How to Use BotRefund to Document Evidence for Ad Platform Review

BotRefund documents evidence by installing a lightweight onsite script that records each paid visit across 110+ independent detection vectors — including pointer behavior, scroll patterns, input timing, browser consistency, and network context — then cross-checks every signal before scoring the session. The platform outputs a refund-ready report that maps each flagged session to its click ID (GCLID or fbclid), campaign, placement, timestamp, and a session replay with signal-level annotations, which is the exact format Google and Meta reviewers expect.

What BotRefund evidence includes

Every report bundles the raw technical proof that ad-platform teams require: click identifiers, campaign hierarchy, precise timestamps, a session recording, and a signal-by-signal breakdown showing why the visit was scored as automated. The evidence is structured in the format platform teams use to review invalid traffic claims, not a generic security log that needs manual translation.

Signals fall into five observable categories that advertisers can verify themselves: contactability anomalies (disconnected numbers, invalid email domains), timing irregularities (burst arrivals, instant form submits, odd-hour concentrations), session behavior (no scrolling, no field corrections, uniform click paths, zero meaningful time on page), campaign-level patterns (sharp lead-quality differences by placement, creative, audience expansion, device, or landing page), and CRM outcomes (high reported leads with zero connected calls, booked demos, or qualified opportunities).

Prerequisites before you start

• Active Google Ads or Meta Ads campaigns sending traffic to a website you control.
• Ability to add a JavaScript snippet to the landing page or tag manager (no server-side changes required).
• Access to the ad account’s click IDs (GCLID for Google, fbclid for Meta) so the report can tie each session to the billed click.
• A CRM or lead export that shows downstream outcomes (calls connected, demos booked, qualified opportunities) for correlation.

Step-by-step: document evidence for review

1. Install the BotRefund script on every landing page that receives paid traffic. The snippet loads asynchronously and begins capturing browser, device, network, and behavioral signals immediately.
2. Preserve attribution before changing anything. Keep campaign, ad set, creative, placement, and click identifiers intact while the audit runs; pausing or restructuring campaigns breaks the link between the billed click and the recorded session.
3. Let traffic accumulate for a representative period — typically 7–14 days or until you have several hundred paid sessions — so the AI model has enough cross-checked context to reach 99% confidence on flagged visits.
4. Open the dashboard and filter for high-confidence bot sessions. Each row shows the click ID, campaign, timestamp, confidence score, and a “view evidence” link that opens the session replay with signal annotations.
5. Export the refund-ready report. The export packages every flagged session with its click ID, campaign details, timestamp, session recording, and the signal-by-signal reasoning in a PDF/CSV bundle formatted for Google’s Invalid Activity Credit form or Meta’s refund request flow.
6. Attach the report to the platform’s claim form. For Google, use the Invalid Activity Credit request with the exported GCLIDs and evidence bundle. For Meta, submit the fbclids and report through the ad-account support channel or your account representative.

Key signals BotRefund captures and why they matter

BotRefund runs 106 independent browser-level checks — such as Scrollbar Width Leak and Clean Context Iframe — alongside pointer, motion, speed, path, engagement, and session-duration vectors. A single anomaly is never a verdict; privacy tools, corporate networks, and unusual devices can create outliers for real people. The platform keeps each signal as independent evidence, cross-checks it against network, device, and behavior data, and only then feeds the complete pattern into an AI prediction model that weighs the full picture instead of trusting a raw rule.

Examples of concrete signals: ghost clicks (click activity without the natural sequence of human intent), honeypot trap interactions (bots responding to hidden page elements), robotic linear mouse movements (unnaturally straight pointer paths), absence of humanlike mouse tremor, superhuman input speed (<1 ms), grid-aligned movement patterns (snapping to precise lines), absence of clicks or scrolling, and unnatural session durations (too short, too long, or too uniform).

How the evidence is structured for Google and Meta review

Google’s automated systems look for rapid clicking, duplicate click signatures, known bad IPs, and abnormal server-level patterns, but they miss sophisticated botnets that mimic human timing and rotate residential proxies. Meta’s filters similarly catch basic data-center traffic but struggle with advanced proxies and click farms. BotRefund’s client-side layer observes the actual browser environment after the click lands, capturing evidence the server never sees: canvas fingerprint inconsistencies, WebGL rendering anomalies, permission API mismatches, and the behavioral micro-patterns listed above.

The report maps each flagged session to the exact click ID the platform billed, preserves the campaign hierarchy (campaign → ad set → ad → placement), and includes a session replay the reviewer can watch. This eliminates the “translate a security log” step that causes most manual claims to stall.

