Your IVT rate, calculated.

An invalid traffic (IVT) rate is the proportion of your measured traffic events — clicks, impressions, or visits — that a detection system classifies as non-human or low-quality. The definition sounds simple, but in practice the number you get depends entirely on how you measure it and what goes into the assessment.

Two measurement systems looking at the same campaign will routinely disagree because they weigh inputs differently, look at different aspects of each visit, and apply different classification thresholds. This is the first reason why an externally published benchmark is a poor yardstick: you cannot compare your rate to someone else's without knowing that their methodology matches yours exactly.

A working definition for your own program:

• Scope: which traffic events are you judging? All clicks to a landing page? Only clicks that actually reach the page?
• Verdict rule: what has to be true of a visit before you label it invalid? A rule that needs a hard fact versus one that acts on suspicion will produce very different headline rates.
• Segmentation unit: are you computing a single campaign rate, or separate rates per publisher, placement, and zone? The latter is the only operationally useful level.

ValidVisit gives every visit a single verdict, Valid or Invalid, along with the reason behind it, so you can count by reason and recompute your rate without re-running anything.

IVT rate = visits judged Invalid ÷ all judged visits × 100, computed separately for each segment you buy: publisher, placement or zone.

An illustrative week for one push campaign (the numbers are made up):

• Zone 1041: 4,200 visits, 210 Invalid: 5%
• Zone 2290: 3,100 visits, 1,240 Invalid: 40%
• Zone 3317: 2,700 visits, 135 Invalid: 5%
• Whole campaign: 10,000 visits, 1,585 Invalid: 15.9%

The campaign rate says the campaign is mediocre. The zone rates say two zones are fine and one is the problem: zone 2290 sends 31% of the visits and 78% of the Invalid ones. Cutting that zone, not the campaign, is the decision.

Only compare rates measured with the same rule, over the same period, and with enough visits per segment for the share to settle.

Most ad platforms apply their own invalid-click filtering before they bill you, and many report a filtered count in their dashboards. This is useful, but it creates a fundamental measurement problem: you are asking the platform to audit itself.

Platform filtering is real and often effective, but it operates on what the platform can see and applies thresholds chosen for billing, not for your optimization decisions. It does not tell you:

• Which specific sub-sources are driving the invalid share — the publisher, zone, or placement that is degrading your results.
• Why traffic was classified as invalid — without an explanation per visit, you cannot tell automated server-farm traffic apart from a click farm or visits routed through anonymizing proxies.
• What proportion of borderline traffic slipped through — platforms typically apply conservative thresholds to avoid over-filtering legitimate clicks.

Independent, post-arrival verdicts give you a parallel measurement that is under your control. When your independent IVT rate diverges sharply from the platform's reported filtered rate, that gap is itself a signal worth investigating by sub-source.

ValidVisit works after the click arrives: it checks each visit against more than 100 data points — covering the network the click came from, the device on the other end and the page itself — and returns one verdict, Valid or Invalid, with its evidence. One thing a real visitor cannot produce makes a visit Invalid. The verdict reflects what genuinely landed on your page, not only what the ad network observed at the click event. It never sits in the click path, and the verdict never blocks a visitor or excludes anything in your ad accounts; it measures and reports.

Figures published as 'the industry IVT rate' aggregate across wildly different channel types, geos, targeting approaches, and measurement methodologies. Using such a figure to evaluate your own campaigns is like comparing your company's support ticket volume to an industry average without knowing the mix behind it.

Several structural factors make a universal benchmark functionally meaningless:

• Methodology variance: published figures come from different vendors who look at different things and set different thresholds. A vendor that only checks IP reputation will report a very different rate from one that also weighs how the visitor behaves once they arrive.
• Sampling bias: benchmark studies typically draw from traffic that flows through a specific network or panel. If your traffic mix skews toward channels or geos under-represented in that sample, the number does not apply to you.
• Time decay: IVT patterns shift as bad actors adapt. A benchmark published even months ago may no longer reflect current conditions on any given channel.

