Seatext library / BotRefund evidence
How to Use BotRefund to Retain Evidence for Ad Refund Claims
BotRefund retains evidence by installing its tracking script on your landing pages, which captures 110+ behavioral, browser, hardware, and network signals per session. The system preserves click IDs, timestamps, session recordings, and signal-by-signal reasoning...
✓ Built for advertisers who need clear, refund-ready traffic evidence.
BotRefund retains evidence by installing a lightweight script on your landing pages that records every visitor session after a paid click. The script collects over 110 independent signals — including click timing, mouse movement patterns, scroll behavior, browser fingerprint inconsistencies, and network context — and ties each session to its originating campaign, ad set, creative, and click identifier (GCLID or fbclid). This data is stored in a structured report that matches the evidence format Google and Meta require for invalid-activity credit requests.
To preserve evidence, install the script before you launch or continue campaigns, let it run without pausing traffic, and export the audit-ready report when you file a refund claim. The platform keeps session-level detail so you can show exactly which clicks were automated, not just aggregate estimates.
What BotRefund Evidence Looks Like
Each flagged session comes with a session recording, a list of triggered detection signals, and the attribution metadata that connects the visit to your ad spend. The report includes click IDs, campaign names, placement, device, timestamp, and a signal-by-signal explanation of why the visit was classified as automated. This granularity is what platform reviewers look for — they need to see the specific behavior, not a summary score.
BotRefund's detection combines behavioral, browser, hardware, and network signals. Examples include ghost clicks (clicks without human intent sequence), honeypot trap interactions, robotic linear mouse movements, absence of humanlike mouse tremor, superhuman input speed under 1 millisecond, grid-aligned movement patterns, absence of clicks or scrolling, and unnatural session durations. No single signal proves fraud; the system cross-checks all 110+ signals and weighs them through an AI model that reaches 99% confidence when the full pattern supports it.
Prerequisites Before You Start
- Active paid campaigns on Google Ads or Meta Ads — BotRefund tracks traffic that originates from paid clicks with click identifiers.
- Access to add JavaScript to your landing pages — The tracking script must load on every page a paid visitor might reach.
- Admin access to the ad accounts — You need campaign, ad set, and creative names to map evidence to spend.
- No immediate campaign pauses — Preserve attribution by keeping campaigns running while the audit collects a representative sample.
Step-by-Step Evidence Retention Process
- Create a BotRefund account and add your domain. The platform generates a unique tracking snippet.
- Install the snippet on all landing pages that receive paid traffic. Place it in the
<head>so it loads before user interaction. - Verify the script is firing using the BotRefund dashboard's live view. Confirm sessions appear with click IDs (GCLID for Google, fbclid for Meta).
- Let traffic run for a meaningful period — typically 7–14 days or until you have several hundred paid sessions. Do not pause campaigns during this window.
- Review the audit dashboard. Filter by campaign, placement, device, or date to see bot-rate breakdowns and flagged sessions.
- Export the refund-ready report. The report packages click IDs, timestamps, session recordings, and signal reasoning in the format Google and Meta review teams expect.
- File the invalid-activity claim with the platform using the exported report as evidence. BotRefund's team can assist with claim formatting and negotiation.
Key Signals BotRefund Captures
The system groups signals into categories that map to human vs. automated behavior:
- Click behavior — Ghost click detection catches clicks that lack the natural sequence of human intent.
- Trap behavior — Honeypot interactions reveal bots that respond to hidden page elements.
- Pointer behavior — Robotic linear movements and grid-aligned paths flag scripted navigation.
- Motion behavior — Absence of humanlike mouse tremor (micro-jitter) indicates automation.
- Speed behavior — Superhuman input speed under 1 ms exceeds physical human limits.
- Engagement behavior — Absence of clicks, scrolling, or field corrections suggests non-human sessions.
- Session behavior — Unnatural durations (too short, too long, or too uniform) and missing page engagement.
Each signal is recorded as independent evidence, then cross-checked against browser, network, device, and behavioral context before the AI model assigns a bot/human classification.
How Evidence Maps to Platform Refund Requirements
Google's invalid activity credit system and Meta's traffic quality review both require click-level evidence tied to specific campaigns. Google looks for rapid clicking, duplicate click signatures, known bad IPs, and abnormal server-level patterns. Meta evaluates placement-level quality spikes, conversion events without meaningful page engagement, and contactability signals (disconnected numbers, invalid emails). BotRefund's reports provide the click IDs (GCLID/fbclid), timestamps, and behavioral proof that align with these criteria.
