BotRefund lets you capture click‑level data, preserve attribution before you pause or change a campaign, and export a refund‑ready report that ties each invalid click to its original ad set, creative, and placement. Install the BotRefund tag, enable campaign‑level reporting, and run an audit to generate click IDs that you can submit to Google or Meta for a refund.

click_id, timestamp, campaign, ad_set, creative, placement, bot_score, evidence
fbclid=AbC123, 2026-08-20T14:32:10Z, "LeadGen_Summer", "Lookalike_25-34", "Video_Ad_01", "Feed", 0.99, "Ghost click, linear mouse path, no scroll"

• Refund - bot run campaign - Google Ads Community
• Invalid traffic and bot clicks on my campaigns. Looking for a refund ...
• [UA] About Refund Data [Legacy] - Analytics Help

BotRefund works by installing a lightweight script on your landing pages that captures 106 independent browser, network, device, and behavioral signals per session. The system cross-checks these signals through an AI model that reaches 99% confidence when evidence supports it, then packages findings into reports structured for Google and Meta invalid-traffic review teams. The most common mistakes advertisers make are changing campaigns before preserving click IDs, relying only on server-side logs, treating every poor lead as fraud, and submitting unstructured evidence that platform reviewers reject.

What BotRefund Does

BotRefund is a client-side detection and evidence layer for paid search and social campaigns. It sits on your landing pages and records each visitor's behavioral fingerprint — mouse movement, scroll patterns, typing rhythm, browser API consistency, hardware signals, and network context. Unlike server-side filters that only see IP addresses and headers, BotRefund observes what the visitor actually does in the browser. This catches advanced botnets, headless browsers, and click farms that rotate IPs and spoof user agents but cannot perfectly replicate human micro-behaviors.

The output is not a simple block list. Each flagged session gets a signal-by-signal explanation showing exactly which checks fired and why the AI classified the visit as automated. These session records are then assembled into refund-ready reports that include click IDs (GCLIDs for Google, fbclids for Meta), campaign hierarchy, timestamps, and session replays — formatted the way platform review teams expect to see them.

Prerequisites Before You Start

• Active Google Ads or Meta Ads campaigns with click tracking enabled (auto-tagging for Google, Meta Pixel for Facebook/Instagram).
• Access to your website's or tag manager to install the BotRefund script before the closing tag.
• CRM or lead database access to match ad-platform lead counts against actual sales outcomes (calls connected, demos booked, qualified opportunities).
• Admin access to ad accounts for submitting invalid-activity claims once reports are generated.
• At least 2–4 weeks of traffic before expecting statistically meaningful detection; the system needs volume to establish baselines.

Step-by-Step: Using BotRefund to Avoid Common Mistakes

1. Install the script before launching or changing campaigns. Place the BotRefund snippet in the of every landing page that receives paid traffic. This ensures click IDs (GCLID, fbclid, msclkid) are captured from the first visit. Mistake avoided: changing targeting or pausing campaigns before attribution data is preserved.
2. Run a free bot audit to establish a baseline. BotRefund offers a free audit that scans recent traffic and returns a sample report. Use this to see your current invalid-traffic rate and identify which placements, creatives, or audiences show the strongest bot signals. Mistake avoided: guessing where fraud lives without data.
3. Cross-reference three data layers weekly. Compare (a) ad-platform reported leads, (b) BotRefund-flagged sessions tied to click IDs, and (c) CRM outcomes (contact rates, qualification rates, revenue). Look for gaps: high reported leads but low CRM contactability, or sharp quality differences by placement. Mistake avoided: treating every unresponsive lead as fraud when some audiences simply convert slower.
4. Suppress flagged conversion events in-platform. Use BotRefund's conversion-signal protection to stop poisoned conversion data from training Google's or Meta's bidding algorithms. This prevents the platforms from optimizing toward bot-like behavior patterns. Mistake avoided: letting pixel poisoning compound wasted spend over months.
5. Generate refund-ready reports monthly. When the dashboard shows clustered invalid traffic with high-confidence signals, export the structured report. It includes click IDs, campaign/ad set/ad details, timestamps, session recordings, and signal-by-signal reasoning — formatted for Google's invalid activity credit system and Meta's traffic quality review. Mistake avoided: submitting screenshots or raw logs that reviewers cannot process.
6. File claims through the correct platform channel. For Google, use the invalid activity credit request form with the BotRefund report attached. For Meta, work through your ad representative or the traffic quality appeal flow. BotRefund's team can assist with claim wording and follow-up. Mistake avoided: assuming refunds are automatic; Google and Meta both require advertiser-initiated claims for most non-automatic credits.
7. Verify the outcome and iterate. After credits are issued (or denied), check the refund amount against the flagged spend in your report. Adjust targeting exclusions, placement blocks, or audience settings based on which segments showed the highest bot rates. Mistake avoided: treating one claim as a fix instead of an ongoing quality loop.

Key Facts

Common Mistakes and How BotRefund Addresses Them

Mistake 1: Changing campaigns before preserving attribution

Advertisers often pause low-performing ad sets or shift budgets the moment lead quality drops. This severs the link between the click ID and the downstream session data. BotRefund's first workflow step is "preserve attribution before changing the campaign" — capture GCLIDs and fbclids while the campaign is live so evidence remains intact for later claims.

Mistake 2: Relying only on server-side logs

Server logs show IPs, user agents, and request headers. Advanced bots rotate residential proxies, spoof Chrome user agents, and mimic human request timing. They fail at client-side execution: canvas rendering, WebGL parameters, mouse tremor, scrollbar width consistency, iframe context integrity. BotRefund's 106 checks operate in the browser where these evasion attempts break down.

Mistake 3: Treating every bad lead as bot fraud

Not every unresponsive contact is automated. Real people submit forms with typos, use throwaway emails, or change their minds. BotRefund distinguishes by looking for repeatable technical patterns: superhuman input speed (<1ms), grid-aligned mouse paths, absent scroll behavior, identical field structures across sessions. A single anomaly is never a verdict; the AI weighs the complete pattern across browser, network, device, and behavior evidence.

Mistake 4: Submitting unstructured evidence to platforms

Google's invalid activity credit system and Meta's traffic quality team review thousands of claims. They expect click IDs, campaign metadata, timestamps, and a clear signal narrative. Raw analytics exports or security logs get rejected. BotRefund automates the report format both platforms accept, including session replays reviewers can watch.

Mistake 5: Letting poisoned conversions train bidding algorithms

When bot conversions fire your Meta Pixel or Google Ads conversion tag, the platforms learn to find more traffic that looks like those bots. BotRefund's conversion-signal protection suppresses flagged events in real time so only verified human conversions train the optimization models.

Mistake 6: Expecting automatic refunds

Google issues some invalid activity credits automatically, but the majority — especially for sophisticated botnets — require an advertiser-initiated claim with evidence. Meta's process is similarly manual. BotRefund's 83% recovery rate across 2,500+ audits comes from knowing how to package and argue claims that reviewers approve.

Limitations and When This Advice Doesn't Apply

• Brand awareness / video view campaigns where the goal is impressions, not clicks or conversions. BotRefund optimizes for click and conversion fraud detection.
• Traffic from non-Google/Meta sources (TikTok, LinkedIn, programmatic DSPs). The refund-ready reports are structured for Google and Meta review processes; other platforms have different evidence requirements.
• Sites blocking third-party scripts via strict CSP or ad blockers that prevent the detection script from loading. A portion of traffic will remain unobserved.
• Very low volume campaigns (< 500 clicks/month). Statistical confidence requires enough sessions to establish behavioral baselines and detect outliers.
• Advertisers without CRM outcome data. Without matching ad leads to sales results, you cannot validate which flagged sessions represent actual waste versus slow-converting audiences.

Terminology

• Click ID (GCLID, fbclid, msclkid) — Unique identifier appended to landing page URLs by ad platforms. Links a specific ad click to the subsequent session. Essential for refund claims.
• Pixel poisoning — When bot conversions fire your tracking pixel, causing the platform's optimization algorithm to target more bot-like traffic.
• Invalid activity credit — Google's term for refunds issued for clicks/impressions deemed non-genuine. Can be automatic or claim-based.
• Traffic quality appeal — Meta's process for advertisers to contest lead quality and request refunds for invalid traffic.
• Client-side detection — Analysis running in the visitor's browser (JavaScript), capturing behavioral and environmental signals invisible to server logs.
• Signal — One independent check (e.g., scrollbar width leak, clean context iframe, mouse tremor) that contributes evidence toward the AI's classification.
• Cross-checked context — BotRefund's method: no single signal triggers a verdict; the AI evaluates how all 106 signals fit together for each session.

FAQ

How long until I see results after installing BotRefund?

Detection starts immediately, but meaningful patterns emerge after 2–4 weeks of traffic volume. The free audit can scan historical data if click IDs were already captured.

Does BotRefund block bots in real time?

It suppresses conversion events for flagged sessions in real time (preventing pixel poisoning). It does not block page loads or show CAPTCHAs; the focus is evidence collection for refunds and algorithm protection.

What if Google or Meta denies my claim?

BotRefund's team assists with claim wording and follow-up. The 83% recovery rate reflects cases where evidence was sufficient for approval. Denials typically occur when flagged spend is too low to meet platform thresholds or when evidence gaps exist (e.g., missing click IDs).

Can I use BotRefund alongside Cloudflare or other WAFs?

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 pricing requires a quote; the free audit is available at any spend level.

What happens to my data?

Session recordings and signal data are stored for report generation and claim support. BotRefund's privacy approach treats each signal as evidence, not a verdict, and cross-checks against privacy tools, corporate networks, and unusual devices to avoid false positives.

Do I need technical skills to install and use it?

Installation is a single script tag in — manageable via Google Tag Manager or direct HTML edit. The dashboard is designed for marketing teams; no SQL or log analysis required. Refund claim assistance is included.

What a baseline means in bot detection

A baseline is a reference profile of how real visitors behave on your pages after clicking an ad. It captures the range of normal variation — scroll depth, mouse tremor, typing rhythm, session duration, navigation paths — so that automated traffic stands out as a statistical outlier rather than a guess. BotRefund builds this profile by running 106 independent checks on every session and feeding the combined pattern into an AI model that reaches 99% confidence when the evidence supports it.

Prerequisites before you start

• Active Google Ads and/or Meta Ads accounts with click IDs (GCLID, FBCLID) passing through to your landing pages.
• Ability to add a lightweight JavaScript snippet to the header of every landing page that receives paid traffic.
• Access to your CRM or lead database to match BotRefund session IDs with downstream outcomes (calls connected, demos booked, qualified opportunities).
• At least one full campaign cycle (typically 7–14 days) of stable spend so the baseline reflects your actual audience mix, not a test budget.

Step-by-step: Building your first baseline with BotRefund

1. Install the snippet. Paste the provided script into the <head> of every landing page that receives paid clicks. The script loads asynchronously and does not block page render.
2. Verify data flow. Open the BotRefund dashboard and confirm that sessions are appearing with click IDs, timestamps, and the full signal set (browser, network, device, behavior).
3. Run a minimum collection window. Let the script record at least 7 days of traffic across all placements, creatives, audiences, and devices. Do not pause campaigns or change targeting during this window.
4. Review the signal breakdown. In the dashboard, examine the distribution of each of the 106 checks — for example, scrollbar width leak, clean context iframe, ghost click detection, honeypot trap interactions, pointer behavior, motion behavior, speed behavior, path behavior, engagement behavior, and session behavior. Note the median and interquartile range for human sessions.
5. Cross-reference with CRM outcomes. Export the session list and join it to your lead data. Confirm that sessions flagged as human by the model correspond to contacts that become calls, demos, or qualified opportunities. Sessions that the model flags as bot should show disconnected numbers, invalid emails, or no downstream activity.
6. Lock the baseline. Once the human/bot separation aligns with CRM reality, save the current signal thresholds as your baseline. Future sessions will be scored against this profile.
7. Enable suppression and reporting. Turn on conversion-event suppression for sessions that fall outside the baseline, and generate refund-ready reports (click IDs, campaign details, timestamps, session recordings, signal-by-signal reasoning) formatted for Google and Meta review teams.

Key signals BotRefund uses to establish normal behavior

BotRefund does not rely on a single tell. It combines 110+ behavioral, browser, hardware, network, and attribution signals. The following are representative checks that feed the baseline:

• Scrollbar Width Leak — detects a mismatch between the reported scrollbar width and the browser's actual rendering context, which automated browsers often fail to replicate.
• Clean Context Iframe — checks whether standard browser APIs behave consistently when probed from an isolated iframe; automation tools that patch or hide APIs often break under this test.
• Ghost Click Detection — catches click activity that occurs without the natural sequence of human intent (e.g., no preceding hover, no focus change).
• Honeypot Trap Interactions — watches for bots that respond to hidden or intentionally deceptive page elements.
• Robotic Linear Mouse Movements — flags unnaturally straight pointer paths that rarely appear in real user sessions.
• Absence of Humanlike Mouse Tremor — looks for the tiny imperfections and jitter typical of human movement.
• Superhuman Input Speed (<1ms) — identifies interactions that happen faster than a person could realistically perform.
• Grid-Aligned Movement Patterns — detects movement that snaps to precise lines or blocks instead of natural curves.
• Absence of Clicks or Scrolling — highlights sessions that stay too static to match a real browsing journey.
• Unnatural Session Durations — catches visit lengths that are too short, too long, or too uniform to be human.

Each signal is kept as independent evidence — not a verdict — and cross-checked against the others before the AI model weighs the complete pattern.

Reading the baseline dashboard: what to look for

• Signal consistency. Human sessions show variation across signals; bot clusters often share identical anomalies (e.g., same scrollbar width, same iframe context, same pointer path).
• Placement-level splits. A sharp lead-quality difference by placement, creative, audience expansion, device, or landing page often reveals where invalid traffic concentrates.
• Timing anomalies. Several leads arriving in short bursts, forms submitted immediately after landing, or conversions concentrated at unusual hours.
• Contactability gaps. Disconnected numbers, invalid email domains, repeated addresses, or an unusual concentration of one country code.
• CRM outcome mismatch. A high reported lead count paired with no calls connected, demos booked, qualified opportunities, or repeat engagement.

