Ads.
Scoring and attribution are the means — the point is cutting the BIGO Ads 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 BIGO Ads 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 BIGO Ads placement and zone via the network’s own tokens — so the bad traffic has an address, not just a headline percentage.
- Cut
Take the placements 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 BIGO Ads and put your next dollar behind the traffic that converts.
BIGO Ads traffic quality, answered.
Does ValidVisit integrate directly with BIGO Ads’ placement exclusion, or is the process manual?
The process is manual. ValidVisit scores every visit and surfaces the {placement_id} values with weak quality scores in its reports and dashboard. You export those placement IDs and apply them in BIGO Ads’ placement blocklist inside your campaign settings. There is no automated push from ValidVisit into the BIGO Ads platform — the workflow is: score in ValidVisit, identify the problem {placement_id}, exclude it in BIGO Ads. This keeps you in control of the decision and avoids cutting placements that may have isolated invalid-traffic spikes rather than structural problems.
How does scoring in-app placements differ from scoring search or social traffic?
The delivery mechanic changes which signals carry the most weight. In search or social, invalid clicks often come from dedicated click tools that betray themselves quickly. On BIGO Ads’ in-app inventory, the dominant patterns are rewarded taps that earn in-app currency and automated sessions run from emulated devices — so the tap may originate from something that looks device-like, while the connection it arrived over and how the session behaves after the app hands off tend to be the more telling clues. Because ValidVisit weighs all 100+ data points together into one quality score, the math leans on whichever combination is most out of place for a given visit, so in-app traffic gets judged on the evidence that actually fits it.
Which BIGO Ads tokens give the most useful segmentation in ValidVisit?
The {placement_id} is the highest-leverage token because it maps directly to the app slot generating the traffic, and it is the same dimension you exclude on inside BIGO Ads — so you can cross-reference ValidVisit quality scores against your placement-level delivery in one view. The {click_id} provides session-level deduplication and ties each scored visit back to a specific BIGO Ads click record. Adding {app_id} and {campaign_id} lets you confirm whether a low-quality pattern is confined to one placement or spread across a whole partner app, which changes whether you exclude a single slot or the app entirely.
Detect fraud on other social networks
LinkedIn Ads
Meta Ads
Pinterest Ads
Quora AdsFind the bots in your BIGO Ads spend.
See which publishers and placements send real buyers vs bots — every visit scored 0–100, worst first.
Free trial at launch · just your email
One script · no cookies · no fingerprinting · raw IP never stored