The platform formats reports so reviewers can verify each flagged click without translating security logs. This reduces back-and-forth and increases approval rates — BotRefund cites an 83% recovery rate across 2,500+ audits.
Common Mistakes That Weaken Evidence
- Pausing campaigns before the audit completes. This breaks the attribution chain between click IDs and sessions.
- Installing the script on only some landing pages. Missed pages create gaps in the evidence trail.
- Filtering traffic at the edge (CDN/WAF) before it reaches the page. BotRefund needs to see the full browser session to capture behavioral signals.
- Treating every bad lead as bot traffic. Real people with low intent are not fraud; the system distinguishes lead-quality variation from automation.
- Submitting aggregate estimates instead of session-level reports. Platform reviewers reject summary-only evidence.
Verification: Confirming Your Evidence Is Complete
Before filing a claim, check three things in the BotRefund dashboard:
- Click ID coverage — Every flagged session should show a GCLID or fbclid. Missing IDs mean the script didn't fire on the landing page or the click came from an untracked source.
- Signal diversity — Flagged sessions should trigger multiple independent signals, not just one. Single-signal flags are less persuasive to reviewers.
- Campaign mapping — Verify that flagged sessions map to the correct campaigns, ad sets, and creatives in your ad account. Mismatches suggest tracking-parameter issues.
If any of these checks fail, extend the collection window or troubleshoot the script installation before submitting.
Limitations and When This Doesn't Apply
- Organic and direct traffic — BotRefund focuses on paid-click attribution. Sessions without click IDs are not tied to ad spend.
- Server-side only environments — The script requires client-side execution in the visitor's browser. Pure server-to-server funnels (e.g., API-only conversions) won't generate behavioral evidence.
- Campaigns already paused — You cannot retroactively capture sessions for clicks that happened before installation.
- Platforms beyond Google and Meta — Refund-ready reports are formatted for Google Ads and Meta Ads. Other platforms may accept the evidence but have different claim processes.
- Privacy tools and corporate networks — VPNs, privacy browsers, and managed devices can produce anomalous signals. BotRefund treats these as evidence, not verdicts, and cross-checks them to avoid false positives.
Key Facts
| Metric | Detail | Source |
|---|---|---|
| Detection confidence | 99% when session evidence supports it | S2 |
| Independent signals analyzed | 110+ behavioral, browser, hardware, network, and attribution signals | S2 |
| Client recovery rate | 83% of 2,500+ audited brands recover funds from Google and Meta | S2 |
| Report format | Refund-ready with click IDs, campaign details, timestamps, session recordings, signal-by-signal reasoning | S2 |
| Key detection categories | Click, trap, pointer, motion, speed, path, engagement, session behavior | S2 |
| Evidence philosophy | Each signal is independent evidence; AI weighs complete pattern across browser, network, device, behavior | S3, S5 |
FAQ
How long does evidence collection take?
Most audits need 7–14 days of live traffic to build a representative sample. High-volume campaigns may reach significance faster; low-volume campaigns may need longer.
Can I use BotRefund evidence for a claim I already filed?
Only if the claim is still open and you can supplement it with session-level data. Platforms rarely reopen closed claims.
Does the script slow down my pages?
The snippet is lightweight and loads asynchronously. It does not block rendering or affect Core Web Vitals in typical implementations.
What if my site uses a CDN or WAF like Cloudflare?
BotRefund works alongside edge layers. The script runs in the browser after the request reaches your page, capturing behavioral signals that edge filters cannot see. You do not need to replace your CDN.
How does BotRefund differ from Google's or Meta's automatic invalid-click filters?
Platform filters operate at the server level and catch known patterns (rapid clicks, bad IPs). BotRefund adds client-side behavioral evidence — mouse movement, scroll timing, browser fingerprint — that server logs miss. This catches advanced bots that mimic human IPs and click patterns.
Can I export raw data for my own analysis?
Yes. The dashboard allows session-level export with all signals, recordings, and attribution metadata.
What happens if a real user gets flagged?
BotRefund's 99% confidence threshold requires multiple corroborating signals. Single anomalies (e.g., a privacy tool causing a browser fingerprint mismatch) are kept as evidence but not treated as verdicts. The AI model weighs the full pattern before classifying.
Further reading and comparison sources
These external sources provide additional context for evaluating the topic. Their inclusion is not an endorsement.
Learn more
Visit the website for more information.