Common mistakes when setting a baseline

1. Collecting during a campaign change. Pausing ads, swapping creatives, or adjusting audiences mid-collection pollutes the baseline with transitional traffic.
2. Ignoring CRM ground truth. The dashboard model is 99% accurate when session evidence supports it, but you must verify against actual sales outcomes — calls, demos, qualified opportunities — before locking thresholds.
3. Treating every anomaly as fraud. Privacy tools, corporate networks, travel, and unusual devices can produce unexpected behavior for genuine people. BotRefund keeps each signal as evidence, not a verdict; the baseline should reflect the cluster of corroborated anomalies.
4. Using too short a window. Less than 7 days often misses weekly seasonality (weekday vs. weekend behavior) and under-represents low-volume placements.
5. Forgetting attribution preservation. Before changing any campaign, keep campaign, ad set, creative, placement, and click identifiers intact so refund reports remain valid.

Verifying your baseline is accurate

After locking the baseline, run a one-week verification: compare the bot-flagged sessions in the dashboard with your CRM's disqualified leads. If the overlap is high (the FinTrust case study showed a 14% bot click rate and an 18% conversion-rate increase after suppression), the baseline is working. If you see many false positives — human sessions flagged as bot — review the signal breakdown for that segment and adjust the collection window or check for new device/browser combinations that were not represented in the original sample.

Limitations and when the baseline may shift

• New browser versions or privacy tools can change the distribution of signals like scrollbar width or iframe context; plan to re-baseline quarterly or after major browser releases.
• Campaign structure changes (new geos, new languages, new landing page layouts) introduce behavioral patterns the original baseline never saw.
• Seasonal traffic spikes (Black Friday, product launches) may temporarily alter session duration and scroll depth distributions; consider a separate seasonal baseline.
• BotRefund does not replace server-side fraud filters. It adds a marketing-layer evidence layer that preserves attribution and produces refund-ready reports; edge protection (CDN, WAF) serves a different job.
• Refund approval is not guaranteed. Across 2,500+ brands audited, 83% of clients recover funds from Google and Meta, but each platform's review team makes the final decision.

Key facts

Terminology

• Click ID (GCLID/FBCLID) — unique identifier appended by Google or Meta when a user clicks an ad; required to tie a session to a specific paid click for refund claims.
• Pixel poisoning — when bot conversions feed the ad platform's optimization algorithm, causing it to bid more for similar low-quality traffic.
• Invalid traffic (IVT) — Google and Meta's term for clicks or impressions not resulting from genuine user interest (automated tools, accidental clicks, competitor click fraud).
• Refund-ready report — evidence package formatted to the specifications Google and Meta reviewers expect, including session recordings and signal-by-signal reasoning.
• Suppression — preventing a conversion event from firing for sessions the model flags as bot, so the ad platform's algorithm trains only on verified human actions.

FAQ

How long does it take to build a reliable baseline?

Plan for 7–14 days of stable paid traffic across all campaigns. Shorter windows risk missing weekly seasonality and low-volume placements.

Do I need to change my landing pages or forms?

No. The script runs in the browser and observes behavior; it does not modify your page, add CAPTCHAs, or block visitors.

What if my traffic volume is low?

Low volume extends the collection window. You need enough human sessions to define the normal range for each signal — typically a few hundred verified human sessions per major placement/device combination.

Can I use BotRefund alongside Cloudflare or another WAF?

Yes. BotRefund operates at the marketing layer (onsite behavioral investigation, conversion-signal protection, refund-ready reporting) while edge providers handle DDoS, CDN, and WAF rules. They serve different jobs and can coexist.

What happens after I submit a refund report?

BotRefund formats the evidence, writes the claim, and supports the negotiation with Google and Meta reviewers. The platforms make the final credit decision; historically 83% of audited clients recover funds.

Does the baseline automatically update?

Not automatically. Re-baseline quarterly or after major changes (browser releases, new geos, landing page redesigns) to keep the reference profile current.

What if I see a sudden spike in bot-flagged sessions?

Check the signal breakdown for that spike — often a single placement, creative, or audience expansion is the source. You can pause that segment, suppress its conversions, and generate a targeted refund report for the affected click IDs.

Quick answer: what the check actually does

BotRefund treats an unusual concentration of a single country code in phone numbers or form submissions as one piece of evidence that a lead batch may be automated or low-quality. The platform does not run a standalone "country code tool" you click on demand. Instead, it continuously analyzes every session tied to your paid clicks, and the country code pattern shows up in the audit report if the signal crosses the platform's confidence threshold.

To see it, you add the BotRefund script to your landing pages, run traffic for a period (typically a few days to a week), then open the generated report. The report lists flagged sessions with click IDs, timestamps, and a signal-by-signal breakdown that includes contactability anomalies such as repeated country codes, disconnected numbers, or invalid email domains.

Why country code concentration matters for Meta lead campaigns

Meta campaigns can reach users across Facebook, Instagram, and partner inventory at high volume. That reach also brings accidental clicks, low-intent traffic, and deliberate form spam. A sudden cluster of leads sharing the same country code — especially when it does not match your targeting — often signals a botnet, click farm, or affiliate fraud ring submitting fabricated contacts.

BotRefund's blog notes that contactability signals like "an unusual concentration of one country code" help separate normal lead-quality variation from automated activity. The key is corroboration: a single anomaly is not a verdict, but when country code clustering appears alongside superhuman input speed, missing scroll behavior, or identical field structures, the combined pattern supports a high-confidence bot finding.

Prerequisites before you start

• Active Meta or Google Ads campaigns sending traffic to pages you control.
• Ability to add JavaScript to those landing pages (or via GTM).
• Access to CRM or lead export so you can later match flagged sessions to actual outcomes (calls connected, demos booked, etc.).
• Admin rights on the BotRefund account to install the script and download reports.

Step-by-step: from install to country code insight

1. Create a BotRefund account and choose the plan that covers your monthly ad spend tier.
2. Install the tracking script on every landing page that receives paid traffic. The script captures 110+ independent signals — browser, device, network, pointer, scroll, typing, and behavioral — without slowing the page.
3. Verify data collection in the dashboard. You should see live sessions with click IDs (GCLID, FBCLID) attached.
4. Run campaigns normally for at least 3–7 days to accumulate a representative sample. Do not pause or restructure campaigns during this window; preserving attribution is critical for any later refund claim.
5. Generate the audit report. BotRefund compiles a refund-ready PDF/CSV with session recordings, click IDs, campaign details, timestamps, and signal-by-signal reasoning.
6. Locate the contactability section. The report groups anomalies: disconnected numbers, invalid email domains, repeated addresses, and unusual concentration of one country code. Each flagged session shows the specific country code, frequency, and the other signals that co-occurred.
7. Cross-reference with CRM outcomes. Export the flagged click IDs and check whether those leads ever became qualified opportunities. A high reported lead count with zero calls connected or demos booked confirms the pattern is waste, not just noise.

How the signal fits into BotRefund's detection model

BotRefund does not rely on any single rule. It runs 106 independent checks (e.g., scrollbar width leak, clean context iframe, robotic mouse movements, superhuman input speed) and feeds every signal into an AI prediction model. The model weighs the complete pattern across browser, network, device, and behavior evidence. Country code concentration is one contactability signal among many; it gains weight only when other independent signals tell the same story.

The platform states its accuracy comes from corroboration, not one browser tell. A single anomaly is kept as evidence, not a verdict. Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior for genuine people, so cross-checking is mandatory before any refund request.

Key facts at a glance

Common mistakes to avoid

• Treating the country code flag as proof. It is evidence, not a verdict. Always check for co-occurring signals (timing bursts, no scroll, superhuman speed) before asking Meta for a refund.
• Changing targeting mid-audit. Preserve attribution first. The blog workflow explicitly says: keep campaign, ad set, creative, placement, and click identifiers intact before any changes.
• Ignoring CRM reality. A high lead count with zero qualified outcomes is the strongest validation. If the sales team actually closes deals from that country code, the signal may be a false positive.
• Expecting a live "country code dashboard." BotRefund surfaces the pattern in the periodic audit report, not in a real-time filter you can toggle.

Limitations and when this check does not apply

• Non-phone leads. If your forms only collect email, country code concentration is irrelevant; the system will flag invalid email domains instead.
• Legitimate international campaigns. Targeting multiple countries intentionally will show multiple country codes — that is expected, not suspicious.
• Low volume. With fewer than a few hundred leads, statistical clustering is unreliable. The signal needs enough data to distinguish pattern from chance.
• Offline conversions only. If you import offline conversions without the original click ID, BotRefund cannot tie the session to the paid click.

Terminology you will see in the report

• Click ID (GCLID/FBCLID) — the unique identifier Google or Meta attaches to each paid click; required for refund claims.
• Contactability signal — any data point that suggests the submitted contact info is fake or unreachable (country code cluster, disconnected number, invalid domain).
• Pixel poisoning — when bot conversions train Meta's or Google's optimization algorithms on junk data, degrading future targeting.
• Refund-ready report — a document formatted to the specifications Google and Meta reviewers expect, including session recordings and signal reasoning.
• Suppression — preventing flagged conversion events from firing back to the ad platform so the algorithm stops optimizing for bots.

Practical scenario: e-commerce lead gen

A retailer runs Meta lead ads for a financing offer in the US and Canada. After a week of BotRefund tracking, the report shows 22% of flagged sessions share a single non-targeted country code (+XX). Those same sessions also show superhuman input speed (<1 ms), no scroll events, and grid-aligned mouse paths. The CRM shows zero funded applications from that cluster. The retailer exports the click IDs, files a Meta invalid traffic claim with the BotRefund report, and suppresses the conversion pixel for future sessions matching the pattern.

FAQ

Does BotRefund automatically block traffic from suspicious country codes?

No. BotRefund is an evidence and reporting layer. It identifies the pattern and gives you the click IDs to suppress in your ad platform or to submit in a refund claim. You decide whether to exclude the audience, adjust targeting, or pursue a credit.

Can I see country code concentration in real time?

The dashboard shows live sessions, but the structured contactability analysis (including country code clustering) appears in the compiled audit report after enough data accumulates.

What if my legitimate customers use VoIP numbers with unusual country codes?

The AI model cross-checks the country code signal against 100+ other signals. A real user on VoIP will still show human scroll, hesitation, varied timing, and normal browser fingerprints. The cluster only weighs heavily when multiple independent signals align.

How long until the first report is ready?

Typically 3–7 days of live traffic, depending on volume. Higher spend campaigns generate a usable sample faster.

Does this work for Google Ads call extensions?

Yes. BotRefund tracks the landing page session after the click. If the call extension drives a page visit, the same contactability signals apply. Pure call-only extensions without a page visit are outside the onsite script's scope.

What does the report cost?

BotRefund offers a free bot audit to start. Paid tiers scale with monthly ad spend; the enterprise tier covers budgets over $10,000/mo. Exact pricing is on the website.

Can I use the report for a chargeback with my payment processor?

The report is formatted for Google and Meta invalid traffic review teams. Payment processors have different evidence standards; check with your processor before relying on it for a chargeback.

BotRefund flags fast form completion by measuring input speed, session timing, and behavioral patterns that humans cannot replicate. The system records every keystroke, click, and pointer movement, then compares the complete pattern against 110+ independent signals to reach 99% confidence before generating a refund-ready report.

What fast form completion signals indicate

Fast form completion is not a single metric. BotRefund treats it as a cluster of timing and behavior anomalies that appear together when a script or automated browser fills and submits a form. The most direct signal is superhuman input speed — interactions measured in under one millisecond — which a person cannot physically perform. Other signals include forms submitted immediately after the landing page loads, zero scrolling or field corrections, uniform click paths, and session durations that are too short, too long, or too uniform to be human.

These signals appear in the source pack as "Timing: several leads arriving in short bursts, forms submitted immediately after landing, or conversions concentrated at unusual hours" and "Speed behavior: Superhuman input speed (<1ms) Identifies interactions that happen faster than a person could realistically perform." When several of these appear in the same session, the AI model weighs the complete pattern instead of trusting any single rule.

How BotRefund detects fast form completion

BotRefund runs client-side checks in the visitor's browser. Each check produces an independent piece of evidence — for example, the Scrollbar Width Leak check looks for a mismatch that a real browsing session does not normally create, and the Clean Context Iframe check detects automation tools that patch or hide browser APIs. The speed behavior check specifically measures input timing down to sub-millisecond precision. All 110+ signals feed into a prediction AI that evaluates browser, network, device, and behavior evidence together. As the documentation states, "BotRefund sends this signal into our prediction AI, which evaluates the complete picture across browser, network, device, and behavior evidence. By seeing how all signals fit together, it identifies a visit as bot or human with 99% accuracy."

A single anomaly is never a verdict. Privacy tools, corporate networks, and unusual devices can produce unexpected behavior for genuine people. BotRefund keeps each signal as evidence and cross-checks it against independent data before scoring the session.

Setting up BotRefund for form completion monitoring

1. Add the BotRefund script to your landing pages. The script loads asynchronously and begins collecting behavioral, browser, hardware, network, and attribution signals immediately.
2. Ensure your Meta Pixel or Google Ads conversion tracking fires on the same pages. BotRefund protects selected conversion signals so the ad platforms only train on verified human interactions.
3. Let the system run for a representative traffic period — typically one to two weeks — so the AI model sees enough human and automated sessions to calibrate.
4. Open the dashboard and filter for sessions flagged with speed behavior, timing anomalies, or engagement behavior (absence of clicks or scrolling). Each flagged session includes a session recording, click ID, campaign details, and timestamp.

Interpreting the detection signals

The dashboard groups signals into categories: click behavior (ghost click detection), trap behavior (honeypot interactions), pointer behavior (robotic linear mouse movements), motion behavior (absence of humanlike mouse tremor), speed behavior (superhuman input speed), path behavior (grid-aligned movement patterns), engagement behavior (absence of clicks or scrolling), and session behavior (unnatural session durations). For fast form completion, focus on speed behavior combined with engagement behavior and session behavior. A session that shows <1ms input speed, zero scrolling, and a session duration of two seconds is a high-confidence bot session. A session with fast input but natural mouse tremor and reading pauses may be a power user or autofill — the AI weighs the full pattern.

Using evidence for refund claims

BotRefund turns each finding into a refund-ready report with click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning. The evidence is structured in the format platform teams use to review invalid traffic claims. According to the homepage, "We turn each finding into a refund-ready report with click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning. The evidence is structured in the format platform teams use to review invalid traffic claims." Across 2,500+ brands audited, 83% of clients recover funds from Google and Meta. The high approval rate comes from three things: 99% bot-detection confidence, reports built in a format their teams can review, and deep experience negotiating successful claims.

