Seatext library / BotRefund evidence
What Does 99% Accuracy Mean for BotRefund? A Practical Breakdown
BotRefund's 99% accuracy refers to its confidence level in identifying non-human traffic on your website, achieved by cross-checking 106 independent behavioral, browser, network, and device signals through an AI prediction model rather than relying...
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BotRefund's 99% accuracy means the system identifies a visit as bot or human with 99% confidence by evaluating the complete pattern across 106 independent checks covering browser, network, device, and behavior evidence. No single signal — such as impossible tab speed, superhuman input speed, or absence of mouse tremor — acts as a verdict on its own. Instead, each check contributes one objective fact that the prediction AI weighs together with all other signals to reach a corroborated conclusion.
This approach matters because ad platforms bill for every click at the moment it happens, leaving advertisers to prove after the fact which clicks were non-human. Industry audits consistently place automated traffic between 9% and 20% of paid clicks. BotRefund's 99% confidence level supports the evidence packages that achieve an 83% approval rate on refund claims filed with Google and Meta, recovering spend dating back to 2017.
How the 99% confidence is built
BotRefund runs 106 independent checks during each visit. These checks fall into four categories: browser signals, network signals, device signals, and behavioral signals. Each check produces one piece of evidence — for example, whether the tab speed is physically impossible for a human, whether mouse movements lack natural tremor, or whether input speed exceeds human limits.
The system does not treat any single anomaly as a bot verdict. Privacy tools, corporate networks, travel, and unusual devices can create unexpected behavior for genuine visitors. BotRefund keeps each signal as evidence and cross-checks it against the other 105 signals. The AI prediction model then weighs the complete pattern instead of trusting a raw rule.
This corroboration method is what drives the 99% confidence figure. A single browser tell can be spoofed or occur naturally. A consistent pattern across browser, network, device, and behavior dimensions is far harder for automated systems to fake convincingly.
What the 99% specifically measures
The 99% confidence applies to the identification of non-human traffic on your site. It is a detection accuracy metric, not a refund guarantee. The platform uses this high-confidence detection to capture Google Click IDs (GCLIDs) and Facebook Click IDs (FBCLIDs) linked to behavioral proof of invalidity, then generates audit-ready dispute reports for submission to the ad platforms' own invalid-traffic channels.
Separately, BotRefund reports an 83% approval rate across client refund claims submitted to Google and Meta. The gap between 99% detection confidence and 83% claim approval reflects platform discretion, evidence thresholds, and the fact that ad platforms have no incentive to flag their own revenue. Refunds happen almost exclusively when an advertiser contests specific charges with specific evidence.
Why detection accuracy changes the refund outcome
Google and Meta both operate invalid activity credit systems, but their automated detection catches only a fraction of invalid traffic. Google's systems analyze server-level patterns like rapid clicking, duplicate click signatures, known bad IP ranges, and abnormal click patterns. Meta faces additional challenges from click farms using real smartphones and residential proxy botnets that hide within legitimate consumer traffic.
When an advertiser submits a claim with client-side behavioral evidence — showing, for example, that a session had superhuman input speed (<1ms), grid-aligned movement patterns, and impossible tab speed all in the same visit — the platform must evaluate that specific evidence against its own records. The 99% confidence means the evidence package is built on a detection method that rarely misclassifies human visitors as bots, reducing the risk of rejected claims due to false positives.
Detection accuracy vs. refund approval rate
It is important to distinguish two different metrics:
- 99% detection confidence: The probability that a visit flagged as non-human is actually non-human, based on corroborated multi-signal analysis.
- 83% refund approval rate: The percentage of BotRefund-filed claims that Google and Meta approve, resulting in credited spend returned to the advertiser.
The approval rate is lower because platforms apply their own review standards and retain discretion over what counts as invalid activity under their policies. BotRefund's role is to supply the evidence that meets those standards; the decision rests with the platform.
What 99% accuracy does not mean
- It does not mean 99% of bot clicks are caught. Coverage depends on traffic volume, bot sophistication, and whether the BotRefund script is installed on all landing pages.
- It does not guarantee a 99% refund recovery. Recovery depends on platform approval, lookback windows, and the specific campaigns affected.
- It does not replace the need for conversion pixel protection. Without real-time filtering, invalid sessions can still poison Smart Bidding and Advantage+ algorithms before a refund is filed.
- It does not apply to traffic that never reaches your site (e.g., impression fraud on third-party publisher placements where the click never loads your page).
