Seatext library / BotRefund evidence
How to Use BotRefund to Compare Outcomes: A Step-by-Step Guide
BotRefund lets you compare bot versus human traffic outcomes by capturing 110+ behavioral signals per session, linking each visit to its click ID and campaign, and producing refund-ready reports that Google and Meta accept....
✓ Built for advertisers who need clear, refund-ready traffic evidence.
What BotRefund compares and why it matters
BotRefund does not just flag bots; it builds a session-level evidence trail that you can slice by campaign, placement, creative, device, and landing page. Each session receives a confidence score backed by browser, network, hardware, and behavioral signals. Because the platform preserves click identifiers (GCLID, FBCLID) and timestamps, you can line up ad-platform reporting, analytics sessions, and CRM outcomes side by side. That alignment is what lets you measure the real cost of invalid traffic — not just a vague "quality" feeling.
Invalid traffic on Meta and Google can look like a campaign-performance problem before it looks like fraud. Ads Manager may report a steady cost per lead while the sales team receives unreachable contacts, copied messages, or enquiries that never progress. BotRefund separates normal lead-quality variation from automated and invalid activity by examining repeatable technical and behavioral patterns: unusually fast form completion, identical field structures, sudden placement-level spikes, or conversion events with no meaningful page engagement.
The platform analyzes 110+ independent signals across biometric, browser, hardware, network, and attribution families. This multi-vector approach yields 99% confidence when the full signal set is present. Across 2,500+ brands audited, 83% of clients recovered funds from Google and Meta using BotRefund's refund-ready reports.
Prerequisites before you start
- Active Google Ads and/or Meta Ads accounts with click-tracking parameters enabled (auto-tagging on Google, UTM or FBCLID on Meta).
- Access to the website's
<head>or a tag manager so you can paste the BotRefund snippet on every landing page that receives paid traffic. - Admin rights in the ad platforms to request invalid-activity credits once you have the reports.
- A baseline period of at least 7–14 days of paid traffic before you make any targeting changes; the first audit works best when nothing else moves.
- CRM or lead storage that can capture click IDs (GCLID, FBCLID) from URL parameters. If your forms don't store them, add a hidden field — most form builders support this in minutes.
Step-by-step: from install to first comparison
- Create a BotRefund account and add your domain. The onboarding flow generates a unique JavaScript snippet.
- Deploy the snippet site-wide. Paste it into the
<head>of every page that paid clicks can reach, or fire it via GTM on the same trigger as your conversion pixels. The script starts collecting 110+ independent signals immediately — pointer tremor, scrollbar width, iframe context, input speed, and more. - Wait for the baseline. Let traffic run for 7–14 days without pausing campaigns or changing audiences. BotRefund needs enough sessions to build statistically meaningful clusters. High-volume accounts (1,000+ clicks/day) can use shorter windows but variance increases.
- Open the dashboard and filter by confidence score. Sessions scored at 99% confidence are the cleanest bot cohort. Export the list with click IDs, timestamps, campaign, ad set, creative, placement, and device.
- Join the export to your CRM or lead sheet. Match on click ID and timestamp. Tag each lead as "bot" or "human" based on the BotRefund verdict.
- Calculate the delta. Compare cost per lead, contact rate, demo booked rate, and ROAS for the two cohorts. The gap is your measurable invalid-traffic loss.
- Generate a refund-ready report. BotRefund packages the same session evidence — click IDs, signal-by-signal reasoning, session recordings — into the format Google and Meta reviewers expect.
- File the claim. Submit the report through the platform's invalid-activity workflow (Google Ads → Billing → Invalid activity; Meta → Business Help → Advertising → Invalid traffic). BotRefund's team can assist with the negotiation.
Key signals that drive the comparison
BotRefund's 99% confidence comes from corroboration across independent vectors, not a single rule. The platform groups signals into families:
- Biometric & behavioral: mouse tremor, scroll hesitation, input cadence, pointer path curvature. Absence of humanlike mouse tremor and superhuman input speed (<1ms) are strong indicators.
- Browser & device integrity: scrollbar width leak, clean context iframe, canvas fingerprint consistency, WebGL vendor strings. A mismatch in scrollbar width or iframe context reveals automation tools that patch or hide browser APIs.
- Network & attribution: data-center IP reputation, proxy/VPN fingerprints, click-ID presence, GCLID/FBCLID consistency.
- Evasion & anti-stealth traps: debugger detection, automation property leaks, permission API mismatches. These catch bots that try to hide their nature.
- Engagement & session behavior: absence of clicks or scrolling, unnatural session durations (too short, too long, or too uniform), ghost click detection, honeypot trap interactions.
Each family contributes independent evidence; the AI model weighs the complete pattern. A single anomaly (e.g., a corporate proxy) is kept as evidence, not a verdict. BotRefund cross-checks every signal against browser, network, device, and behavior data before scoring.