To file a claim, export the report for the date range and campaigns in question. Submit it through Google Ads' invalid activity credit request or Meta's ad quality reporting flow. BotRefund's team can also support the negotiation with documentation and arguments the reviewers need.

Limitations and when to investigate further

Fast form completion alone does not prove fraud. Legitimate users with autofill, password managers, or accessibility tools can complete forms quickly. BotRefund's cross-checked context prevents false positives by requiring corroboration across independent signals. If a session shows fast input but passes all other checks — normal mouse movement, realistic session duration, varied scroll behavior — it remains unflagged. The system also preserves attribution before you change campaigns, so you can compare lead quality by placement, creative, audience expansion, device, or landing page without losing the click ID trail.

Not every bad lead is a bot. A weak campaign can attract real people who are not ready to buy. Treating every unresponsive contact as fraud can make a team exclude a valuable audience. Start with a structured audit that compares ad-platform data, website sessions, and CRM outcomes before changing targeting or making a refund request.

Key facts

Frequently asked questions

How quickly does BotRefund start detecting fast form completions?

The script begins collecting signals on the first page load. Meaningful pattern recognition typically requires one to two weeks of traffic so the AI model sees enough human and automated sessions to calibrate.

Can BotRefund distinguish autofill from bot automation?

Yes. Autofill produces fast input but retains humanlike mouse tremor, natural scroll behavior, and varied session duration. The AI weighs the complete pattern across 110+ signals rather than relying on speed alone.

What happens if a legitimate user is flagged?

BotRefund keeps each signal as evidence, not a verdict. A single anomaly never triggers a bot classification. The cross-checked context requires corroboration from independent browser, network, device, and behavior data before scoring a session as automated.

Do I need to change my forms or add honeypot fields?

No. BotRefund's client-side checks run on your existing forms. The trap behavior check watches for bots that respond to hidden or intentionally deceptive page elements, but you do not need to add them yourself.

How do I connect a flagged session to a specific ad click?

Each flagged session includes the click ID (GCLID for Google, fbclid for Meta), campaign, ad set, creative, placement, and timestamp. The report preserves attribution so you can trace the invalid click back to the exact paid interaction.

What if Google or Meta rejects the refund claim?

BotRefund's team supports negotiations with documentation and arguments the reviewers need. The 83% recovery rate across 2,500+ audits reflects experience with the evidence format both platforms accept.

Does BotRefund work on single-page applications or React forms?

The script captures behavioral signals regardless of framework. It measures browser-level interactions — pointer movement, scroll, input timing, and API consistency — which are framework-agnostic.

Quick answer: what uniform click paths mean and how BotRefund surfaces them

Uniform click paths are sequences of clicks that follow the same exact coordinates, timing, or element order across multiple sessions. Real users hesitate, scroll, correct typos, and vary their routes. Bots often replay a recorded script or drive a headless browser that clicks the same buttons in the same order every time. BotRefund flags this pattern under its Path behavior signal set — specifically grid-aligned movement patterns and robotic linear mouse movements — and shows the evidence in a session-level report that includes click IDs, timestamps, and a visual replay.

To check for uniform click paths with BotRefund: (1) add the BotRefund script to your landing pages, (2) let traffic run for a meaningful volume (typically a few thousand visits), (3) open the dashboard or download the refund-ready report, (4) filter or search for the Path behavior category, and (5) review the flagged sessions where click paths are grid-aligned, linear, or identical across visits. Each finding includes a session-by-session explanation rather than a generic invalid-traffic estimate.

Why uniform click paths matter for paid campaigns

When a lead campaign reports steady cost-per-lead but the sales team sees unreachable contacts, copied messages, or enquiries that never progress, the problem often looks like a campaign-performance issue before it looks like fraud. Uniform click paths are a repeatable technical pattern that separates automated submissions from real people who simply aren't ready to buy. Treating every unresponsive contact as fraud can make a team exclude a valuable audience; starting with a structured audit that compares ad-platform data, website sessions, and CRM outcomes prevents that mistake.

BotRefund's investigation workflow begins by preserving attribution before changing the campaign — keeping campaign, ad set, creative, placement, and click identifiers intact — so the evidence stays tied to the exact paid click that brought the visitor.

Prerequisites before you start

• Active Google or Meta campaigns sending traffic to pages you control.
• Ability to add JavaScript to the landing page or tag manager (the BotRefund script loads asynchronously and does not block page render).
• Access to click IDs (GCLID for Google, fbclid for Meta) so findings can be mapped back to the ad platform for refund claims.
• Conversion events firing on the same domain so BotRefund can suppress bot conversions in real time and protect pixel training data.

Step-by-step: set up BotRefund and review uniform click path evidence

1. Create a BotRefund account and choose the plan that matches your monthly ad spend (the free audit tier covers the detection layer; paid tiers add refund-ready reports and negotiation support).
2. Install the tracking script on every landing page that receives paid traffic. The script captures 110+ independent signals across browser, network, device, and behavior layers.
3. Verify data collection in the dashboard. Within minutes you should see live sessions with signal breakdowns: click behavior, trap behavior, pointer behavior, motion behavior, speed behavior, path behavior, engagement behavior, and session behavior.
4. Let traffic accumulate until you have a representative sample — typically a few thousand visits or at least one full campaign cycle.
5. Open the Path behavior section. Look for two specific flags: Grid-aligned movement patterns (movement that snaps to precise lines or blocks instead of natural curves) and Robotic linear mouse movements (unnaturally straight pointer paths that rarely appear in real user sessions).
6. Drill into flagged sessions. Each session shows a visual replay, click coordinates, timestamps, and the exact signal-by-signal reasoning. Uniform click paths will appear as identical or near-identical click sequences across multiple sessions.
7. Export the refund-ready report (paid tiers). The report includes click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning formatted for Google and Meta review teams.

How BotRefund evaluates uniform click paths alongside other signals

A single anomaly is not a bot verdict. Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior for genuine people. BotRefund keeps the uniform click path signal as evidence — not a verdict — and cross-checks it against independent browser, network, device, and behavior data. The prediction AI weighs the complete pattern instead of trusting a raw rule, which is how the system reaches 99% confidence when the session evidence supports it.

Other session-behavior signals that often appear alongside uniform click paths include:

• No scrolling — the visitor never moves down the page.
• No field corrections — forms are filled in one pass without backspaces or edits.
• No meaningful time on the offer page — dwell time is too short, too long, or too uniform to be human.
• Superhuman input speed — interactions faster than 1 millisecond.
• Absence of humanlike mouse tremor — missing the tiny imperfections and jitter typical of human movement.
• Honeypot trap interactions — bots responding to hidden or intentionally deceptive page elements.

Key facts: BotRefund detection and refund capabilities

Common mistakes when investigating click-path anomalies

Practical scenarios: when to check for uniform click paths

Scenario 1: Lead quality drops but cost-per-lead looks normal

Ads Manager reports steady CPL. Sales reports disconnected numbers, invalid email domains, repeated addresses, or unusual country-code concentration. Open BotRefund, filter by the affected campaign and placement, and check Path behavior for grid-aligned or linear click patterns. If uniform click paths appear across multiple sessions from the same placement, you have evidence to suppress those conversions and file a refund claim.

Scenario 2: Sudden placement-level spike in conversions

A new placement or audience expansion delivers a burst of leads. Several arrive in short bursts, forms submit immediately after landing, or conversions concentrate at unusual hours. Review the Path behavior and Speed behavior signals together. Uniform click paths combined with superhuman input speed (<1ms) strongly indicate automated form submission.

Scenario 3: CRM shows high lead count but zero qualified opportunities

Reported leads are high; calls connected, demos booked, and repeat engagement are near zero. Pull the BotRefund report for the campaign date range. Look for sessions with no scrolling, no field corrections, uniform click paths, and no meaningful time on the offer page. This cluster supports a high-confidence bot verdict and a refund request.

Limitations and when this advice does not apply

• Non-paid traffic: BotRefund is built for paid-click investigation. Organic, direct, or referral traffic without click IDs cannot be mapped to ad-platform refund processes.
• Single-page apps with heavy client-side routing: Ensure the script fires on each virtual page view; otherwise session stitching may split a single visit.
• Very low traffic volumes: Statistical confidence improves with volume. A few dozen visits may not produce a reliable pattern.
• Campaigns already paused: BotRefund preserves evidence after a campaign is paused, but you must have installed the script before the traffic occurred.
• Platforms beyond Google and Meta: Refund-ready reports and negotiation experience are specific to Google Ads and Meta Ads. Other platforms may accept the evidence but have different claim processes.

Terminology quick reference

• Click ID (GCLID / fbclid): Unique identifier appended to the landing-page URL by Google or Meta when a user clicks an ad. Required to tie a session to a specific paid click for refund claims.
• Pixel poisoning: When bot conversions train the ad platform's optimization algorithm to find more bots, raising CAC and lowering ROAS.
• Refund-ready report: A document formatted to the specifications Google and Meta reviewers use, containing click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning.
• Path behavior: BotRefund signal category covering grid-aligned movement patterns and robotic linear mouse movements.
• Cross-checked context: BotRefund's method of testing whether multiple independent signals support the same bot/human conclusion before scoring.

Frequently asked questions

How long does it take to see uniform click path data after installing BotRefund?

Live sessions appear within minutes. A reliable pattern typically emerges after a few thousand visits or one full campaign cycle, depending on traffic volume.

Can I check for uniform click paths on historical traffic?

No. BotRefund collects signals in real time. You must have the script installed before the traffic arrives. Evidence is preserved after a campaign is paused, but not retroactively created.

Does a uniform click path flag automatically mean the visit is a bot?

No. BotRefund treats it as evidence, not a verdict. The prediction AI cross-checks it against browser, network, device, and other behavior signals. Only when the complete pattern supports it does the system score the visit as bot with 99% confidence.

What if my team doesn't have developer resources to install the script?

The script loads asynchronously via a single line in your tag manager or page template. Most marketing teams can deploy it without engineering help. BotRefund support can review the implementation if needed.

How does BotRefund's uniform click path detection differ from Google's or Meta's built-in filters?

Platform filters operate at the server level (IP reputation, click frequency, known data-center ranges). BotRefund adds client-side behavioral evidence — pointer paths, scroll behavior, timing, browser consistency — that server logs cannot see. This evidence is formatted for platform review teams to supplement their automated decisions.

What does the refund-ready report include for uniform click path findings?

Each flagged session shows the visual replay, click coordinates, timestamps, the specific Path behavior signals triggered (grid-aligned movement, robotic linear mouse), and the signal-by-signal reasoning. The report bundles these sessions with click IDs and campaign metadata in the format Google and Meta accept.

Can I use BotRefund data to block bots in real time, not just claim refunds?

Yes. Paid tiers include real-time conversion suppression: when BotRefund detects automated browser emulation signals (including uniform click paths), it can prevent the conversion event from firing to your Meta Pixel or Google Ads conversion tag, protecting the optimization algorithm from pixel poisoning.

Verification step: confirm the evidence before acting

Before changing targeting, filing a refund claim, or suppressing a placement, open three flagged sessions side by side. Verify that the click paths are genuinely identical or grid-aligned, that the sessions also show at least two other corroborating signals (e.g., no scrolling + superhuman speed), and that the click IDs match the campaign and placement you intend to challenge. This three-point check — pattern consistency, multi-signal corroboration, attribution integrity — is the practical threshold for a high-confidence decision.

Direct answer: join BotRefund session evidence to CRM records by click ID

Export BotRefund's refund-ready report — it includes click IDs (GCLID, FBCLID), campaign hierarchy, placement, device, timestamp, and a signal-by-signal bot verdict for each session. In your CRM, tag each lead with the same click ID captured at form submission. Then pivot: for each campaign, ad set, creative, and placement, compare BotRefund's bot-rate and confidence score against CRM outcomes — calls connected, demos booked, opportunities created, and repeat engagement. A high BotRefund bot-rate paired with zero CRM progression signals invalid traffic that Meta's own filters missed.

Why this comparison matters

Meta Ads Manager may report a steady cost per lead while the sales team receives unreachable contacts, copied messages, or enquiries that never progress. The platform's automated systems catch some invalid activity, but they operate at the server level — IP reputation, click velocity, known data-center ranges. They do not see the browser behavior that BotRefund captures: scroll depth, mouse tremor, input speed, honeypot interactions, and 100+ other client-side signals. When CRM outcomes diverge from platform-reported leads, the gap is often automated traffic that looks human to server logs but fails behavioral checks.

Treating every unresponsive contact as fraud can make a team exclude a valuable audience. A structured audit that compares ad-platform data, website sessions, and CRM outcomes before changing targeting or making a refund request prevents over-correction.

Prerequisites before you start

• Click-ID capture on your forms. Your landing page must read the GCLID (Google) or FBCLID (Meta) from the URL query string and write it into a hidden form field that flows into the CRM lead record.
• BotRefund script installed site-wide. The JavaScript snippet must load on every landing page and thank-you page so it observes the full session from click to conversion.
• CRM export capability. You need to pull a lead list that includes: click ID, campaign/ ad set/ creative/ placement (from UTM or platform macros), lead creation timestamp, and outcome fields (call connected, demo booked, opportunity stage, disqualification reason).
• Time-window alignment. Pull BotRefund and CRM data for the same date range — ideally 14 to 30 days to smooth daily variance.

Step-by-step investigation workflow

1. Preserve attribution before changing the campaign. Keep campaign, ad set, creative, placement, and click identifier intact in both BotRefund and CRM exports. Do not pause ads or rename UTM structures until the baseline comparison is complete.
2. Export BotRefund session verdicts. From the BotRefund dashboard, download the session-level report for the chosen window. Each row contains: click ID, timestamp, campaign hierarchy, placement, device, bot confidence (0–99%), top contributing signals (e.g., superhuman input speed, grid-aligned movement, honeypot trigger), and a session replay link.
3. Export CRM leads with outcomes. Pull all leads created in the same window. Ensure each row has the click ID, the same campaign hierarchy, and a clean outcome status: connected, demo booked, qualified opportunity, disqualified — bad contact, disqualified — no response, disqualified — duplicate.
4. Join on click ID. Use a spreadsheet, BI tool, or SQL to left-join BotRefund sessions to CRM leads on click ID. Sessions without a CRM match are clicks that didn't convert; leads without a BotRefund match are form submissions the script missed (check script coverage).
5. Pivot by placement, creative, and audience expansion. For each segment, calculate: BotRefund bot-rate (sessions flagged ≥90% confidence / total sessions), CRM connect-rate (connected / leads), and CRM qualification-rate (qualified opportunities / leads).
6. Flag discrepancies. Segments where BotRefund bot-rate >20% and CRM connect-rate <10% are prime candidates for refund claims and suppression lists.
7. Build the refund-ready report. BotRefund's export already formats evidence in the structure Google and Meta reviewers expect: click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning. Attach the CRM outcome pivot as supporting context.