Key facts
| Metric | Value | Source context |
|---|---|---|
| Detection confidence | 99% | AI prediction model weighing 106 independent checks across browser, network, device, and behavior signals |
| Independent checks per visit | 106 | Includes impossible tab speed, superhuman input speed, absence of mouse tremor, grid-aligned movement, VPN detection, honeypot trap interactions, and more |
| Refund claim approval rate | 83% | Across client claims submitted to Google and Meta invalid-traffic channels |
| Estimated bot share of paid clicks | 9%–20% | Industry audits cited by BotRefund |
| Lookback window for Google Ads refunds | Dating back to 2017 | BotRefund recovers spend from historical campaigns |
| Installation | One script tag, ~1 minute | No ad-account access required |
| Pricing model | Performance-based for enterprise | Fees come out of recovered spend; no upfront cost on enterprise plans |
How the detection feeds the refund workflow
- Script installation: Add the BotRefund tag to your site. It begins collecting behavioral, browser, network, and device signals on every visit.
- Real-time classification: Each visit is scored by the AI model. Visits flagged as non-human have their GCLID or FBCLID captured with the supporting evidence.
- Pixel protection: Conversion pixels are suppressed for flagged sessions so Smart Bidding and Advantage+ do not optimize toward bot traffic.
- Evidence compilation: BotRefund builds compliance-grade dispute logs linking each flagged click ID to the specific behavioral anomalies detected.
- Claim submission: Reports are filed through Google and Meta's official invalid-activity channels.
- Recovery: Approved credits appear in the ad account. BotRefund's enterprise tier takes its fee from the recovered amount.
Common misconceptions
- "99% accuracy means almost no bots get through." Accuracy measures classification correctness, not coverage. Sophisticated bots that mimic human behavior across all 106 dimensions could still evade detection, though the corroboration approach makes this extremely difficult.
- "The 83% approval rate is low." Most advertisers never file claims because assembling session-level evidence manually is impractical. An 83% approval rate on filed claims represents a high success rate for a process that otherwise rarely happens.
- "This replaces Google's or Meta's own filters." BotRefund works alongside platform filters. It catches traffic the platforms miss and provides the evidence needed to contest charges the platforms did not automatically credit.
When to consider BotRefund
You should evaluate BotRefund if:
- Your monthly Google + Meta spend exceeds $10,000 and you have never filed an invalid-activity claim.
- You see high click volume but low conversion quality, suggesting pixel poisoning.
- You run Performance Max, Advantage+ Shopping, or other algorithmic campaigns that optimize toward conversion signals.
- You want historical recovery for spend going back several years.
- You need audit-ready evidence for finance or compliance teams.
The free bot audit (available on the BotRefund site) quantifies the bot share in your current traffic and estimates recoverable spend before any commitment.
FAQ
Does 99% accuracy mean 1% of human visitors are wrongly flagged as bots?
The 99% confidence refers to the overall classification reliability when all 106 signals are weighed together. False positives are minimized by the corroboration requirement — a single anomalous signal is never enough to flag a visit. However, no detection system eliminates false positives entirely. BotRefund's evidence packages are designed so that any disputed classification can be reviewed against the raw signal data.
How does BotRefund's 99% confidence compare to Google's or Meta's own detection?
Google and Meta do not publish comparable confidence figures for their automated invalid-activity filters. Their systems operate at the server level (IP patterns, click timing, known bad networks) while BotRefund operates at the client level (behavioral biometrics, browser fingerprinting, device signals). The two approaches catch different fraud types. BotRefund's evidence is used to supplement — not replace — platform credits.
What happens if a refund claim is denied?
Denied claims can sometimes be appealed with additional evidence. BotRefund retains the session-level data and can refine the dispute package. The 83% approval rate is an aggregate across all client claims; individual account results vary by campaign type, traffic sources, and platform reviewer discretion.
Is the 99% figure audited by a third party?
BotRefund does not publicly cite a third-party audit of the 99% confidence figure. The figure is presented as a property of its AI prediction model. Advertisers can verify detection quality by running the free bot audit, which shows flagged sessions and the signals that triggered each classification.
Does the 99% accuracy apply to all bot types equally?
The 106 checks cover a wide range of automation signatures: browser automation frameworks, headless browsers, residential proxy botnets, click farms, scraper scripts, and more. Sophisticated bots that invest in mimicking human behavior across all dimensions (timing, movement, hesitation, device characteristics) are harder to detect, but the multi-signal approach raises the cost and complexity of such evasion significantly.
How long does it take to see refund results after installing BotRefund?
Detection begins immediately after script installation. Review timelines vary by platform and depend on the specific claim and evidence submitted. Historical claims for spend dating back to 2017 can be filed once evidence is compiled.
What is required to start the free bot audit?
The audit requires installing the BotRefund script on your site. No credit card or ad-account access is needed. The audit runs live on a scheduled call where BotRefund reviews your site's actual traffic patterns and provides a recoverable-spend estimate based on your current ad spend level.
Further reading and comparison sources
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Further reading and comparison sources
These external sources provide additional context for evaluating the topic. Their inclusion is not an endorsement.
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