Banner / DisplayWhich AppLovin placements send real 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.
See which AppLovin publishers, placements and sub-sources send real clicks versus bots, attributed via AppLovin’s own tracking tokens and scored 0–100 per source.
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, your spend is a single number. Scored per sub-source it runs from 89 down to 14 — the worst is nearly all bots. That’s the leak a blended average hides.
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.
How does ValidVisit detect invalid AppLovin traffic?+
Every AppLovin click is weighed against 100+ independent data points spanning the network it came from, the device behind it and how the visitor behaves — combined into a single 0–100 quality score so real humans pass and bots stand out.
Can I see which AppLovin publishers send bots?+
Yes. Using AppLovin's own tracking tokens, ValidVisit attributes each scored click to the publisher, placement and sub-source, so invalid traffic is pinned to the exact sub-source — which you can then exclude manually in AppLovin.
Does it require a funnel hop on my AppLovin links?+
No. Detection runs from one lightweight script — no extra hop, no link rewriting, no change to your AppLovin destination URLs.
Detect fraud on other banner / display networks
Find 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