Key signals to correlate with CRM outcomes

Source: BotRefund's Meta Ads Invalid Traffic guide lists these five signal categories as the primary investigation vectors.

Common patterns and what they mean

Pattern A: High bot-rate on Audience Network, zero CRM progression

BotRefund flags 35% of Audience Network sessions as ≥90% confidence bots. CRM shows 200 leads, 0 calls connected, 0 demos. Action: exclude Audience Network from the campaign; file refund claim for the flagged click IDs.

Pattern B: Low bot-rate but high "no response" disqualifications

BotRefund shows 5% bot-rate. CRM shows 40% "disqualified — no response" with valid phone numbers. Action: this is likely low-intent human traffic, not bot fraud. Improve lead magnet or follow-up speed instead of filing refunds.

Pattern C: Creative-specific bot spike

One creative drives 60% bot-rate; others are under 8%. CRM mirrors: that creative's leads never progress. Action: pause the creative; the botnet is targeting its specific landing page or offer.

Pattern D: Device-type divergence

Mobile web shows 22% bot-rate; desktop shows 3%. CRM connect-rate: mobile 8%, desktop 34%. Action: bid-adjust mobile down or apply a mobile-specific suppression list built from BotRefund click IDs.

Limitations and when this comparison does not apply

• No click-ID capture = no join. If your forms don't persist GCLID/FBCLID, you cannot link BotRefund sessions to CRM records at the individual level. You can still compare aggregate trends by day and campaign, but you lose the per-lead verdict.
• Offline conversions imported via API. If you push offline conversion events (e.g., "qualified opportunity") back to Meta via Conversions API, the platform's own optimization may already down-weight bot-heavy segments. The comparison still validates whether the down-weighting is sufficient.
• Long sales cycles. For B2B with 90-day cycles, CRM outcomes lag. Use leading indicators (call connected, demo booked) rather than closed-won for the initial comparison.
• Privacy tools and corporate networks. VPNs, enterprise browsers, and privacy extensions can trigger behavioral anomalies for real users. BotRefund treats each signal as evidence, not a verdict, and cross-checks across 106 independent checks before assigning 99% confidence. Still, review false-positive rates on a sample of session replays before suppressing entire segments.
• Script not firing on thank-you page. If the BotRefund snippet misses the conversion event, the session won't have a bot verdict. Verify coverage by comparing BotRefund session count to analytics form-submit count.

Verification step: spot-check session replays

Before filing a refund claim or building a suppression list, open 10–20 session replays from the flagged segment. Confirm the behavior matches the signal cluster: no scroll, linear mouse path, instant form fill, honeypot trigger. This manual check catches edge cases where a real user's assistive technology or corporate proxy mimics bot patterns. BotRefund's 99% confidence claim is built on corroboration across browser, network, device, and behavior signals — but a human spot-check remains the final guardrail.

Key facts

FAQ

Do I need developer resources to set up the click-ID join?

Yes, minimally. A developer must add hidden fields to your forms that capture GCLID/FBCLID from the URL and pass them to the CRM. Most marketing teams can implement this in a few hours using Google Tag Manager or a form-builder webhook.

Can I compare BotRefund data to CRM outcomes without a full CRM export?

You can start with a CSV export from your CRM's lead list view. The join requires only click ID, campaign hierarchy, and outcome status — no API integration needed for the initial audit.

What if BotRefund flags a session as bot but the CRM shows a real customer?

Review the session replay. Privacy tools, corporate proxies, and accessibility software can produce false positives. BotRefund's 99% confidence is a statistical claim across large volumes; individual sessions can be edge cases. Use the replay to decide whether to include that click ID in a refund claim.

How often should I run this comparison?

Monthly for active campaigns; weekly during new creative tests or audience expansions. Bot traffic patterns shift when fraudsters adapt to new placements or creative formats.

Does BotRefund automatically suppress bot traffic in Meta Ads Manager?

No. BotRefund provides the evidence and click IDs; your team (or BotRefund's managed service) builds the suppression list and uploads it to Meta's Custom Audiences or uses the click IDs in a refund claim. The platform does not ingest third-party suppression lists automatically.

What's the minimum spend threshold for this to be worthwhile?

There's no fixed threshold, but the economics favor accounts spending >$5,000/month on Meta or Google where a 10–20% bot rate represents recoverable waste. Smaller accounts can still use the free audit to baseline their traffic quality.

Can I use this workflow for Google Ads as well as Meta?

Yes. The same click-ID join works with GCLID. BotRefund's Google Ads invalid-activity guide covers the Google-specific refund process, which also accepts the same evidence format.

If you're looking for a button inside BotRefund that sends a message straight to Meta, it doesn't exist. BotRefund is an evidence layer, not a support ticketing system. What it does is automate the hard part: proving that a chunk of your Meta spend went to bots, scrapers, or accidental clicks, and packaging that proof in the exact format Meta's refund reviewers expect.

What BotRefund actually does for Meta campaigns

BotRefund installs a lightweight script on your landing pages. As visitors arrive from Meta ads, it collects over 100 behavioral, browser, hardware, network, and attribution signals — things like mouse tremor, scroll depth, click timing, scrollbar width leaks, and iframe context consistency. Each visit gets a session-level verdict with a plain-English explanation. When the system flags a session as automated with 99% confidence, it captures the Meta click ID (fbclid), campaign hierarchy, placement, timestamp, and a session replay.

Those flagged sessions are then assembled into a refund-ready report. The report includes:

• Click IDs and campaign/ad set/ad identifiers for every suspicious session
• Signal-by-signal reasoning (e.g., "superhuman input speed <1ms", "grid-aligned mouse movement", "absence of humanlike mouse tremor")
• Session recordings you can watch
• A summary table Meta's review team can scan in minutes

You download that report and attach it when you open an invalid-traffic case in Meta's Business Help Center or reply to a billing dispute. BotRefund's team can also review your draft claim, suggest wording, and help you respond if Meta asks for clarification — but the actual submission clicks are yours.

Step-by-step: from install to Meta claim

1. Add the BotRefund script to every landing page that receives Meta traffic. Use Google Tag Manager, a header include, or the platform-specific snippet. The script loads asynchronously and does not affect page speed scores.
2. Verify data flow in the BotRefund dashboard. Within a few hours you'll see live sessions, each tagged with the fbclid, campaign, placement, and device. Confirm that click IDs are populating — if they're missing, check that your Meta ads use the "Add UTM parameters" or "Add click ID" setting.
3. Run a free bot audit (available from the dashboard). This gives you a baseline: percentage of bot traffic by campaign, placement, and device, plus a sample of flagged sessions with recordings.
4. Let the audit run for 7–14 days across a full weekly cycle. BotRefund's AI needs enough volume to separate real anomalies from privacy tools, corporate proxies, or unusual but human devices.
5. Generate the refund-ready report. In the dashboard, select the date range, filter to "High confidence (99%)" sessions, and click "Export for Meta claim." The download is a PDF + CSV bundle formatted to Meta's evidence guidelines.
6. Open a Meta invalid-traffic case. In Meta Business Help Center → Billing → Advertising charges → Dispute a charge → Invalid traffic. Attach the BotRefund PDF and CSV. In the description field, reference the report's case ID and note: "Evidence collected via client-side behavioral audit (100+ signals, 99% confidence threshold). Click IDs and session replays included."
7. Track the case. Meta typically responds in 5–10 business days. If they request more detail, BotRefund's support team can help you pull additional session replays or signal breakdowns for the specific click IDs Meta questions.

What the evidence package contains (and why Meta accepts it)

Meta's refund reviewers look for three things: a clear link between the click ID and the suspicious session, behavioral proof that the session was non-human, and a volume threshold that makes the refund worthwhile. BotRefund's report is built around those requirements.

The key distinction: Meta's own automated filters catch data-center IPs, rapid-click patterns, and known bad actors at the server level. They miss bots that run on residential IPs, mimic human timing, and execute JavaScript. BotRefund's client-side signals catch that second layer — and the report format mirrors the evidence checklist Meta's own traffic-quality team uses internally.

Where BotRefund's role ends and yours begins

• BotRefund does: collect evidence, score sessions, build the report, advise on claim wording, help you respond to Meta follow-ups.
• BotRefund does not: log into your Meta account, click "Submit" on the dispute form, guarantee a refund, or negotiate by phone with Meta reps (Meta does not offer phone negotiation for invalid-traffic claims).
• You do: attach the report, hit submit, monitor the case ID, and forward any Meta requests for clarification to BotRefund if you want help drafting the reply.

This division exists because Meta's policy requires the account holder (or an authorized admin on the Business Manager) to file the claim. No third party can submit on your behalf.

Common mistakes that delay or kill a Meta refund claim

How long it takes and what success looks like

Across 2,500+ audits, 83% of BotRefund clients recover funds from Google and Meta. The typical timeline:

• Days 1–3: Script live, data flowing
• Days 3–14: Audit window (longer if daily spend is under $500)
• Day 15: Report generated, claim filed
• Days 20–30: Meta responds (credit issued, partial credit, or request for more info)
• Days 30–45: If partial, one round of back-and-forth with supplemental sessions

Credits appear as a line item in your Meta billing summary ("Invalid activity credit"). They offset future spend; they are not wired to your bank account.

Key facts

When BotRefund won't help (limitations)

• Spend too low: If monthly Meta spend is under $1,000, the audit may not collect enough flagged sessions to clear Meta's minimum-volume threshold for a manual review.
• No landing page control: If you send traffic to a third-party form (Typeform, Google Form, Meta Instant Form) where you cannot install the script, BotRefund cannot see the session.
• Lead-quality issues that aren't bots: Real humans who fill forms with fake data, competitors manually clicking, or low-intent users from broad targeting — these are not invalid traffic under Meta's policy and won't generate a refund.
• Historical claims only: BotRefund cannot retroactively audit past months. It only sees traffic after the script is installed.
• Meta policy changes: Meta's invalid-traffic definitions and refund thresholds can change. BotRefund updates its report format accordingly, but past success does not guarantee future approval.

Terminology quick reference

• fbclid: Facebook Click Identifier — the unique query parameter Meta appends to your landing page URL (e.g., ?fbclid=IwAR123...). Required to link a session to a specific charged click.
• Invalid traffic (Meta definition): Clicks or impressions from automated tools, bots, accidental taps, known data-center IPs, or competitor click fraud. Not the same as "low-quality leads."
• Pixel poisoning: When bot conversions fire your Meta Pixel, corrupting the optimization model so it bids more for bot-like audiences.
• Refund-ready report: A packaged evidence bundle (PDF + CSV + replays) structured to match Meta's internal review checklist.
• 99% confidence: BotRefund's AI verdict threshold. Sessions below this are labeled "suspicious" but excluded from the refund claim to avoid false positives.

FAQ

Does BotRefund file the Meta claim for me?

No. Meta requires the Business Manager admin to submit the dispute. BotRefund provides the evidence package and can review your draft submission before you hit send.

What if Meta rejects the claim?

BotRefund's team will review the rejection reason, pull additional session replays or signal breakdowns for the specific click IDs Meta questioned, and help you file a one-time appeal with supplemental evidence.

How much does BotRefund cost?

Pricing is tiered by monthly ad spend. The entry tier covers up to $10,000/mo across Google and Meta. Enterprise plans add dedicated support, custom signal rules, and API access. A free bot audit is available before any commitment.

Can I use BotRefund evidence for a Google Ads claim at the same time?

Yes. The same script captures gclid (Google Click Identifier) alongside fbclid. The dashboard lets you generate separate refund-ready reports for each platform.

Will the script slow down my landing pages?

The script loads asynchronously, is under 30 KB gzipped, and has no measurable impact on Core Web Vitals in standard tests.

What if I use a headless CMS or single-page app?

BotRefund works with React, Vue, Next.js, and similar frameworks. The script initializes on each route change and captures the fbclid from the URL or data layer.

Do I need to keep the script running after I get the refund?

Recommended. Bot traffic patterns shift when campaigns change, new placements launch, or Meta rolls out new ad formats. Continuous monitoring protects future spend and keeps your Pixel clean.

Next step: run the free audit

Before you spend time on a Meta claim, know whether you have a case. Install the script, let it run for a week, and review the free audit. If the bot rate is under 2%, a claim likely won't clear Meta's threshold. If it's 5–20% (common on Audience Network and expanded audiences), the report will be strong enough to file.

Pixel poisoning happens when bots click your Meta ads, land on your site, and trigger conversion events — form submissions, button clicks, or page views — that feed false signals back to Meta's optimization engine. The result: your campaigns optimize for traffic that never converts, your cost per lead rises, and your sales team chases ghost contacts.

BotRefund detects pixel poisoning by installing a lightweight script on your landing pages. That script runs 110+ independent behavioral, browser, hardware, and network checks on every session. Each check produces a single piece of evidence — not a verdict. The system then cross-checks all signals against each other and feeds the complete pattern into an AI model that classifies the visit as human or bot with 99% confidence. When bot traffic is confirmed, BotRefund compiles a refund-ready report with click IDs, timestamps, session recordings, and signal-by-signal reasoning formatted for Meta's review teams.

What Pixel Poisoning Actually Is

Pixel poisoning is the corruption of your Meta Pixel's conversion data by non-human traffic. When bots trigger conversion events — lead forms, purchases, add-to-carts — the Pixel records them as real conversions. Meta's delivery system then optimizes toward the audiences, placements, and creatives that produced those poisoned events. You pay for more of the same junk traffic, and your reported cost per lead looks deceptively healthy while actual sales outcomes flatline.