The most useful benchmark you can have is your own historical baseline, segmented at the publisher and placement level and trended over time. A rate that rises sharply on a specific zone while remaining stable elsewhere is a clear operational signal; a comparison to an external industry figure is not.

IVT rates vary a lot by traffic type. Understanding the qualitative drivers helps you form realistic expectations for your own baseline before you have measured data.

Channel type is the strongest structural driver. Branded and navigational search traffic tends to attract lower IVT because it is harder and less economically attractive to fake intent signals that survive post-click scrutiny. Native and push traffic runs meaningfully higher because the supply chain is deeper — more intermediaries, more sub-publishers — and the click value is lower, making volume-based invalid traffic more viable. Pop and interstitial formats are historically associated with elevated IVT because the click mechanics require less user intent.

Sub-source mix within a channel matters more than the channel average. A native network with strong publisher quality controls may deliver more valid traffic than a poorly optimized search campaign on broad match with low bids. The channel label is a starting prior, not a prediction.

Geography and carriers matter. Some regions and mobile carriers route far more traffic through VPNs and proxies, often for ordinary reasons such as corporate networks or national firewalls. ValidVisit counts a paid visit through a VPN or proxy as invalid, so expect a higher rate in those markets and compare a source with its own baseline rather than with another country.

Targeting specificity influences economic incentive. Narrow, high-CPM audiences are more attractive targets for sophisticated invalid traffic because the reward per bad click is higher. Broad, low-CPM placements may carry more unsophisticated bot traffic optimized for volume.

Segmenting your measured IVT rate along these dimensions — channel, sub-source, geo, CPM tier — lets you identify which combinations underperform relative to your own baseline, which is the only comparison that drives action.

Establishing a meaningful IVT baseline is a process, not a one-time measurement.

1. Define your measurement unit. Choose the traffic event you will judge consistently — typically the post-click page visit — and keep your tagging stable across all placements. Inconsistent coverage produces a rate that reflects tagging gaps as much as traffic quality.

2. Run a clean measurement period. Before drawing conclusions, collect judged data across at least several weeks to smooth out day-of-week patterns and campaign warm-up effects. Your baseline should cover enough volume per publisher and placement to be stable.

3. Segment before you aggregate. Compute your IVT rate separately for each publisher, placement, and zone. An aggregate campaign-level rate can mask a single poor placement inflating your overall figure while most of your supply is valid.

4. Document your definition. Record which reasons count toward 'invalid' and what you leave out. If you later narrow the definition, your historical rate will appear to drop — not because quality improved, but because your definition changed. Consistent methodology is the only way to trend meaningfully.

5. Set deviation alerts, not target rates. Rather than trying to hit an external benchmark, watch for your per-publisher rate to move materially against its own rolling average. A publisher whose rate doubles in a week warrants investigation regardless of where it started.

ValidVisit attributes every judged visit to the publisher, placement, and zone via the network's own tracking tokens, so you can filter your judged dataset to any sub-source and compute its rate independently. The per-visit evidence lets you go further and see whether a rate spike is driven by a new wave of automated traffic, a shift in the networks and devices behind the clicks, or heavier use of anonymizing proxies — each with different implications for the conversation with your network. When you confirm a sub-source is the problem, you exclude it yourself in that network's own dashboard; ValidVisit never acts in your ad accounts.

ValidVisit's output is designed to support exactly this self-benchmarking workflow. Every judged visit produces a verdict (Valid or Invalid), the plain-language reason behind it backed by evidence in your dashboard, and sub-source attribution carried through the network's publisher, placement, and zone tokens so every judged row is joinable back to the supply segment that delivered it.

With this structure, computing your IVT rate for any segment is a simple filter and aggregation: take all visits in the window, filter to your sub-source of interest, count those judged invalid, divide by total visits. No black box, no proprietary index you have to take on faith.

The per-visit evidence adds operational intelligence that a raw rate does not. If a publisher's rate rises but the dominant reason shifts from one kind of problem to another — say from server-farm traffic to automated browsers — that is a qualitatively different situation, and it points you toward a different conversation with the supplier. Each Invalid verdict carries the fact that decided it, so the explanation reflects what actually drove the call.