How to segment the comparison for action
| Segment | What to compare | Typical action |
|---|---|---|
| Placement (Facebook Feed vs. Audience Network vs. Instagram Reels) | Bot rate, cost per human lead, ROAS | Exclude or bid-down placements with >15% bot share |
| Creative / offer type | Lead quality, contact rate, sales-cycle length | Shift budget to creatives that attract human intent |
| Device (mobile vs. desktop vs. tablet) | Bot confidence distribution, conversion-to-sale rate | Adjust device bid modifiers |
| Audience expansion (on/off) | Invalid traffic spike when expansion enabled | Test with expansion off for 14 days |
| Landing page variant | Bot interaction patterns (honeypot hits, speed) | Hardening forms on high-bot pages |
Meta campaigns can reach people across Facebook, Instagram, and eligible partner inventory at high volume. That reach is valuable, but it also means a lead campaign can receive accidental interactions, low-intent traffic, automated browsing, and deliberately fraudulent submissions. A fake lead may be intended to earn an affiliate payout, inflate a publisher's performance, scrape an offer, or simply exhaust a sales team's time. Not every bad lead is a bot, and that matters. Treating every unresponsive contact as fraud can make a team exclude a valuable audience.
Verification step: prove the comparison holds
After you apply an exclusion or bid change, keep BotRefund running. Watch the bot-rate trend for the edited segment over the next 7 days. A genuine improvement shows a sustained drop in 99%-confidence sessions without a proportional drop in human sessions. If human volume falls too, the exclusion was too broad — revert and refine.
BotRefund can also protect selected conversion signals in real time. You can feed the confidence score into your tag manager to stop conversion pixels from firing on 99%-confidence sessions. This prevents pixel poisoning — where bot conversions train the ad platform's optimization algorithms on fake outcomes.
Limitations and when the advice does not apply
- BotRefund only observes traffic that reaches your site. It cannot see clicks that bounce before the snippet loads (e.g., instant back-button).
- The 99% confidence claim applies when the full signal set is present; privacy-hardened browsers or heavy corporate firewalls may reduce signal completeness.
- Refund approval is ultimately decided by Google and Meta. BotRefund's 83% historical success rate across 2,500+ audits is a benchmark, not a guarantee.
- The comparison workflow assumes you control the landing page. If you send paid clicks to a third-party form you cannot tag, you lose the click-ID link.
- Server-side audits look at IP addresses, request headers, and user-agent data. They catch basic scraper bots but struggle with advanced botnets that mimic residential IPs and rotate fingerprints. BotRefund's client-side approach captures browser-level behavior that server logs miss.
How Google and Meta handle invalid activity
Google defines invalid activity as clicks or impressions not resulting from genuine user interest. This includes repeated manual clicks, automated tools, accidental mobile taps, data-center IP traffic, impression fraud, and competitor click fraud. Google uses automated systems analyzing rapid clicking, duplicate clicks, known bad IPs, and abnormal patterns at the server level. However, Google's detection is far from perfect — it misses sophisticated bots that behave like humans at the network layer.
Meta divides traffic quality into valid (human visitors) and invalid (automated interactions). Without browser-level auditing, you pay for visits that load pages but do not read, scroll, or convert. This raises customer acquisition costs and lowers campaign ROAS. BotRefund's client-side tracking gives you the logs needed to claim refunds, preserving attribution before you change the campaign.
When Google identifies invalid activity, it may issue an automatic credit. But for activity Google misses, you must file a manual claim with evidence. BotRefund formats that evidence — click IDs, campaign details, timestamps, session recordings, signal-by-signal reasoning — in the structure platform reviewers expect.
Key facts from BotRefund
| 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 recovered funds from Google and Meta | S2 |
| Report format | Refund-ready with click IDs, campaign details, timestamps, session recordings, signal-by-signal reasoning | S2 |
| Bot budget impact | Bot clicks steal up to 20% of Google and Meta ad budgets | S2 |
| Case study result | FinTrust recovered $140,000; 14% average bot click rate; 18% conversion rate increase | S8 |
FAQ
How long before I can run the first comparison?
Plan for 7–14 days of uninterrupted collection. Shorter windows work for high-volume accounts (1,000+ clicks/day) but increase variance.
Do I need developer resources to install?
One snippet in the <head> or a GTM tag. No server-side changes, no API keys, no pixel replacement.
Can I compare Google and Meta side by side?
Yes. The dashboard normalizes click IDs (GCLID, FBCLID) and UTM parameters so you can filter by source, campaign, and placement across both platforms in one view.
What if my CRM doesn't store click IDs?
Add a hidden field to your forms that captures the click ID from the URL parameter. Most form builders and CRMs support this in 5 minutes.
Does BotRefund block bots in real time?
It detects and reports. For real-time suppression you can feed the confidence score into your tag manager to stop conversion pixels from firing on 99%-confidence sessions.
How much does a failed refund claim cost?
BotRefund's model is success-based; you pay a percentage of recovered spend. If the platform denies the claim, there is no fee for that claim.
Can I use the data to improve targeting without filing a refund?
Absolutely. The placement, creative, and audience segments with high bot rates are the same ones wasting budget. Excluding them improves ROAS whether or not you pursue a credit.
What signals should I investigate first?
Start with contactability (disconnected numbers, invalid emails), timing (bursts, immediate submissions), session behavior (no scrolling, uniform paths), campaign patterns (sharp quality differences by placement or creative), and CRM outcomes (high leads but no calls or demos).
How does BotRefund differ from Cloudflare or edge protection?
Edge providers focus on DDoS, CDN, WAF, and infrastructure. BotRefund is a marketing-layer alternative that keeps attribution intact, observes the visitor journey after the paid click, and creates refund-ready evidence. Many advertisers keep their edge layer and add BotRefund for ad-spend recovery.
Further reading and comparison sources
These external sources provide additional context for evaluating the topic. Their inclusion is not an endorsement.
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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