Source S4 explains that bots "load pages but do not read, scroll, or convert" yet still fire conversion pixels, which "raises your customer acquisition costs (CAC) and lowers your campaign ROAS." Source S8 adds that fake leads are "a major drain on sales team resources, ad budgets, and optimization algorithms."

How BotRefund's Detection Works

BotRefund uses client-side auditing — code that runs in the visitor's browser — rather than relying solely on server logs. Server-side audits only see IP addresses, headers, and user agents, which advanced botnets spoof easily. Client-side checks observe actual behavior: mouse movement, scroll patterns, typing rhythm, browser API consistency, and hardware signals.

Source S2 lists the signal categories: "click behavior, trap behavior, pointer behavior, motion behavior, speed behavior, path behavior, engagement behavior, session behavior." Each category contains multiple specific checks. For example, the Scrollbar Width Leak check (Source S3) detects a mismatch between reported and actual scrollbar dimensions that automation tools struggle to replicate. The Clean Context Iframe check (Source S5) spots when automation frameworks patch browser APIs but fail to hide those patches from a cross-origin iframe probe.

No single signal proves a bot. Source S3 states: "A single anomaly is not a bot verdict. Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior for genuine people." BotRefund keeps each signal as evidence, cross-checks it against independent browser, network, device, and behavior data, then weighs the complete pattern with an AI predictor.

Step-by-Step: Using BotRefund to Detect Pixel Poisoning

1. Install the BotRefund script on every landing page that receives Meta ad traffic. The script loads asynchronously and does not block page rendering.
2. Verify pixel firing in BotRefund's dashboard. Confirm your Meta Pixel events (Lead, Purchase, CompleteRegistration, etc.) are being captured alongside BotRefund's session data.
3. Run a baseline audit. Let traffic accumulate for 7–14 days. BotRefund will classify each session and flag those with high bot probability.
4. Review flagged sessions. Each flagged session shows: click ID (fbclid), campaign/ad set/ad, timestamp, session recording, and the specific signals that triggered the classification (e.g., "superhuman input speed <1ms," "grid-aligned mouse movement," "absence of humanlike mouse tremor").
5. Correlate with CRM outcomes. Export the flagged click IDs and match them against your CRM. Look for the patterns Source S1 describes: "disconnected numbers, invalid email domains, repeated addresses," "several leads arriving in short bursts," "forms submitted immediately after landing," and "high reported lead count paired with no calls connected, demos booked, or qualified opportunities."
6. Generate a refund-ready report. BotRefund compiles the evidence into the format Meta's review teams expect: click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning. Source S2 confirms: "We turn each finding into a refund-ready report with click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning. The evidence is structured in the format platform teams use to review invalid traffic claims."
7. Submit the claim to Meta. Use the report to file an invalid traffic refund request. BotRefund's team can assist with the negotiation; Source S2 notes they have "worked through more than 2,500 audits and know how to present bot evidence to Google and Meta."

Key Signals That Indicate Pixel Poisoning

Source S1 lists additional investigation signals: "Contactability: disconnected numbers, invalid email domains, repeated addresses, or an unusual concentration of one country code. Timing: several leads arriving in short bursts, forms submitted immediately after landing, or conversions concentrated at unusual hours. Campaign patterns: a sharp lead-quality difference by placement, creative, audience expansion, device, or landing page."

From Detection to Refund: The Evidence Chain

Detecting pixel poisoning is only half the job. To recover budget, you need evidence Meta will accept. BotRefund structures each finding as a session-level case file:

• Click ID (fbclid/gclid) — ties the session to a specific paid click
• Campaign hierarchy — campaign, ad set, ad, placement, creative
• Timestamp — exact moment of each conversion event
• Session recording — visual replay of mouse, scroll, and keyboard activity
• Signal breakdown — each of the 110+ checks with pass/fail and raw values
• AI confidence score — 99% threshold for inclusion in refund reports

Source S2 emphasizes: "Reports in the format Google and Meta accept. We turn each finding into a refund-ready report with click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning. The evidence is structured in the format platform teams use to review invalid traffic claims."

Source S6 notes that Google's automated systems catch only a fraction of invalid activity; the same principle applies to Meta. Advertisers who supplement platform detection with client-side evidence recover significantly more.

Limitations and When This Approach Does Not Apply

• Low traffic volumes — statistical confidence requires sufficient sessions. Very small campaigns may not generate enough data for 99% confidence classifications.
• Non-Meta/Google channels — BotRefund's refund-ready reports are formatted for Google and Meta. Other platforms may not accept the same evidence structure.
• First-party fraud — if real humans submit fake leads intentionally (e.g., incentive fraud), behavioral signals will classify them as human. This is a lead-quality issue, not bot traffic.
• Script blocking — aggressive ad blockers or privacy extensions may prevent the BotRefund script from loading, creating blind spots.
• Attribution changes mid-campaign — Source S1 warns: "Preserve attribution before changing the campaign. Keep campaign, ad set, creative, placement, click identifier." Changing UTM structures or pixel events mid-audit breaks the evidence chain.

Terminology Quick Reference

Pixel poisoning

Corruption of conversion pixel data by bot-triggered events, causing ad algorithms to optimize for non-converting traffic.

Client-side audit

Detection running in the visitor's browser, observing actual behavior (mouse, scroll, typing, browser APIs) rather than server logs alone.

Refund-ready report

Evidence package formatted to match the specific requirements of Google's or Meta's invalid traffic review teams.

fbclid

Facebook Click Identifier — the unique parameter Meta appends to ad click URLs, essential for tying a session to a specific paid click.

Signal

A single independent check (e.g., scrollbar width, mouse tremor) that contributes one piece of evidence; not a verdict on its own.

Cross-check

Comparing a signal against independent browser, network, device, and behavior data to rule out false positives from privacy tools, VPNs, or unusual devices.

FAQ

How long does it take to see results after installing BotRefund?

Plan for 7–14 days of traffic accumulation before the first meaningful audit. High-volume campaigns may produce actionable flagged sessions in 3–5 days.

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

Detection and evidence collection are the core product. Source S4 mentions "block pixel poisoning in real time" as a capability, but the primary value for pixel poisoning is the forensic evidence needed for refunds. Real-time blocking can be configured but does not replace the refund workflow.

What if Meta rejects the refund claim?

BotRefund's team supports negotiation. Source S2 states they "format the data, write the claim, and support the negotiation with the documentation and arguments their reviewers need to return money to advertisers." The 83% recovery rate across 2,500+ audits reflects this end-to-end approach.

Can I use BotRefund alongside Cloudflare or other WAF/CDN bot protection?

Yes. Source S7 explains: "Many advertisers do not need to replace their edge layer; they need a marketing-focused system that keeps attribution intact, observes the visitor journey, and creates a clear record for an ad-platform review." Edge protection and client-side evidence serve different purposes.

What happens to my page load speed?

The script loads asynchronously and is designed not to block rendering. Specific performance metrics are not published in the source pack; test in your staging environment before full deployment.

Does BotRefund work for Google Ads pixel poisoning too?

Yes. Source S6 covers Google Ads invalid activity credits, and Source S2 notes BotRefund works with both Google and Meta. The detection signals are platform-agnostic; the report formatting adapts to each platform's requirements.

How much budget do I need for this to be worthwhile?

Source S2 mentions an "Under $10,000/mo" tier, suggesting the service scales down to smaller spend levels. The ROI threshold depends on your current invalid traffic rate — Source S2 states "Bot clicks steal up to 20% of your Google and Meta ad budget."

BotRefund identifies unusual timing patterns by measuring input speed, session duration, and lead arrival intervals across every paid click. The platform flags interactions faster than humanly possible — such as form submissions in under 1 millisecond — and sessions that are too short, too long, or too uniform to be human. These timing signals feed into an AI model that cross-checks 110+ independent browser, network, device, and behavioral checks to reach 99% confidence before generating a refund-ready report with click IDs, timestamps, and session recordings formatted for Google and Meta review teams.

What unusual timing means in paid traffic

Unusual timing covers any temporal pattern that real users rarely produce. On Meta campaigns this includes several leads arriving in short bursts, forms submitted immediately after landing, or conversions concentrated at unusual hours. On Google Ads it appears as rapid clicking — multiple clicks from the same IP in a short window — or duplicate click signatures suggesting automated repetition. Both platforms treat these as invalid activity when they deviate significantly from typical user behavior at the server or browser level.

BotRefund separates these patterns from normal variation. A weak campaign can attract real people who are not ready to buy, but 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.

How BotRefund captures timing signals

BotRefund runs client-side checks in the visitor's browser after the paid click lands. The script measures pointer behavior (robotic linear mouse movements, absence of humanlike mouse tremor), speed behavior (superhuman input speed under 1ms), path behavior (grid-aligned movement patterns), engagement behavior (absence of clicks or scrolling), and session behavior (unnatural session durations that are too short, too long, or too uniform). Each check produces independent evidence rather than a verdict.

The platform combines 110+ behavioral, browser, hardware, network, and attribution signals. Timing signals are weighed alongside scrollbar width leaks, clean context iframe mismatches, and evasion/debugger/anti-stealth traps. The AI prediction model evaluates the complete pattern instead of trusting a raw rule, which is how BotRefund reaches 99% confidence when the session evidence supports it.

Key timing signals BotRefund monitors

• Superhuman input speed: Interactions faster than 1 millisecond that a person could not realistically perform.
• Unnatural session durations: Visits that are too short, too long, or too uniform across multiple sessions.
• Burst lead arrivals: Several conversions arriving in tight time clusters inconsistent with organic traffic flow.
• Immediate form submission: Forms completed instantly after page load without reading or scrolling.
• Unusual hour concentration: Conversions clustered at atypical times for the target audience.
• Rapid clicking patterns: Multiple clicks from the same IP or click ID within implausible intervals.

Step-by-step process to detect unusual timing with BotRefund

1. Install the BotRefund tracking script on your landing pages. The script begins collecting browser, network, device, and behavioral data for every paid click immediately.
2. Preserve attribution before changing campaigns. Keep campaign, ad set, creative, placement, and click identifier data intact so each flagged session ties back to the exact paid interaction.
3. Allow data collection across a representative traffic volume. BotRefund needs enough sessions to distinguish anomalies from normal variation.
4. Review the audit dashboard for timing-specific flags: speed behavior alerts, session duration anomalies, and burst-pattern clusters. Each finding includes a session-by-session explanation with signal-by-signal reasoning.
5. Cross-check timing signals against CRM outcomes. A high reported lead count paired with no calls connected, demos booked, or qualified opportunities confirms the timing anomalies map to wasted spend.
6. Generate the refund-ready report. BotRefund formats click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning in the structure Google and Meta reviewers expect.
7. Submit the claim through the platform's negotiation workflow or export the report for manual filing. BotRefund's team has worked through 2,500+ audits and knows how to present bot evidence to both platforms.

Interpreting timing data in context

Not every fast session is a bot. Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior for genuine people. BotRefund keeps each timing signal as evidence — not a verdict — and cross-checks it against independent browser, network, device, and behavior data. The AI model weighs the complete pattern: a single anomaly rarely triggers a bot classification, but a consistent cluster of timing, pointer, scroll, and rendering anomalies does.

Campaign patterns matter too. A sharp lead-quality difference by placement, creative, audience expansion, device, or landing page helps isolate where unusual timing concentrates. This lets you suppress specific traffic sources rather than pausing entire campaigns.

Common mistakes and limitations

• Treating every unresponsive contact as fraud. This can make a team exclude a valuable audience. Start with a structured audit comparing ad-platform data, website sessions, and CRM outcomes before changing targeting.
• Relying on a single timing signal. BotRefund's 99% confidence comes from corroboration across 110+ checks, not one browser tell.
• Changing campaigns before preserving attribution. If you pause or edit campaigns before the audit captures click IDs and placement data, you lose the evidence chain needed for refunds.
• Expecting automatic platform credits. Google and Meta issue some invalid activity credits automatically, but their server-level detection catches far less than client-side behavioral evidence. Most refunds require a filed claim with structured evidence.
• Assuming timing detection works without the script installed. Server-side logs alone miss advanced botnets that mimic human IPs and headers. Client-side measurement is required for input-speed and session-duration signals.

Verification step: confirm timing anomalies map to wasted spend

Before filing a refund request, verify that flagged timing anomalies correlate with zero CRM progression. Pull the click IDs from BotRefund's report, match them to your CRM records, and confirm those sessions produced no calls connected, demos booked, qualified opportunities, or repeat engagement. This CRM-outcome check is the practical proof that unusual timing represents invalid traffic rather than fast but genuine users.

Key facts

Frequently asked questions

How fast is "superhuman" input speed?

BotRefund flags interactions under 1 millisecond. Real human input — clicks, keystrokes, form field transitions — cannot occur this fast. This threshold is measured client-side in the browser for each session.

Does BotRefund detect timing anomalies on both Google and Meta?

Yes. The same 110+ signal suite runs on traffic from both platforms. Meta-specific patterns include burst leads and immediate form submissions; Google-specific patterns include rapid clicking and duplicate click signatures. The refund-ready report is formatted for each platform's review process.

Can I use BotRefund timing data without filing a refund claim?

Yes. The dashboard shows timing anomalies in real time. You can suppress conversion events for flagged sessions so Google and Meta bidding algorithms train only on verified human traffic, improving ROAS without waiting for a platform refund.

What if my traffic has legitimate fast users?

BotRefund cross-checks timing signals against 100+ other browser, network, device, and behavioral checks. A single fast session without corroborating anomalies (robotic mouse paths, no scrolling, grid-aligned movement, etc.) will not be classified as a bot. The AI model weighs the complete pattern.

How long does data collection take before timing patterns are reliable?

There is no fixed minimum, but you need enough paid sessions to establish a baseline. Low-volume campaigns may require weeks; high-volume campaigns can produce actionable clusters in days. The dashboard indicates when statistical confidence is reached for each signal cluster.

Does BotRefund replace server-side fraud filters?

No. Server-side filters catch basic scraper bots via IP and header analysis. BotRefund adds the client-side behavioral layer that detects advanced botnets mimicking human IPs. Both layers can run together; BotRefund does not require infrastructure changes.

What happens after I submit a refund claim with BotRefund's report?

BotRefund's team supports the negotiation with Google and Meta reviewers using the structured evidence. Across 2,500+ audits, 83% of clients recover funds. The report format matches what platform teams use to review invalid traffic claims, reducing back-and-forth requests for additional data.

