Seatext library / BotRefund evidence
How to Understand Platform Policies for Invalid Traffic: A Practical Guide for Advertisers
Platform policies define invalid traffic as automated or non-genuine interactions that inflate costs — Meta and Google each publish their own definitions, detection methods, and refund processes. Start by reading the official policy pages,...
✓ Built for advertisers who need clear, refund-ready traffic evidence.
Platform policies for invalid traffic tell you what each ad network considers billable versus refundable. Meta and Google both define invalid traffic broadly — automated clicks, accidental taps, competitor click fraud, and publisher fraud — but they differ in how they detect it, what evidence they accept, and whether refunds are automatic or require a manual claim. Understanding these policies means reading the official definitions, knowing the detection gaps, and learning how to document violations in the format each platform's review team expects.
Most advertisers discover a policy gap only after they see a discrepancy: Ads Manager reports a steady cost per lead while the sales team gets disconnected numbers, copied messages, or enquiries that never progress. That gap is where policy knowledge becomes practical. You need to map platform definitions to your own funnel data — click IDs, session recordings, CRM outcomes — so you can spot the patterns the platform's automated filters missed and file a claim that gets approved.
What Platform Policies Actually Cover
Both Meta and Google split traffic into valid (human visitors with genuine interest) and invalid (automated interactions, accidental clicks, and deliberate fraud). The categories overlap but the wording matters when you file a claim.
- Meta focuses on "invalid traffic" across Facebook, Instagram, and partner inventory. Their policy covers accidental interactions, low-intent traffic, automated browsing, and deliberately fraudulent submissions meant to earn affiliate payouts or inflate publisher performance.
- Google uses the term "invalid activity" and lists repeated manual clicks, automated tools and bots, accidental mobile taps, data-center IP ranges, impression fraud from auto-refresh tools, and competitor click fraud.
Neither platform treats every bad lead as fraud. A weak campaign can attract real people who aren't ready to buy. The policy distinction is evidence: bot traffic and form spam leave repeatable technical and behavioral patterns — unusually fast form completion, identical field structures, sudden placement-level spikes, or conversion events with no meaningful page engagement.
Key Differences Between Meta and Google Policies
| Aspect | Meta (Facebook/Instagram) | Google Ads |
|---|---|---|
| Policy term | Invalid traffic | Invalid activity |
| Automatic credits | Limited; most refunds require manual review | Partial automatic filtering; manual claims for missed activity |
| Evidence format | Click IDs, placement breakdowns, session behavior logs | GCLIDs, click timestamps, IP data, behavioral proof logs |
| Claim process | Contact support or account representative; provide structured evidence | Click Quality team investigation form; submit GCLIDs and logs |
| Detection focus | Placement-level quality, audience expansion, creative-level patterns | Server-level patterns: rapid clicking, duplicate signatures, known bad IPs |
| Refund success factors | Structured audit comparing ad-platform data, website sessions, CRM outcomes | Client-side behavioral evidence that supplements server-side gaps |
If your campaigns rely heavily on placement-level quality signals, prioritize Meta's evidence format; if server-level pattern detection is your focus, align with Google's GCLID-based claims process.
Takeaway: Meta's policy leans on placement and creative-level signals; Google's leans on server-level patterns. Both miss sophisticated residential proxy networks and modern botnets — that's where your own client-side audit fills the gap.
How to Read and Apply Policy Documentation
- Bookmark the official pages. Meta's Business Help Center "Traffic Quality" section and Google Ads Help "Invalid activity" page are the primary sources. They change; check quarterly.
- Identify the evidence requirements. Each policy lists what the review team needs: click identifiers (fbclid, gclid), campaign/ad set/ad IDs, timestamps, placement reports, and behavioral session data.
- Map policy categories to your funnel. Create a simple spreadsheet: policy category (e.g., "automated tools") → your detectable signal (e.g., superhuman input speed <1ms) → your data source (client-side script, CRM, analytics).
- Note the exclusions. Both platforms exclude accidental clicks from refunds unless they form a pattern. Low-intent but human traffic is not invalid under policy.
Step-by-Step: Auditing Your Traffic Against Platform Rules
This workflow mirrors the practical investigation process used by advertisers who successfully recover spend.
- Preserve attribution before changing the campaign. Keep campaign, ad set, creative, placement, and click identifiers intact. Turning off a placement or pausing an ad set breaks the evidence chain.
- Pull platform reports. Export lead counts, cost per lead, and placement breakdowns from Ads Manager (Meta) or Google Ads reports. Segment by placement, device, audience expansion, and creative.
- Match to website sessions. Use your analytics or a client-side detection script to join each click ID to a session recording: scroll depth, field interactions, time on page, mouse movement, form completion speed.
- Match to CRM outcomes. Tag each lead with contactability (valid phone/email), sales-team result (connected, demo booked, qualified), and repeat engagement.
- Flag policy-violating patterns. Look for the signals the policies describe: bursts of leads in short windows, forms submitted immediately after landing, no scrolling or field corrections, uniform click paths, high lead count with zero CRM progression.
- Build the refund-ready report. Structure each finding with click ID, campaign details, timestamp, session recording link, and signal-by-signal reasoning. Format it the way the platform's review team expects.
- Submit the claim. For Meta: contact support or your account rep with the structured report. For Google: complete the Click Quality investigation form with GCLIDs and behavioral logs.