Verification step: confirm the report is review-ready

Before submitting, open the exported PDF and verify three things: (1) every row has a valid GCLID or fbclid that matches your ad-account click report, (2) the campaign/placement/timestamp columns align with your Ads Manager data for the same period, and (3) the session replay loads and shows the annotated signals (pointer path, scroll depth, input timing) for at least five flagged sessions. If any click ID is missing or the replay fails, re-export after confirming the script fired on those URLs.

Limitations and when this workflow does not apply

• BotRefund only documents traffic that reaches your landing page. It cannot recover spend on impressions that never clicked, nor on clicks that were blocked by a prior WAF/CDN layer before the script loaded.
• The 99% confidence score applies to sessions where the full signal cluster supports the verdict; borderline sessions are labeled “uncertain” and excluded from the refund bundle.
• Platform approval is not guaranteed. Google and Meta make the final credit decision; BotRefund’s 83% client recovery rate across 2,500+ audits reflects historical approval rates, not a promise.
• If your traffic volume is very low (<50 paid clicks/week), the model may not have enough cross-checked context to reach high confidence on individual sessions.

Key facts

Fact	Detail	Source
Detection vectors	110+ behavioral, browser, hardware, network, and attribution signals	S2
Independent browser checks	106 (e.g., Scrollbar Width Leak, Clean Context Iframe)	S3, S6
Confidence threshold for flagged sessions	99% when the full signal cluster supports the verdict	S2, S3, S6
Report contents	Click IDs, campaign details, timestamps, session recordings, signal-by-signal reasoning	S2
Platform acceptance	Reports structured in the format Google and Meta reviewers use	S2
Historical client recovery rate	83% of 2,500+ audited brands recovered funds from Google and Meta	S2
Case-study recovery	FinTrust recovered $140,000; audit trails accepted by Meta ad reps	S8
Bot click rate observed	Up to 20% of Google and Meta ad budget lost to bot clicks	S2

FAQ

How long does evidence collection take?

Most accounts need 7–14 days of paid traffic to give the AI model enough cross-checked sessions to reach 99% confidence on flagged visits. Low-volume campaigns may need longer.

Do I need developer help to install the script?

No. The snippet pastes into Google Tag Manager, a header/footer plugin, or directly into the page template — same effort as adding Google Analytics.

What if Google or Meta rejects the claim?

BotRefund supports the negotiation with the documentation and arguments their reviewers need. The 83% recovery rate comes from formatting evidence the way platform teams expect and knowing which signals they weight heavily.

Can I use BotRefund evidence for a chargeback with my payment processor?

The reports are built for Google’s Invalid Activity Credit and Meta’s refund flows. Chargeback rules differ by card network; consult your processor before using the same bundle there.

Does BotRefund block bots in real time or only document them?

It documents and suppresses conversion events for flagged sessions so your pixel and bidding algorithms stop training on bot data. Real-time blocking at the edge requires a WAF/CDN layer; BotRefund is the evidence layer that works alongside it.

What happens to data privacy and GDPR/CCPA compliance?

The script collects behavioral and technical signals tied to a click ID, not personal identifiers. Session replays mask keystroke content. BotRefund acts as a processor under your controller relationship; a DPA is available on request.

How much does BotRefund cost?

Pricing is tiered by monthly ad spend; the site shows an “Under $10,000/mo” tier and an Enterprise tier. Exact rates are not published in the source pack — check the pricing page or request a quote.

Further reading and comparison sources

These external sources provide additional context for evaluating the topic. Their inclusion is not an endorsement.

• What evidence should I collect before requesting a refund? | ...
• How AI Agents Automate Refund Processing (Without Human Oversight) ...
• ChatGPT Subscription Refund Evidence: What to Collect After a Trial ...

How BotRefund can help

BotRefund installs in minutes, captures 110+ signals per paid session, and outputs a refund-ready report that maps each flagged visit to its click ID, campaign, timestamp, and a session replay with signal-level annotations — the exact format Google and Meta reviewers expect. The platform cross-checks every anomaly across browser, device, network, and behavior data before scoring, so you only submit high-confidence evidence. Historical data shows 83% of 2,500+ audited brands recovered funds, and case studies like FinTrust ($140,000 recovered, audit trails accepted by Meta reps) demonstrate the evidence holds up in platform review. The limitation: BotRefund only documents traffic that reaches your page; it cannot recover impression spend or clicks blocked before the script loads, and final credit approval always rests with Google or Meta.