BotRefund distinguishes bots from low-intent humans by collecting client-side evidence — mouse movement, scroll behavior, browser API consistency, timing, and network context — and weighing the full pattern with an AI model that reaches 99% confidence when the evidence supports it. A single anomaly is never a verdict; the system cross-references each signal against independent browser, device, and network data so privacy tools, corporate networks, or unusual devices don't cause false positives.

Why the distinction matters for ad budgets

Treating every bad lead as bot traffic wastes money twice: you lose the refund you could claim for actual invalid clicks, and you risk suppressing audiences that contain real buyers who just aren't ready yet. Not every bad lead is a bot, and that matters. Treating every unresponsive contact as fraud can make a team exclude a valuable audience. A weak campaign can attract real people who are not ready to buy. Bot traffic and form spam tend to 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.

Low-intent humans still show human variance — hesitation, scrolling, corrections, imperfect timing. Bots leave clusters of technical tells: linear mouse paths, sub-millisecond inputs, missing scroll events, or browser API mismatches that automation frameworks struggle to fake. The practical difference shows up in CRM outcomes: a high reported lead count paired with no calls connected, demos booked, qualified opportunities, or repeat engagement suggests automation; a trickle of real contacts that don't convert suggests targeting or offer problems.

How BotRefund builds the evidence layer

BotRefund runs 110+ independent checks on every session. Each check adds one objective fact — for example, whether the scrollbar width matches a real browser, whether an iframe context is clean, whether mouse movement has natural tremor, whether clicks follow human-like timing. BotRefund combines 110+ behavioral, browser, hardware, network, and attribution signals to identify automated traffic with 99% confidence. Each finding includes a clear, session-by-session explanation instead of a generic invalid-traffic estimate.

The engine does not rely on any single signal. A single anomaly is not a bot verdict. Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior for genuine people. BotRefund keeps this signal as evidence — not a verdict — and cross-checks it against independent browser, network, device, and behavior data. An AI prediction model weighs the complete pattern across all signals instead of trusting a raw rule.

Key signals that separate bots from low-intent visitors

These signals map to the investigation framework BotRefund recommends: Contactability: disconnected numbers, invalid email domains, repeated addresses, or an unusual concentration of one country code. Timing: several leads arriving in short bursts, forms submitted immediately after landing, or conversions concentrated at unusual hours. Session behavior: no scrolling, no field corrections, uniform click paths, and no meaningful time on the offer page. Campaign patterns: a sharp lead-quality difference by placement, creative, audience expansion, device, or landing page. CRM outcome: a high reported lead count paired with no calls connected, demos booked, qualified opportunities, or repeat engagement.

Step-by-step investigation workflow

1. Preserve attribution before changing anything. Keep campaign, ad set, creative, placement, and click identifiers intact so every flagged session ties back to a specific paid click.
2. Install the client-side tracker. BotRefund's script captures behavioral, browser, hardware, and network signals on each visit — something server logs alone cannot do.
3. Run a free bot audit. The audit surfaces the percentage of automated traffic and shows session-by-session evidence with signal-level reasoning.
4. Cross-reference with CRM outcomes. Match flagged sessions to lead records. If a session shows bot signals and the lead never responds, that's a refund candidate. If the session looks human but the lead is cold, that's a targeting or nurture problem.
5. Segment by placement, creative, and audience. Look for sharp lead-quality differences. A single placement driving 80% of bot signals is actionable; a broad quality issue across placements suggests creative or offer mismatch.
6. Generate refund-ready reports. We turn each finding into a refund-ready report with click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning. The evidence is structured in the format platform teams use to review invalid traffic claims.
7. Submit claims to Google and Meta. BotRefund's team formats the data, writes the claim, and supports negotiation with documentation the reviewers expect.
8. Verify the next cycle. After suppressions and refunds, re-audit to confirm bot rate drops and lead quality improves.

Common mistakes that blur the line

• Relying only on server-side data. IP reputation and user-agent strings miss advanced botnets that rotate residential proxies and spoof headers. Server-side audits look at server log files. They monitor IP addresses, request headers, and user-agent data. While this catches basic scraper bots, it struggles to detect advanced botnets.
• Treating every anomaly as fraud. A single odd signal — like an unusual screen resolution — can come from a legitimate user on a corporate VPN or privacy browser. BotRefund's cross-checking prevents this.
• Ignoring CRM feedback. Platform-reported conversions don't equal revenue. If sales can't reach the contact, the lead is either bot or mis-targeted; the evidence tells you which.
• Pausing campaigns before preserving click IDs. Once a campaign is paused, attribution data can degrade. Capture GCLIDs and fbclids first.
• Assuming low conversion rate = bot traffic. A 2% conversion rate with human session behavior is a funnel issue, not fraud.

Limitations and when this approach doesn't apply

• Very low traffic volumes. Statistical confidence improves with session count; tiny campaigns may not yield enough evidence for 99% confidence on individual sessions.
• Non-Meta/Google ad platforms. Refund-ready reports are formatted for Google and Meta review processes. Other platforms may accept the evidence but have different claim workflows.
• First-party fraud (real humans gaming incentives). If a real person fills forms for a reward, behavioral signals look human. This is a policy/enforcement issue, not a detection gap.
• Sessions without JavaScript execution. BotRefund's client-side checks require script execution. Purely server-side bots that never render the page are caught by IP/behavior correlation but not full behavioral analysis.

Key facts at a glance

Frequently asked questions

How long does a bot audit take?

The free audit runs automatically once the tracker is installed. Meaningful results typically appear within a few days depending on traffic volume.

Will BotRefund slow down my landing pages?

The script is lightweight and loads asynchronously. It does not block rendering or interfere with Core Web Vitals.

Can I use BotRefund alongside Cloudflare or a WAF?

Yes. Many advertisers do not need to replace their edge layer; they need a marketing-focused system that keeps attribution intact, observes the visitor journey, and creates a clear record for an ad-platform review. BotRefund adds the evidence layer; your edge provider handles DDoS and infrastructure security.

What if Google or Meta already issued an automatic credit?

Automatic credits cover only what their systems catch. Google's detection is sophisticated but far from perfect. BotRefund finds the invalid activity their filters miss and helps you claim the difference.

Do I need technical resources to implement?

Installation is a single script tag. The dashboard and reports are designed for marketing teams, not engineers. BotRefund's team also handles claim writing and negotiation.

How does pricing work?

Plans start under $10,000/month for enterprise. A free audit shows your bot rate before any commitment.

What happens after I get a refund?

BotRefund helps you suppress the flagged traffic sources so the same bots don't keep clicking. Conversion signals stay clean for future optimization.

BotRefund documents bot behavior by installing a lightweight client-side script on your landing pages that records 110+ independent signals per visit — including mouse movement patterns, scroll behavior, browser fingerprint inconsistencies, timing anomalies, and attribution data like click IDs and campaign parameters. The system cross-checks each signal against a prediction model that reaches 99% confidence when the evidence supports it, then produces a session-by-session report formatted for Google and Meta invalid-traffic review teams. You install the script, let it run while your campaigns spend, and download a refund-ready report that includes click IDs (GCLID, FBCLID), timestamps, session replays, and signal-level explanations.

What BotRefund Documents and Why It Matters

BotRefund focuses on the visitor journey after a paid click. It captures browser-level evidence that server logs miss: pointer paths that snap to grid lines instead of curving naturally, scrollbar width mismatches that reveal automated browsers, iframe context leaks from automation frameworks, superhuman input speeds under one millisecond, and the absence of humanlike mouse tremor. Each signal is stored as independent evidence, not a verdict, so privacy tools or corporate networks don't trigger false positives. The platform correlates these signals with your ad-platform click identifiers so every flagged session ties back to a specific campaign, ad set, creative, and placement.

This matters because Google and Meta only refund invalid activity when you supply evidence in their review format. Server-side IP filters catch basic scrapers but miss residential proxy networks and sophisticated botnets that mimic human IPs. Client-side behavioral documentation fills that gap by proving the visitor behind the click did not behave like a person.

Key Facts

How the Detection Works: Signal Categories

BotRefund groups its 110+ checks into behavioral families. Each family contributes one piece of the overall pattern:

• Click behavior — Ghost click detection catches clicks that fire without the natural human intent sequence (S2).
• Trap behavior — Honeypot interactions reveal bots that respond to hidden page elements real users never see (S2).
• Pointer behavior — Robotic linear movements and grid-aligned paths flag unnaturally straight or block-snapped trajectories (S2).
• Motion behavior — Absence of humanlike mouse tremor detects the missing micro-jitter present in every real hand (S2).
• Speed behavior — Superhuman input speed under 1ms identifies actions faster than any person can perform (S2).
• Engagement behavior — Absence of clicks or scrolling highlights sessions too static for genuine browsing (S2).
• Session behavior — Unnatural durations (too short, too long, or too uniform) signal scripted visits (S2).
• Browser fingerprint checks — Scrollbar Width Leak (S3) and Clean Context Iframe (S5) expose automation frameworks that patch or hide browser APIs inconsistently.

No single signal proves fraud. BotRefund keeps each as evidence and feeds the full pattern into an AI prediction model that weighs corroboration across browser, network, device, and behavior layers (S3, S5).

Step-by-Step: Documenting Behavior for a Refund Claim

1. Add the script to every landing page that receives paid traffic. The snippet loads asynchronously and does not block page render.
2. Verify click-ID capture in the dashboard. Confirm GCLID (Google) and FBCLID (Meta) parameters are attached to sessions so evidence ties to the exact campaign, ad set, creative, and placement (S1, S2).
3. Run campaigns normally for a meaningful sample — typically 7–14 days or until you have several thousand paid clicks. Do not pause or restructure campaigns mid-audit; you need the live placement mix.
4. Review the flagged sessions in the BotRefund dashboard. Each session shows a timeline, signal breakdown, and a replay. Look for clusters: same placement, same creative, same hour bursts (S1).
5. Export the refund-ready report. The report includes click IDs, timestamps, campaign metadata, session recordings, and signal-by-signal reasoning formatted for Google and Meta review teams (S2).
6. File the claim through the platform's invalid-activity or traffic-quality form. Attach the BotRefund report. BotRefund's team can assist with claim wording and follow-up negotiation (S2).
7. Track the outcome. Credits appear in your ad account. Reinvest or reallocate based on cleaned data.

Building a Refund-Ready Report

Google and Meta reviewers expect specific fields. BotRefund structures each report with:

• Click identifier (GCLID / FBCLID)
• Campaign, ad set, creative, placement, device, geo
• Timestamp of click and session start
• Session recording (scrubbed of PII)
• Signal list with pass/fail and confidence weight
• AI prediction summary (bot / human / inconclusive)
• Narrative explanation linking signals to platform policy definitions

The platform teams use this format internally, so a matching submission reduces back-and-forth. BotRefund's 83% approval rate across 2,500+ audits comes from this alignment plus negotiation experience (S2).

Common Mistakes and Limitations

• Starting the audit after pausing campaigns — you lose the live placement mix that reveals which sources drive bots (S1).
• Treating every bad lead as a bot — weak offers attract real but unqualified people. BotRefund separates low intent from automation (S1).
• Expecting 100% coverage — sophisticated actors may evade some signals. The 99% confidence applies when evidence supports it; inconclusive sessions are labeled, not forced (S3, S5).
• Using server logs alone — they miss client-side behavior like mouse tremor, scrollbar leaks, and iframe context (S2, S3, S5).
• Filing without click IDs — platforms cannot match sessions to billed clicks without GCLID/FBCLID (S2).

BotRefund does not replace your analytics, CRM, or tag manager. It adds an evidence layer for paid-traffic quality. It also does not block traffic in real time; it documents for refund claims and suppression lists.

When to Use This vs. Other Approaches

Many advertisers keep their edge provider and add BotRefund for the marketing-layer evidence. The jobs coexist (S7).

FAQ

How long until I have enough data to file a claim?

Typically 7–14 days of live spend across the placements you want audited. You need a few thousand paid clicks to surface statistically meaningful clusters.

Does the script slow down my page?

It loads asynchronously and is designed for negligible impact on Core Web Vitals. Most sites see no measurable change.

What if I run campaigns on both Google and Meta?

BotRefund captures GCLID and FBCLID automatically. One script covers both platforms; the dashboard separates reports by source.

Can I use the data to exclude placements in-platform?

Yes. Export the placement-level bot rates and add the worst offenders to your placement exclusion lists in Google Ads and Meta Ads Manager.

What happens if Google or Meta rejects the claim?

BotRefund's team reviews the rejection reason, supplements evidence if gaps exist, and resubmits. The 83% success rate includes negotiated approvals after initial denial (S2).

Is there a minimum spend requirement?

No published minimum. The free audit tier lets you test detection volume before committing. Enterprise plans start under $10,000/mo (S2).

How does BotRefund handle privacy regulations?

Session recordings are scrubbed of personally identifiable information. The system stores behavioral signals, not form content or keystrokes.

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

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.

BotRefund does not run a traditional deduplication database. Instead, it detects duplicate and fraudulent leads by examining each visitor session for evidence of automation. When the same bot network or click farm submits multiple forms, the sessions share telltale patterns: identical browser fingerprints, superhuman input speed, missing mouse tremor, grid-aligned pointer paths, and no meaningful page engagement. BotRefund captures these signals client-side, correlates them with the click ID (fbclid or gclid) that brought the visitor, and builds a session-by-session evidence pack that Google and Meta accept for invalid-activity refunds.

To use BotRefund for this purpose, you install its tracking script on your landing pages, let it collect a baseline of traffic, then review the dashboard for clusters of high-confidence bot sessions. Each flagged session includes a click ID, timestamp, campaign metadata, and a signal-by-signal explanation. You can export these clusters, suppress the associated conversion events in your ad platforms, and submit the report for a credit. The workflow is designed for marketing teams, not infrastructure engineers — no CDN changes, no log parsing, no server-side integration required.

What BotRefund Actually Measures

BotRefund runs 106 independent browser checks (plus network and attribution signals) on every visit. Each check produces one objective fact — for example, whether the scrollbar width matches a real browser, whether an iframe context is clean, whether mouse movements show human tremor, or whether inputs occur faster than 1 millisecond. No single check decides "bot"; the system weighs the complete pattern through an AI model that reaches 99% confidence when the evidence supports it. This corroboration approach matters for duplicate leads: a single anomaly could be a privacy tool or corporate network, but a cluster of sessions sharing multiple anomalies across different IPs and user agents is strong evidence of coordinated automation.

