Seatext library / BotRefund evidence
What Mistakes Do Advertisers Make When Trying to Stop Bot Clicks?
Advertisers often rely solely on platform filters that catch less than half of invalid traffic, over-block IP addresses and hit legitimate users on VPNs or corporate proxies, assume logged-in social platforms are immune to...
✓ Built for advertisers who need clear, refund-ready traffic evidence.
Most advertisers waste money twice: first on the bot clicks themselves, then on prevention methods that block real customers or miss sophisticated fraud. Google's own automated filters catch less than 50% of invalid traffic, leaving the rest classified as sophisticated invalid traffic that requires manual evidence submission. Meanwhile, 11% to 14% of clicks across all Google Ads campaigns are invalid on average, and high-CPC verticals like legal, insurance, and B2B SaaS see even higher rates. The mistakes below are the ones that show up repeatedly in audits and refund disputes.
Relying only on platform automated filters
Google Ads and Meta both run automated invalid-click filters. They are necessary but not sufficient. According to aggregated audit data, Google's filters catch less than 50% of invalid traffic. The remainder is sophisticated invalid traffic (SIVT) — bots that mimic human behavior well enough to pass basic checks. Meta's systems similarly miss traffic that originates from its Audience Network or residential proxy networks. Advertisers who assume the platform "handles it" typically lose 20% to 50% of budget to non-productive activity without realizing it.
Platform filters operate mostly on server-side signals: IP reputation, click timing, and known bot signatures. They do not see what happens in the browser after the click. A bot that loads a page, waits a random interval, scrolls a little, and leaves looks like a low-intent human to the platform. Only client-side behavioral analysis — mouse tremor, pointer path, click speed, session depth — can separate those sessions reliably.
Assuming logged-in platforms are bot-proof
Many advertisers believe Facebook and Instagram ads are safe because users must log in. That assumption is wrong. Bot traffic reaches Meta campaigns through three main channels: the Audience Network (which opts advertisers in by default and serves ads on thousands of third-party mobile apps and sites), profile scrapers and directory bots that crawl public posts and follow outbound links, and click farms that use real smartphones with logged-in accounts. Clicks from the Audience Network historically show high click-through rates and near-instant bounce rates. If you have not explicitly opted out of Audience Network placements, you are likely paying for that traffic.
Over-blocking IP addresses without behavioral context
Adding suspicious IPs to an exclusion list feels productive. It also catches legitimate users. Corporate proxies, university networks, VPNs, and shared residential IPs often route dozens or hundreds of real people through a single address. Blocking the IP because one session looked robotic penalizes every other user on that network. Click farms and residential proxy botnets deliberately route traffic through normal consumer IPs to hide inside legitimate regional traffic. An IP-only approach either misses the fraud or blocks the wrong people. The fix is to layer behavioral verification on top of IP signals: flag the IP for review, but block only when client-side evidence (missing mouse tremor, superhuman input speed, grid-aligned movement) confirms automation.
Ignoring Audience Network and mobile app placements
On Meta, the Audience Network is opted in by default. On Google, Display Network and mobile app placements can deliver similar low-quality traffic. Publishers on these networks sometimes run automated scripts or click farms to inflate their own revenue. The traffic looks like it comes from real devices — because it often does — but the intent is artificial. Advertisers who do not segment performance by placement, or who do not exclude mobile app categories known for fraud, pay for clicks that never convert. A structured audit that compares ad-platform data, website sessions, and CRM outcomes by placement is the only way to see the pattern.
Using only server-side detection
Server-side logs show IP, user agent, referrer, and request timing. They cannot see mouse movement, scroll depth, form interaction timing, or whether a click happened without the natural sequence of human intent. Advanced botnets rotate residential IPs, spoof user agents, and simulate realistic navigation paths at the HTTP level. Client-side detection — running in the browser — captures the behavioral micro-signals that server logs miss: absence of humanlike mouse tremor, robotic linear mouse movements, grid-aligned movement patterns, superhuman input speed under 1 millisecond, honeypot trap interactions, and unnatural session durations. Without client-side data, you are blind to the most sophisticated fraud.
Treating every low-quality lead as fraud
Not every bad lead is a bot. A weak campaign can attract real people who are not ready to buy. Treating every unresponsive contact as fraud leads to over-exclusion: you block audiences that would convert with better creative, offer, or follow-up. The source pack emphasizes starting with a structured audit that compares three layers — ad-platform data, website sessions, and CRM outcomes — before changing targeting or filing refund requests. Signals worth investigating include contactability (disconnected numbers, invalid email domains), timing (bursts of leads, immediate form submits, unusual hours), session behavior (no scrolling, no field corrections, uniform click paths), campaign patterns (sharp lead-quality differences by placement, creative, audience expansion, device), and CRM outcome (high reported leads but no calls connected, demos booked, or revenue).
