Banner / DisplayWhich AppLovin placements send bots and invalid traffic?
Not all AppLovin 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.
There is nothing the buyer can literally do with a bad-source list in the dashboard/API; the only recourse is emailing the list of publisher package/bundle IDs to their AppLovin account manager or filing a support request and asking for a manual block.
ValidVisit reports the device, OS, browser — down to the version — plus the language and ISP behind every flagged visit, and AppLovin supports OS and language targeting. The segments we flag are segments you can exclude.
How invalid traffic slips in through AppLovin placements.
AppLovin runs its performance ad engine, AppDiscovery, across a vast in-app exchange: when you buy installs and visits through it, your creative surfaces inside thousands of other publisher apps — rewarded video, interstitials, banners, offer walls. The supply is mobile-native. Each impression fires inside a real app’s ad SDK, and the tap threads through an in-app browser to your destination. AppLovin passes an {APP_ID} — the specific publisher app that showed your ad — and a {PLACEMENT_ID} — the exact slot within that app — on every destination URL, alongside {EVENT_ID} and {CAMPAIGN_ID}. The vulnerability is that in-app supply quality varies enormously: legitimate apps sit beside device farms, emulator fleets, and integrations that manufacture sessions no person ever drove. ValidVisit takes a different approach to telling those apart: every arriving visit is measured against 100+ independent data points that span the network the visit came over, the device behind it, and how the visitor actually behaves once the page loads, and all of that collapses into one 0–100 quality score for that single visit. Genuine installs and human visitors clear the bar; emulator, farm, and spoofed sessions separate against it. Because each visit’s score attaches to its own {APP_ID} and {PLACEMENT_ID}, you can see which publisher app — and which placement within it — carries a structural IVT problem versus one that is merely low-converting.
In-app invalid traffic on AppLovin is shaped by the mobile supply chain, not by the ad format on screen. The most costly pattern is emulator and device-farm sourcing: a publisher app — or an operator standing behind several {APP_ID} values — serves your ad inside virtualized or racked devices rather than phones in real hands, then generates taps and post-click sessions at volume. Because the tap still threads through a genuine in-app browser to your page, click-count filters and simple IP screens rarely separate it. ValidVisit reads the network origin of each arriving visit as part of its score, which is where farmed supply tends to give itself away on AppLovin: the connection and device behind these sessions don’t line up with a person on the phone and OS they claim to be, and that mismatch marks the score long before any single filter would flag it.
A second pattern specific to in-app supply is manufactured sessions that never had a human behind them — SDK integrations or click-flooding setups that report an install or an open, fire a tap toward your destination, and produce on-page activity that behaves nothing like a real user once it lands. The longer in-app path — through the app, its ad SDK, and an in-app browser before your page loads — gives those inconsistencies more places to surface, so a spoofed {PLACEMENT_ID} tends to score well below a campaign’s genuine placements even when its raw install and click counts look healthy.
A third, lower-volume pattern is incentivized traffic misread as engaged: users nudged to tap your ad inside a rewarded-video or offer-wall placement to earn in-app currency, who arrive with no intent toward your offer. ValidVisit’s scoring tells this apart from automation — the session reads as human across the network and device signals, but its depth and the texture of its engagement read as reflexive rather than genuine interest. That distinction matters because the remedy differs: an emulator-heavy {APP_ID} warrants exclusion, while a low-intent incentivized {PLACEMENT_ID} may warrant a bid reduction or a move off rewarded inventory rather than a full block.
What to watch on AppLovin
{APP_ID} IVT concentrationSegment your ValidVisit report by AppLovin’s {APP_ID} token — the publisher app that served your ad. Apps driving a disproportionate share of your visit volume alongside low quality scores are the primary signal of farmed or emulator supply. An {APP_ID} whose score profile sits well below your campaign baseline warrants manual exclusion in AppLovin’s AppDiscovery blocklist before that app’s volume distorts your campaign optimization.
{PLACEMENT_ID} quality within a single appOne app can carry both low-IVT and IVT-heavy inventory depending on the slot. Compare scores across {PLACEMENT_ID} values inside the same {APP_ID}: if a rewarded-video or interstitial placement draws a low-quality visit share while a banner placement in the same app looks valid, the problem is the placement, not the whole publisher. That points to a placement-level exclusion rather than blocking an app that also sources genuine traffic.
{APP_ID}For each app, look at whether a visit’s poor score is driven mostly by where it arrived from on the network side or by how it behaved once the page loaded. An {APP_ID} whose low scores trace almost entirely to its traffic source points to a structural sourcing problem — emulator or farm supply. One where the weakness shows up mainly in on-page behavior points to manufactured in-app sessions instead. Both are read from the same per-visit score before you decide whether to exclude.
Farmed and automated in-app supply often runs on a schedule rather than following a real audience’s daily rhythm. If a high-volume {APP_ID} shows quality scores well below your campaign baseline in tight bursts or overnight windows but looks valid otherwise, that time-pattern is itself a diagnostic signal worth folding into your manual review before you exclude the app.
Pinpoint the bot publishers & placements in AppLovin.
AppLovin 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 AppLovin tokens, not by creative (which says nothing about whether a click was human).