Key Signals That Reveal Duplicate or Fraudulent Leads

The source documentation highlights five signal categories worth investigating when lead quality drops:

• Contactability: Disconnected numbers, invalid email domains, repeated addresses, or an unusual concentration of one country code.
• Timing: Several leads arriving in short bursts, forms submitted immediately after landing, or conversions concentrated at unusual hours.
• Session behavior: No scrolling, no field corrections, uniform click paths, and no meaningful time on the offer page.
• Campaign patterns: A sharp lead-quality difference by placement, creative, audience expansion, device, or landing page.
• CRM outcome: A high reported lead count paired with no calls connected, demos booked, qualified opportunities, or repeat engagement.

BotRefund surfaces these patterns automatically. When you see a placement or creative generating leads that share the same behavioral fingerprint — same input speed, same missing tremor, same scrollbar anomaly — you have a duplicate-lead cluster driven by automation, not by real people filling forms twice.

Step-by-Step: Setting Up BotRefund to Audit Lead Quality

1. Add the script to your landing pages. Paste the BotRefund snippet in the <head> of every page that receives paid traffic. The script loads asynchronously and does not block page render.
2. Preserve attribution before changing campaigns. Keep campaign, ad set, creative, placement, and click identifiers (fbclid, gclid) intact in your analytics and CRM. BotRefund ties each session to these IDs so the refund report maps back to the exact paid click.
3. Let traffic accumulate for 7–14 days. A baseline period lets the model calibrate to your normal human traffic. Do not pause campaigns or change targeting during this window.
4. Open the dashboard and filter by confidence. Sessions flagged at 99% confidence are the starting point. Sort by campaign, placement, or landing page to see where bot clusters concentrate.
5. Review session recordings and signal breakdowns. Each flagged session shows a replay, a list of triggered checks (e.g., "Superhuman input speed (<1ms)", "Absence of humanlike mouse tremor"), and the click ID. Confirm the pattern looks like automation, not a privacy tool or unusual device.
6. Export the cluster as a refund-ready report. The report includes click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning formatted for Google and Meta review teams.
7. Suppress conversion events for flagged click IDs. In Google Ads and Meta Ads Manager, use the click IDs to exclude those conversions from your optimization signals. This stops the bidding algorithm from learning on bot data.
8. Submit the refund claim. BotRefund's team can format and negotiate the claim, or you can file it yourself using the exported evidence. Across 2,500+ audits, 83% of clients recover funds.

Reading the Evidence: What a Bot Session Looks Like

A human session shows imperfection: pauses between fields, backspacing, curved mouse paths, variable scroll speed, and time spent reading. A bot session often shows the opposite: every field filled in <1ms, pointer moving in straight grid-aligned lines, no scroll events, no mouse tremor, and a scrollbar width that doesn't match the browser's reported rendering engine. BotRefund's individual checks — such as Scrollbar Width Leak and Clean Context Iframe — each add one independent fact. The AI prediction weighs all 106+ facts together. When you open a flagged session, you see which checks fired and why the model concluded "bot." This transparency lets you explain the finding to a platform reviewer or a skeptical stakeholder.

Integrating With Your CRM and Ad Platforms

BotRefund does not replace your CRM deduplication rules. It operates upstream: it tells you which paid clicks produced automated sessions so you can stop counting those conversions. The practical integration points are:

• Click ID pass-through: Ensure your forms capture fbclid/gclid in hidden fields and write them to the lead record. BotRefund uses the same IDs.
• Conversion API / offline conversions: When you suppress a bot click, also remove or mark the corresponding offline conversion event so the platform's optimization sees the correction.
• Suppression lists: Export the flagged click IDs and upload them as exclusion audiences or invalid-click lists where the platform supports it.
• Reporting cadence: Schedule a weekly review of new high-confidence clusters. Bot traffic patterns shift when fraud operators rotate infrastructure.

Limitations and When This Approach Does Not Apply

• Human duplicates: If a real person submits the same form twice (e.g., double-click, page refresh), BotRefund will see two human sessions. This is a form-handling or CRM deduplication issue, not a bot issue.
• Low-volume campaigns: The model benefits from volume to establish a baseline. Very small test campaigns may not generate enough sessions for high-confidence clustering.
• Non-JavaScript environments: If a significant portion of your traffic comes from environments that block client-side scripts (some enterprise proxies, certain embedded browsers), those sessions won't be analyzed.
• Privacy tools and corporate networks: VPNs, anti-fingerprinting extensions, and managed devices can trigger individual checks. BotRefund's cross-checking reduces false positives, but you should still review borderline sessions manually.
• Server-side fraud only: If fraud occurs entirely server-to-server (e.g., API abuse, postback spoofing) without a browser visiting your page, client-side detection cannot see it.

Key Facts

Common Mistakes to Avoid

Practical Scenarios

Scenario 1: Sudden Lead Spike on a New Creative

A new Facebook creative drives a 3x lead volume increase. Cost per lead looks stable. Sales reports zero contactability. BotRefund dashboard shows 87% of the new creative's sessions flagged at 99% confidence — identical pointer paths, superhuman input speed, no scroll. You export the click IDs, suppress the conversions, and file a refund claim for that creative's spend.

Scenario 2: Gradual Quality Decline Across Placements

Over three weeks, lead-to-opportunity rate drops from 12% to 4%. No single placement stands out. BotRefund reveals a cluster of sessions from Audience Network placements sharing the same Clean Context Iframe anomaly and grid-aligned mouse movements. You exclude Audience Network from the campaign, suppress the flagged click IDs, and recover two weeks of spend.

Scenario 3: Competitor Click Fraud on Brand Terms

Brand search campaigns show high click volume but zero conversions. BotRefund detects ghost clicks (click activity without human intent sequence) and trap interactions (honeypot elements triggered) concentrated on a few IP blocks. The refund-ready report includes timestamps and click IDs for each ghost click. You submit the claim and add the IPs to your exclusion list.

Terminology Quick Reference

• Click ID (fbclid/gclid): Unique identifier appended to the landing page URL by Meta or Google when a user clicks an ad. Essential for tying a session to a paid click.
• Invalid activity / invalid traffic: Platform term for clicks or impressions not resulting from genuine user interest (bots, accidental clicks, competitor fraud).
• Pixel poisoning: When bot conversions train the ad platform's optimization algorithm to target more bot-like users.
• Refund-ready report: Evidence package formatted to the platform's review specifications — click IDs, timestamps, session recordings, signal reasoning.
• Suppression: Removing or marking a conversion event so it no longer feeds the bidding algorithm.
• Corroboration: Requiring multiple independent signals to agree before labeling a session as bot. Reduces false positives from privacy tools or unusual devices.

FAQ

Does BotRefund automatically block bots from submitting forms?

No. BotRefund is an evidence and refund layer, not a WAF or form blocker. It detects and documents automated sessions so you can suppress their conversions and claim refunds. For real-time blocking, pair it with a form-level honeypot or a lightweight challenge.

How long before I see results?

Most teams see high-confidence clusters within 7–14 days of installing the script, depending on traffic volume. The model needs a baseline of human traffic to calibrate.

Can I use BotRefund only for Meta (Facebook/Instagram) campaigns?

Yes. The script works on any landing page receiving paid traffic. The refund workflow supports both Meta and Google Ads. You can filter the dashboard by platform.

What if my forms don't capture click IDs today?

Add hidden fields for fbclid and gclid to your forms and write them to the lead record in your CRM. Without click IDs, you can still see bot clusters in BotRefund, but you cannot map them to specific paid clicks for suppression or refund claims.

Does BotRefund work with server-side tracking (CAPI, Enhanced Conversions)?

Yes. BotRefund's client-side evidence complements server-side tracking. When you suppress a bot click, also remove the corresponding server-side conversion event so the platform's optimization sees the correction.

How does BotRefund differ from Cloudflare or a WAF?

Cloudflare and WAFs operate at the network edge (IP reputation, request headers, rate limiting). BotRefund operates in the browser after the click, capturing behavioral, rendering, and interaction signals that edge layers cannot see. T

To use BotRefund to identify suspicious sessions, you first add the BotRefund tracking snippet to every page of your site. The snippet runs in the visitor's browser and collects behavioral data such as mouse movement speed, click timing, and scroll activity.

Overview: Why behavioral detection matters

IP‑based filters can be evaded by rotating addresses or using residential proxies. Behavioral signals are harder to fake because they reflect how a real person moves, clicks, and reads a page. BotRefund looks at over a hundred independent browser actions instead of relying only on network data.

Source S1 notes that Meta campaigns often show normal cost per lead while sales teams receive unreachable contacts, a pattern that can hide automated traffic. Source S4 explains that default network filters miss advanced proxies, so client‑side behavioral audits are needed to catch fake clicks that poison conversion tracking.

Detection mechanics: the 106 checks and risk score

BotRefund runs 106 independent checks. Examples include click behavior (ghost click detection), pointer behavior (robotic linear mouse movements), speed behavior (super‑human input speed under 1 ms), path behavior (grid‑aligned movement), engagement behavior (absence of clicks or scrolling), and session behavior (uniform click paths, no field corrections).

Two specific checks described in the source pack are the Scrollbar Width Leak (S3) and the Clean Context Iframe (S5). Each looks for a mismatch that a real browsing session does not normally create, such as altered browser APIs or unexpected scrollbar dimensions.

A single anomaly is not enough to label a visitor as a bot. BotRefund treats each signal as evidence, cross‑checks it with other browser, network, device, and behavior data, and feeds the full pattern into an AI prediction model. This corroboration is why the vendor claims up to 99 % accuracy (S3, S5).

The risk score shown in the dashboard is a weighted sum of the 106 checks. Higher scores indicate stronger evidence of non‑human behavior, but the exact weighting is proprietary; the model decides which combinations matter most.

Setup: adding the snippet and verifying collection

You need access to the website’s HTML or a tag manager, a BotRefund account, and permission to run JavaScript on your pages. No server changes are required.

1. Log in to BotRefund and copy the tracking snippet from the Setup page.
2. Paste the snippet just before the closing tag on every page, or add it through your tag manager as a custom HTML tag.
3. Publish the change and verify that the snippet loads by opening the browser console and looking for the BotRefund object.
4. In the BotRefund dashboard, enable Session recording and set the sensitivity level to Standard (the default catches the most common bot patterns).
5. Allow at least 24 hours for data to accumulate before reviewing results.

Source S2 notes that the snippet can be added in about one minute and that a free bot audit is available without a credit card.

Using the dashboard to review suspicious sessions

After data collection, open the Sessions tab and apply the Suspicious filter. Each flagged session shows a risk score, a timestamp, and a replay button.

Click the replay to watch the visitor’s mouse movements, clicks, and scrolls. Look for the patterns BotRefund highlights: super‑human speed, grid‑aligned pointer paths, missing scroll activity, or uniform click paths that lack natural hesitation.

Use the Export button to download a CSV or PDF report that includes the session ID, risk score, and the raw signal values. This report can be attached to a refund request with Google Ads or Meta.

The risk score is a weighted sum of the 106 checks; higher scores mean the session deviates more from typical human behavior across many signals.

Preparing refund claims for Google Ads and Meta – common pitfalls and workflow

A common mistake is to submit BotRefund data alone. Ad platforms require their own invalid activity reports to corroborate the evidence. Another pitfall is mismatched timestamps; always preserve the original attribution before changing campaigns or pausing ads.

Workflow:

1. Preserve attribution: export the Google Ads or Meta click report for the same date range before making any changes.
2. Download the BotRefund export of flagged sessions (session ID, timestamp, risk score).
3. Match BotRefund timestamps or session IDs to the platform’s click identifiers (GCLID for Google Ads, Meta click ID).
4. If the same clicks appear in both lists as invalid, you have corroborated evidence.
5. Submit the BotRefund export together with the ad‑platform report to start a refund claim.

Source S6 explains that Google offers invalid activity credits but the process is not automatic; understanding how to file a claim is key to recovering money. BotRefund helps navigate this process with an advertised 83 % success rate.

Source S1 adds that Meta advertisers should look at contactability, timing, session behavior (no scrolling, no field corrections, uniform click paths), campaign patterns, and CRM outcomes to separate normal lead‑quality variation from automated activity.

Source S8 shows a real‑world example: the neobank FinTrust suppressed conversion events for automated browser emulation signals, protected lead quality, and recovered $140,000 in ad spend.

Source S7 notes that BotRefund can prepare reports in a format that Google and Meta can review, supporting negotiations with both platforms.

Limitations, best practices, and frequently asked questions

BotRefund works best on sites that receive at least a few hundred sessions per day. Very low traffic may not generate enough data for reliable scoring (S2).

The tool relies on JavaScript execution; visitors who block scripts or use browsers that strip the snippet will not be scored (S2). Non‑web environments such as mobile apps or server‑to‑server API calls are outside BotRefund’s scope; a different fraud solution is needed for those channels.

Because privacy tools, travel networks, or unusual devices can produce unexpected behavior for genuine people, BotRefund treats each signal as evidence, not a verdict, and cross‑checks it with other data (S3, S5).

Practical tips:

• Start with the Standard sensitivity setting; after reviewing a few flagged sessions, adjust up or down based on the rate of false positives you observe.
• Use tag managers to deploy the snippet quickly and to pause or remove it without editing code.
• Schedule automatic exports of the Suspicious report (CSV or PDF) so you have ready‑to‑submit evidence for regular refund cycles.
• When a replay looks legitimate, exclude that session from the export and consider lowering the sensitivity or adding a whitelist rule if available.
• Keep a log of matched GCLIDs or Meta click IDs to speed up the refund claim process.