Common Mistakes When Interpreting Policies
| Mistake | Why It Hurts | Better Approach |
|---|---|---|
| Treating every unresponsive lead as fraud | Wastes time on claims that get rejected; may cause you to exclude valuable audiences | Start with a structured audit comparing ad data, sessions, and CRM outcomes before changing targeting or filing |
| Relying only on platform auto-filters | Both Meta and Google miss advanced residential proxies and modern botnets | Add client-side behavioral detection (100+ signals) to catch what server-side filters miss |
| Submitting raw logs without structure | Reviewers reject unformatted evidence; they need click IDs, timestamps, and signal reasoning in their template | Use a refund-ready report format: click ID, campaign, timestamp, session recording, signal-by-signal explanation |
| Changing campaigns before preserving evidence | Pausing placements or ads breaks the attribution chain needed for a claim | Export all IDs and session data first; then optimize |
| Confusing low lead quality with invalid traffic | Policy doesn't cover real humans who aren't ready to buy | Separate contactability/timing/session behavior signals from CRM outcome signals; only the former indicate policy violations |
When to Escalate: Filing Claims and Refund Requests
File a claim when your audit shows a pattern the platform's automated systems missed and you have structured evidence matching their evidence requirements. The strongest claims combine:
- Click identifiers (fbclid/gclid) for every suspicious session
- Campaign, ad set, creative, and placement metadata
- Timestamps aligned with platform reporting
- Session recordings showing non-human behavior (no scroll, superhuman speed, linear mouse paths, honeypot interactions)
- CRM outcome data proving zero commercial value
Meta claims typically go through support or an account representative. Google claims use the Click Quality team investigation form. In both cases, the review team looks for evidence formatted to their internal standards — not screenshots or raw analytics exports.
Limitations of Platform-Provided Protections
- Server-side detection has blind spots. Google's systems analyze IP addresses, request headers, and user-agent data at the server level. They struggle with residential proxy networks and headless browsers that mimic real device fingerprints.
- Meta's placement-level filters don't catch creative-level or audience-expansion fraud. A campaign can show healthy aggregate metrics while a single placement or expanded audience drives automated submissions.
- Automatic credits are partial. Both platforms issue some credits automatically, but the majority of sophisticated invalid traffic requires a manual claim with client-side evidence.
- Policy language is broad; enforcement is narrow. "Automated tools" covers a wide range, but reviewers only approve claims with specific, reproducible behavioral signals.
- No real-time suppression. Platform policies are retrospective — they refund after the fact. They don't prevent the next bot click from charging you.
Key Facts from BotRefund's Platform Policy Work
| Fact | Detail | Source |
|---|---|---|
| Bot detection confidence | 99% confidence across 110+ behavioral, browser, hardware, network, and attribution signals | S2 |
| Client refund recovery rate | 83% of 2,500+ audited brands recover funds from Google and Meta | S2 |
| Evidence format accepted by platforms | Refund-ready reports with click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning | S2 |
| Meta invalid traffic signals | Contactability issues, timing bursts, session behavior (no scroll, uniform paths), campaign patterns (placement/creative/device), CRM outcome gaps | S1 |
| Google invalid activity categories | Repeated manual clicks, automated tools/bots, accidental mobile taps, data-center IPs, impression fraud, competitor click fraud | S4 |
| Google detection signals | Rapid clicking, duplicate click signatures, known bad IPs, abnormal server-level patterns | S4 |
| Typical bot budget impact | Bot clicks steal up to 20% of Google and Meta ad budgets | S2 |
| Case study recovery | FinTrust recovered $140,000 (14% bot click rate, +18% conversion rate after suppression) | S6 |
Terminology Quick Reference
- Invalid traffic (Meta) / Invalid activity (Google): Platform terms for non-genuine interactions they may refund.
- Click ID (fbclid, gclid): Unique identifier appended to landing-page URLs; essential for joining platform data to session data.
- Client-side detection: JavaScript running in the visitor's browser that captures behavioral signals (mouse movement, scroll, timing, browser APIs) — catches what server logs miss.
- Server-side detection: Analysis of IP, headers, user-agent at the network level — catches basic scrapers but misses advanced bots.
- Refund-ready report: Structured evidence package formatted to the platform reviewer's template: click IDs, timestamps, session recordings, signal reasoning.
- Pixel poisoning: Invalid traffic corrupting conversion pixels, causing bidding algorithms to optimize for bot patterns.
- Honeypot trap: Hidden page element that only bots interact with; a behavioral signal.
FAQ
Does Meta automatically refund invalid traffic?
Meta issues some automatic credits, but most sophisticated invalid traffic — especially residential proxy networks and placement-level fraud — requires a manual claim with structured evidence.
What evidence does Google's Click Quality team actually accept?
GCLIDs, click timestamps, IP data, and client-side behavioral proof logs (session recordings, mouse movement, scroll depth, form timing) formatted in their investigation form template.
Can I claim a refund for low-quality but human leads?
No. Platform policies only cover automated, accidental, or deliberately fraudulent interactions. Real people who don't convert are a targeting or creative issue, not a policy violation.
How long do I have to file a claim?
Both platforms have lookback windows (typically 60-90 days for manual claims). Preserve click IDs and session data continuously; don't wait for a quarterly review.
What's the difference between a bot audit and a platform refund request?
A bot audit produces the evidence (click IDs, session recordings, signal reasoning). The refund request is the formal submission to the platform's review team using that evidence in their required format.
Do I need a developer to implement client-side detection?
Most detection scripts install via a single JavaScript snippet or tag manager. No backend changes required. The script captures behavioral signals and ties them to click IDs automatically.
What if my claim is denied?
Denials usually mean missing evidence format or insufficient signal corroboration. Re-audit with more behavioral signals (100+ independent checks), restructure the report to match the platform's template, and resubmit. Escalation to a dedicated account rep (Meta) or repeated submission with new evidence (Google) can succeed.
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