Bought as one AppLovin line, a buy reads as a single number. Scored per sub-source, a spread like this illustration runs from 89 down to 14 — the worst is nearly all bots. That’s the leak a blended average hides.
Illustrative: AppLovin traffic scored 0–100 per sub-source, worst first — down to the placement you buy.
Bot / invalid-traffic score broken down by:
- App ID
{APP_ID}
Bot / invalid-traffic score broken down by:
- Placement ID
{PLACEMENT_ID}
Per-click id: AppLovin 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 AppLovin sub-sources scored this way.
How ValidVisit attributes AppLovin traffic
Each AppLovin 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=applovin&utm_medium=banner&vv_click_id={EVENT_ID}&vv_campaign_id={CAMPAIGN_ID}&vv_campaign_name={CAMPAIGN_NAME}&vv_publisher_id={APP_ID}&vv_placement_id={PLACEMENT_ID}&vv_creative_id={CREATIVE_SET_ID}| Token | AppLovin macro | Maps to | Identifies |
|---|---|---|---|
| Event ID (ad request ID) | {EVENT_ID} | click_id | click |
| Campaign ID | {CAMPAIGN_ID} | campaign_id | campaign |
| Campaign Name | {CAMPAIGN_NAME} | campaign_name | campaign |
| App ID | {APP_ID} | publisher_id | publisher |
| Placement ID | {PLACEMENT_ID} | placement_id | placement |
| Creative Set ID | {CREATIVE_SET_ID} | creative_id | creative |
- AppLovin macro
{EVENT_ID}- Maps to
- click_id
- Identifies
- click
- AppLovin macro
{CAMPAIGN_ID}- Maps to
- campaign_id
- Identifies
- campaign
- AppLovin macro
{CAMPAIGN_NAME}- Maps to
- campaign_name
- Identifies
- campaign
- AppLovin macro
{APP_ID}- Maps to
- publisher_id
- Identifies
- publisher
- AppLovin macro
{PLACEMENT_ID}- Maps to
- placement_id
- Identifies
- placement
- AppLovin macro
{CREATIVE_SET_ID}- Maps to
- creative_id
- Identifies
- creative
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 the sources bleeding your budget on AppLovin.
Scoring and attribution are the means — the point is cutting the AppLovin 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 AppLovin 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 AppLovin publisher, placement and zone via the network’s own tokens — so the bad traffic has an address, not just a headline percentage.
- Cut
Take the worst 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 AppLovin and put your next dollar behind the traffic that converts.
AppLovin traffic quality, answered.
Does ValidVisit push exclusions into AppLovin automatically, or is the process manual?
The process is manual. ValidVisit scores every visit and surfaces the {APP_ID} and {PLACEMENT_ID} values with weak quality scores in its reports and dashboard. You export those publisher-app and placement IDs and add them to your AppDiscovery blocklist inside AppLovin’s campaign settings. There is no automated push from ValidVisit into AppLovin’s platform — the workflow is: score in ValidVisit, identify the problem app or placement, exclude it in AppLovin. That keeps the exclusion decision in your hands and avoids cutting an app that had an isolated IVT spike rather than a structural problem.
AppLovin’s AXON engine already optimizes toward my install and event data — won’t it route around low-quality apps on its own?
AXON optimizes toward the events you send back to AppLovin, so it can only route correctly if those events are valid. When installs or post-install actions manufactured by emulator or SDK-spoofed supply flow into your event feed, they register as positive signals in AppLovin’s loop, which can pull spend toward the very apps producing them rather than away from them. ValidVisit sits outside that loop: it gives you an independent per-visit quality score attached to each {APP_ID} and {PLACEMENT_ID}, so you can find which publisher apps are corrupting the optimization signal and exclude them before the engine treats manufactured activity as a positive learning.
Which AppLovin macros should I append to my destination URL, and what does each one tell me?
Five are worth including. {APP_ID} identifies the publisher app that served your ad and is the highest-leverage dimension for exclusion, because AppDiscovery’s blocklist works at the app level. {PLACEMENT_ID} pins the specific slot within that app, letting you separate a bad placement from a bad publisher. {EVENT_ID} is the per-visit identifier ValidVisit uses to tie its score back to AppLovin’s own reporting. {CAMPAIGN_ID} and {CREATIVE_SET_ID} let you confirm whether a low-quality pattern is app-wide or tied to how a particular creative is being served, which changes the exclusion logic. You append these as standard query parameters on your destination URL — no funnel hop and no SDK change is involved.
Does AppLovin’s in-app supply change how ValidVisit scores visits compared to search or social?
The delivery path shifts which signals carry the most weight. Search and social clicks usually come from a browser on a device in someone’s hand, so dedicated click tools tend to expose themselves quickly. On AppLovin’s in-app exchange, the dominant IVT patterns are emulator and device-farm supply and manufactured in-app sessions — so the device may be presented as a plausible consumer phone, while the connection it arrived over and how the session behaves after landing are the more telling clues. Because ValidVisit weighs all 100-plus data points together into one 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.
Detect fraud on other banner / display networks
AdNow
SourceKnowledge
AdsCompass
DAO.ADFind the bots in your AppLovin 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