widget in MGIDValidVisit scores each widget_id; stop the bad widgets in MGID's Widgets tab (or blacklist the whole site).
Native discovery networks like MGID distribute your ads across a sprawling ecosystem of independent publishers, each surfacing teasers inside content-recommendation widgets embedded on third-party sites. The power of that model is reach; the vulnerability is that publisher quality varies enormously, and the network's CPC mechanics create a structural incentive for certain publishers to maximize clicks rather than advertiser outcomes. ValidVisit attaches a lightweight scoring script to your MGID destination URL and judges every arriving click against more than 100 independent data points — covering the network the click came through, the device sitting behind it, and the way the visitor actually behaves — which collapse into one transparent 0-100 quality score per click, so real people sail through and automated traffic stands apart. Each score is tied to MGID's own widget_id and click_id tokens. No funnel hop is involved and no landing page request is altered; scoring happens post-arrival so your page load and Quality Score signals are untouched. The result is a per-widget, per-publisher breakdown you can use to decide which placements to exclude manually inside MGID's dashboard — grounded in your own independent data, not the network's aggregate reporting.
https://yoursite.com/landing?utm_source=mgid&utm_medium=native&vv_campaign_id={campaign_id}&vv_creative_id={teaser_id}&vv_publisher_id={widget_id}&vv_click_id={click_id}MGID's publisher ecosystem is wide and heterogeneous, which means invalid traffic rarely distributes evenly — it concentrates in a small number of publisher placements where the economics favor volume over quality. The dominant pattern in native widget inventory is publisher arbitrage: a site owner drives low-cost traffic from outside sources to inflate the pageviews on which widget impressions are counted, then collects CPCs when those visitors — or bots standing in for them — click teasers. Because MGID pays on the click, not the downstream conversion, the damage to an advertiser's budget can accumulate across dozens of widget_id values before post-click engagement metrics make the pattern visible.
A second pressure comes from automated click generation run out of rented server farms, which lands as low quality scores because the underlying network and device characteristics simply do not line up with a genuine human on a consumer connection. These sweeps tend to hit a single widget_id in bursts rather than distributing naturally across a campaign's placement mix. A third, less common pattern is competitive or incentivized clicking on specific placements where a publisher has an unusual stake in click volume; ValidVisit surfaces this as a concentration of clicks whose behavioral and technical signals are inconsistent with organic content discovery, pulling their quality scores well below the campaign baseline — though inferring intent from signal patterns alone is not definitive.
A well-distributed MGID campaign spreads clicks across many widget placements. When a small number of widget_id values account for a disproportionate share of total click volume — particularly with quality scores running below your campaign average — those publisher placements warrant manual review and potential exclusion in MGID's dashboard before the imbalance compounds.
Bot operators running click farms on rented infrastructure show up with network and device traits that don't match real consumer visitors. If a specific widget_id is generating clicks that consistently land in the low end of the quality scale, that is a strong signal the traffic is non-human — ValidVisit ties that score to the widget and click_id so you can take the finding directly to MGID's account team.
ValidVisit grades each arriving click on its own merits, independent of which placement sent it. When a particular widget_id shows a materially higher share of low-scoring clicks than the rest of the campaign, it suggests a portion of that traffic is automated rather than people genuinely engaging with the page — a pattern distinct from poor audience fit and more consistent with machine-driven clicking.
Some publishers with a high invalid-traffic profile enter a campaign rotation at low volume to stay under automated thresholds, then ramp spend quickly once a pattern is established. Watch for widget_id values that appear for the first time and immediately generate click velocity well above established placements, especially outside the target geography's peak hours.
Each MGID macro maps to a normalized parameter, so every scored click is pinned to the right campaign, creative and publisher.
| Token | MGID macro | Maps to | Identifies |
|---|---|---|---|
| Campaign ID | {campaign_id} | campaign_id | campaign |
| Teaser (Creative) | {teaser_id} | creative_id | creative |
| Widget (Publisher) | {widget_id} | publisher_id | publisher |
| Click ID | {click_id} | click_id | click |
{campaign_id}{teaser_id}{widget_id}{click_id}MGIDitself 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 MGID tokens, not by creative (which says nothing about whether a click was human).
Illustrative example — MGID traffic scored 0–100 per sub-source, worst first.
See your own MGID sub-sources scored this way.
Bot / invalid-traffic score broken down by:
{widget_id}Per-click id: MGID 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.
MGID supports dynamic value insertion on destination URLs, so you can append widget_id and click_id as query parameters that MGID populates automatically on every click. ValidVisit reads those values on arrival and ties each quality score to the originating widget and the unique click event. Adding campaign_id gives you cross-campaign segmentation at the widget level — useful if the same widget_id appears in multiple campaigns with different targeting. The teaser_id (creative) is available but bot-driven patterns live in the publisher layer, not in which creative was displayed, so it is lower priority for IVT analysis.
MGID's internal quality filtering operates on signals the network controls and is applied before clicks reach your URL. ValidVisit runs independently on your side of the click, after arrival, and scores each event against 100-plus data points the network does not have visibility into — spanning the path the click traveled, the device behind it, and how the visitor behaves on your page. That independence matters when you need auditable, per-click evidence to support a placement exclusion decision or a credit conversation: you are presenting your own data, not asking the network to review its own reporting.
ValidVisit identifies which widget_id values carry low quality scores and surfaces them in your dashboard alongside the click volume and signal breakdown. You then take that widget_id into MGID's publisher exclusion controls — available in the campaign settings — and add it to the placement blocklist. ValidVisit does not push exclusions to MGID automatically; the action lives in the network dashboard. That manual step keeps you in control of what gets blocked and gives you a clear audit trail if you need to dispute charges or request make-goods.
See which campaigns and publishers send real, converting traffic vs bots — every click scored 0–100.
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