Failing to capture evidence for refund disputes
Google and Meta both offer refund processes for invalid clicks, but they require evidence. Google's manual review process accepts GCLID-level data with behavioral proof. Meta's billing dispute system requires FBCLIDs and session logs. Advertisers who do not auto-capture click IDs (GCLIDs for Google, FBCLIDs for Meta) tied to behavioral verification — ghost click detection, pointer behavior, motion behavior, speed behavior, path behavior, engagement behavior, session behavior — cannot build the audit-ready reports that platforms accept. BotRefund's data shows an 83% refund success rate for high-volume advertisers who submit this class of evidence. Without it, refund requests are denied or ignored.
Key facts
| Metric | Value | Source |
|---|---|---|
| Global digital ad fraud projected cost (2026) | Over $100 billion | S1 |
| Average invalid click rate across Google Ads campaigns | 11% to 14% | S1 |
| Google automated filters catch rate | Less than 50% of invalid traffic | S1 |
| Invalid traffic share of programmatic ad spend (WFA) | 10% to 30% | S1 |
| Non-human share of total internet traffic (Imperva) | 43% | S6 |
| Invalid click rate range for Google Search campaigns | 4% (well-protected) to over 35% (high-CPC keywords) | S6 |
| Monthly loss at $50k spend (10-30% invalid) | $5,000 to $15,000 | S6 |
| Refund success rate with behavioral evidence (high-volume) | 83% | S2 |
| Refund lookback window for Google Ads | Dating back to 2017 | S2 |
Limitations and when this advice does not apply
Small accounts spending under $3,000 per month may not see enough invalid traffic to justify dedicated detection tooling; platform filters and occasional manual IP reviews may suffice. Advertisers in low-CPC, low-competition verticals often experience invalid click rates near the 4% floor. The behavioral signals described here require JavaScript execution on the landing page; they do not work for AMP pages, email clicks, or app-install campaigns that never hit a web page. Finally, refund policies and evidence requirements change — Google and Meta update their dispute processes periodically. Always check the current platform documentation before filing.
FAQ
How do I know if my current IP exclusions are blocking real customers?
Cross-reference excluded IPs with your CRM or analytics. If you see excluded IPs that previously generated conversions, or if conversion volume drops after a bulk exclusion, you are over-blocking. Use behavioral verification to confirm automation before excluding.
Does opting out of Audience Network reduce reach too much?
It reduces total impressions, but the remaining impressions are higher quality. Most advertisers see cost-per-acquisition improve because the budget shifts to placements where real humans engage. Test with a campaign-level opt-out for 14 days and compare lead quality.
What is the difference between invalid clicks and click fraud?
Invalid clicks is Google's umbrella term for any click that isn't genuine user interest — accidental clicks, duplicate clicks, and automated traffic. Click fraud is a subset: deliberate, malicious automation intended to drain budgets or inflate publisher revenue. Both waste money, but only fraud implies intent.
Can I get refunds for past months without a detection tool installed?
Only if you have raw server logs with GCLIDs/FBCLIDs and can reconstruct behavioral evidence retroactively. Most advertisers cannot. Installing client-side detection now protects future spend and enables refund claims for the lookback window (Google allows disputes back to 2017).
How often should I audit for bot traffic?
Monthly for spend over $10,000. Quarterly for lower spend. High-CPC verticals should monitor weekly during peak seasons. Automate the audit: pull placement reports, segment by device and network, flag sessions with zero scroll, sub-second dwell, or missing mouse events.
What behavioral signals are strongest for proving bot traffic to Google or Meta?
Ghost clicks (clicks without human intent sequence), superhuman input speed (<1ms), absence of mouse tremor, grid-aligned pointer paths, and honeypot trap interactions. These are difficult for bots to fake and are accepted as evidence in platform dispute reviews.
Do I need a separate tool if I already use Google Analytics 4?GA4 shows traffic patterns but does not capture the micro-behavioral signals (mouse tremor, click speed, honeypot interactions) that platforms require for refund evidence. It also cannot auto-capture GCLIDs/FBCLIDs tied to behavioral proof. A dedicated detection layer complements GA4; it does not replace it.
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