FAQ:

1. Why does BotRefund look at mouse movement instead of just IP addresses? Because many bots rotate IPs or use residential proxies; behavioral clues are harder to fake (S1, S4).
2. How long does it take to see results after installing the snippet? You can start seeing flagged sessions within a few hours, but waiting 24 hours gives a more stable risk score (S2).
3. What does the risk score mean? It is a weighted sum of the 106 checks; higher scores indicate stronger evidence of non‑human behavior (S2, S3, S5).
4. Can I adjust which signals BotRefund uses? Yes, in the dashboard you can enable or disable specific checks, but the default set is optimized for most ad‑fraud scenarios (S2).
5. Is there a cost for the free bot audit? No. The audit is free and requires no credit card; you only pay if you choose a paid plan for continuous protection (S2).
6. What should I do if BotRefund flags a session that looks legitimate? Review the replay carefully; if you are still unsure, exclude that session from the report and consider lowering the sensitivity (S2).
7. How do I handle false positives in my refund claim? Exclude the questionable sessions from the export, keep a record of why they were removed, and rely on the remaining corroborated evidence.
8. Can I integrate BotRefund with Google Tag Manager? Yes, add the snippet as a custom HTML tag and publish through the container (S2).
9. Is it possible to automate the export of suspicious sessions for regular reporting? Yes, use the Export button to schedule CSV or PDF downloads, or use the API if available (not detailed in sources but implied by export functionality).

BotRefund improves lead quality by identifying bot and invalid traffic that reaches your lead forms, then giving you the evidence to stop those signals from poisoning your conversion data. The process has four phases: install the onsite tracker, connect your Google and Meta ad accounts so click IDs (GCLID, FBCLID) attach to each session, review the dashboard's session-by-session evidence, and export refund-ready reports or suppression lists that keep fake leads out of your CRM and your bidding algorithms.

What BotRefund actually does for lead quality

BotRefund sits on your landing pages and collects 110+ behavioral, browser, hardware, network, and attribution signals per visit. Its AI weighs the complete pattern across those signals and labels each session as human or bot with 99% confidence when the evidence supports it. Each finding includes a clear, session-by-session explanation instead of a generic invalid-traffic estimate. The platform then turns those findings into refund-ready reports with click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning formatted for Google and Meta review teams.

When you suppress bot conversion events, the ad platforms' optimization algorithms stop training on fake leads. That means your cost per acquisition reflects real prospects, your lookalike audiences model actual buyers, and your sales team spends time on contacts that can convert. The FinTrust case study showed a 14% average bot click rate and an 18% conversion rate increase after suppressing automated browser emulation signals so Facebook and Google AI trained only on verified bank accounts.

Prerequisites before you start

• Active paid campaigns on Google Ads or Meta Ads — BotRefund needs click identifiers (GCLID, FBCLID) to tie sessions back to specific campaigns, ad sets, creatives, and placements.
• Access to add JavaScript to your landing pages — The tracker must load on every page a paid visitor can reach, including thank-you and confirmation pages.
• Admin or analyst access to your ad accounts — You'll need to connect the accounts in BotRefund so it can pull campaign metadata and later push suppression lists or refund claims.
• A CRM or lead database you can query — You'll compare BotRefund's bot labels against downstream outcomes (calls connected, demos booked, qualified opportunities) to verify the system's accuracy for your traffic mix.

Step-by-step implementation process

1. Create a BotRefund account and add your domain. The onboarding flow generates a unique tracking snippet.
2. Install the tracking script on every landing page. Place it in the <head> so it loads before any user interaction. Confirm it fires by checking the live visitor view in the dashboard.
3. Connect Google Ads and Meta Ads accounts. Use the integrations page to authorize BotRefund. This pulls campaign, ad set, creative, placement, and click-ID data into each session record.
4. Let the system collect a baseline. Run traffic for 7–14 days without changing campaigns. BotRefund builds a behavioral profile of your specific audience and flags anomalies against that baseline.
5. Review the dashboard's flagged sessions. Each flagged session shows the specific signals that triggered the bot label — e.g., superhuman input speed (<1ms), absence of humanlike mouse tremor, grid-aligned movement patterns, or scrollbar width leaks. The evidence is cross-checked across browser, network, device, and behavior data.
6. Export a refund-ready report or suppression list. Reports include click IDs, timestamps, session recordings, and signal-by-signal reasoning in the format Google and Meta reviewers expect. Suppression lists can be uploaded to the platforms' invalid-traffic or conversion-exclusion tools.
7. Submit refund claims or apply suppressions. For Google, file an invalid activity credit claim with the exported evidence. For Meta, use the refund request flow with the same documentation. BotRefund's team has worked through 2,500+ audits and knows how to present evidence to both platforms' reviewers.
8. Monitor lead-quality metrics weekly. Track contactability rates, CRM qualification rates, and cost per qualified lead. Expect the bot percentage to drop as the platforms' algorithms stop optimizing for the suppressed traffic patterns.

Key signals BotRefund analyzes to separate bots from humans

No single signal proves fraud. BotRefund's accuracy comes from corroboration across independent evidence layers. Here are the main categories:

• Click behavior — Ghost click detection catches click activity that happens without the natural sequence of human intent.
• Trap behavior — Honeypot trap interactions watch for bots that respond to hidden or intentionally deceptive page elements.
• Pointer behavior — Robotic linear mouse movements flag unnaturally straight pointer paths; absence of humanlike mouse tremor looks for the tiny imperfections and jitter typical of human movement.
• Speed behavior — Superhuman input speed (<1ms) identifies interactions faster than a person could realistically perform.
• Path behavior — Grid-aligned movement patterns detect movement that snaps to precise lines or blocks instead of natural curves.
• Engagement behavior — Absence of clicks or scrolling highlights sessions that stay too static to match a real browsing journey.
• Session behavior — Unnatural session durations catch visit lengths that are too short, too long, or too uniform to be human.
• Browser and device consistency — Checks like Scrollbar Width Leak and Clean Context Iframe reveal mismatches that automated browsers struggle to reproduce.

Privacy tools, corporate networks, and unusual devices can produce unexpected behavior for genuine people. BotRefund keeps each signal as evidence — not a verdict — and cross-checks it against independent browser, network, device, and behavior data before the AI prediction weighs the complete pattern.

How to interpret and act on the data

The dashboard groups flagged sessions by campaign, placement, creative, audience expansion, device, and landing page. Look for sharp lead-quality differences across those dimensions. A practical investigation workflow from BotRefund's Meta invalid-traffic guide recommends:

1. Preserve attribution before changing the campaign. Keep campaign, ad set, creative, placement, and click identifier data intact so you can trace each bad lead back to its source.
2. Compare ad-platform data, website sessions, and CRM outcomes. A high reported lead count paired with no calls connected, demos booked, or qualified opportunities is a strong signal that invalid traffic is inflating your numbers.
3. Check contactability. Disconnected numbers, invalid email domains, repeated addresses, or an unusual concentration of one country code often correlate with bot submissions.
4. Check timing. Several leads arriving in short bursts, forms submitted immediately after landing, or conversions concentrated at unusual hours suggest automation.
5. Check session behavior. No scrolling, no field corrections, uniform click paths, and no meaningful time on the offer page are classic bot patterns.

Not every bad lead is a bot. Treating every unresponsive contact as fraud can make you exclude a valuable audience. Start with the structured audit before changing targeting or making a refund request.

Verification step: confirm the improvement is real

After you submit suppressions or refund claims, wait 14–30 days for the platforms' algorithms to retrain on the cleaned signal. Then compare three metrics against your pre-BotRefund baseline:

• Contactability rate — percentage of leads with working phone/email.
• Qualification rate — percentage of leads that become sales-qualified opportunities.
• Cost per qualified lead — total ad spend divided by qualified leads.

If contactability and qualification rates rise while cost per qualified lead falls, the suppression is working. If they don't move, review whether the flagged sessions were actually bots or whether your lead-quality problem has a different root cause (offer mismatch, audience targeting, form friction).

Limitations and when this advice does not apply

• Organic and direct traffic — BotRefund focuses on paid click attribution. It can still flag bots on organic visits, but refund claims only apply to paid clicks with valid click IDs.
• Low-volume campaigns — If you spend under a few thousand dollars per month, the sample size may be too small for high-confidence pattern detection.
• Single-page funnels without thank-you pages — The tracker needs to see the full journey including the conversion confirmation to attach the click ID to the outcome.
• Platforms beyond Google and Meta — Refund-ready reports are formatted for Google and Meta review teams. Other ad platforms may not accept the same evidence format.
• Genuine low-intent humans — Real people who click accidentally, fill forms casually, or change their minds will not be flagged as bots. Lead-quality issues from weak offers or broad targeting need creative and audience fixes, not bot suppression.

Key facts

FAQ

How long before I see lead-quality improvements?

Most teams see measurable changes in contactability and qualification rates within 14–30 days after submitting suppressions, once the ad platforms' algorithms retrain on the cleaned conversion signal.

Does BotRefund block bots in real time?

BotRefund is primarily an evidence and reporting layer. It identifies and documents bot sessions so you can suppress their conversion signals and claim refunds. It does not function as a real-time WAF or edge blocker.

Can I use BotRefund alongside Cloudflare or another edge provider?

Yes. Many advertisers keep their edge layer for DDoS mitigation and CDN delivery while adding BotRefund for the marketing-focused evidence layer that preserves attribution and creates refund-ready reports.

What if Google or Meta rejects my refund claim?

BotRefund's team supports the negotiation with documentation and arguments their reviewers need. The 83% recovery rate across 2,500+ audits reflects that experience. If a claim is denied, the evidence still lets you suppress those conversion events going forward.

How much traffic volume do I need for reliable detection?

There's no published minimum, but campaigns spending under a few thousand dollars per month may not generate enough sessions for high-confidence pattern detection across all 110+ signals.

Will BotRefund flag legitimate users who use privacy tools or corporate VPNs?

Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior. BotRefund treats each anomaly as evidence, not a verdict, and cross-checks it against independent browser, network, device, and behavior data before the AI prediction weighs the complete pattern.

What's the difference between BotRefund and server-side log analysis?

Server-side audits look at IP addresses, request headers, and user-agent data. They catch basic scrapers but struggle with advanced botnets that rotate IPs and spoof headers. BotRefund's client-side audits analyze the visitor's browser behavior — mouse movement, scroll patterns, timing, rendering details — which are much harder for bots to fake consistently.

Direct answer: connect detection to suppression, then feed clean signals to sales

BotRefund runs 110+ browser, network, and behavioral checks on every paid click. When a session is flagged as automated with 99% confidence, the platform can suppress the conversion event before it reaches your Meta Pixel or Google Ads tag. That means your CRM and sales queue only see leads that passed the behavioral audit. You also get a refund-ready report with click IDs, timestamps, and session recordings formatted for Google and Meta review, so the same evidence that protects sales also supports budget recovery.

Prerequisites before you start

• Active Google Ads and/or Meta Ads accounts with conversion tracking installed.
• Access to your website's tag manager or ability to add a lightweight JavaScript snippet.
• Admin rights in your CRM or lead-routing tool to map BotRefund's verified-lead flag.
• A process for reviewing the weekly audit summary BotRefund sends via email or dashboard.

Step-by-step implementation

1. Install the BotRefund snippet. Paste the provided JavaScript into your tag manager or directly on landing pages. The script loads asynchronously and begins collecting 110+ signals (pointer behavior, scroll patterns, browser consistency, network context, etc.) on every paid visit.
2. Enable conversion suppression. In the BotRefund dashboard, turn on "Suppress invalid conversions" for each connected ad account. This stops the conversion pixel from firing when the AI model scores a session as bot traffic with 99% confidence.
3. Map the verified-lead flag to your CRM. BotRefund adds a custom parameter (e.g., br_verified=true) to the lead payload. Configure your form handler or CRM webhook to only route leads with that flag to the sales queue. Leads without the flag can be held for review or discarded.
4. Set up the weekly audit digest. Choose recipients (growth lead, sales ops, finance). The digest shows total paid clicks, bot percentage, suppressed conversions, and a link to the refund-ready report for each platform.
5. File refund claims using the generated reports. Each report includes click IDs (GCLID/FBCLID), campaign/ad set/creative breakdown, timestamps, session recordings, and signal-by-signal reasoning. Submit these through Google Ads' invalid activity form or Meta's ad-quality appeal flow. BotRefund's historical approval rate is 83% across 2,500+ audits.
6. Verify the loop monthly. Compare CRM-reported lead count vs. BotRefund-verified lead count. Check that sales-connected calls, demos booked, and qualified opportunities trend up while raw lead volume may drop. Confirm that ad-platform credit notifications match the refund-ready reports you submitted.

How the evidence layer works

BotRefund does not rely on IP lists or user-agent strings alone. Each visit is scored across independent vectors: biometric interaction (mouse tremor, scrollbar width leak, clean-context iframe), evasion traps (debugger detection, anti-stealth checks), speed behavior (sub-millisecond inputs), and path behavior (grid-aligned movements). A single anomaly is never a verdict; the AI model weighs the complete pattern across browser, network, device, and behavior data to reach 99% confidence. This corroboration approach is why platform reviewers accept the reports.

Key facts from BotRefund's source pack

Common mistakes to avoid

• Suppressing without reviewing. Treat the first two weeks as a calibration period. Spot-check a sample of suppressed sessions in the dashboard before fully automating CRM routing.
• Ignoring placement-level differences. BotRefund often reveals that one placement (e.g., Audience Network) drives 80% of bot traffic while another is clean. Adjust placement targeting instead of pausing the whole campaign.
• Equating all bad leads with bots. S1 notes that weak campaigns attract real but unready people. Use CRM outcome data (no calls connected, no demos booked) alongside BotRefund's verdict to distinguish fraud from fit.
• Filing refund claims without click IDs. Platform reviewers require GCLID/FBCLID. BotRefund's reports include them; exporting raw CSVs from Ads Manager usually does not.

Practical scenarios

Scenario A: Lead-gen campaign with high form volume, low sales contact rate

Install BotRefund, enable suppression, and map br_verified to your CRM. Within a week you see 22% of form submissions flagged as bot. Sales now receives 78% fewer leads but connects with 3x more prospects per 100 calls. The refund-ready report yields a $12,000 Google Ads credit.

Scenario B: E-commerce brand seeing inflated ROAS from click farms

BotRefund detects grid-aligned pointer paths and superhuman input speed on a specific creative. Suppression stops those conversions from poisoning the pixel. Meta's algorithm relearns on verified purchasers; CAC drops 15% in 14 days. The same evidence supports a Meta refund claim for the prior quarter.

Scenario C: Agency managing multiple client accounts

Use the agency dashboard to run free bot audits for prospects. For active clients, centralize the weekly digest in a shared Slack channel. Sales ops at each client maps verified leads to their CRM. Agency files consolidated refund claims quarterly, citing BotRefund's 83% approval rate as a selling point.

Limitations and when this does not apply

• BotRefund only protects paid traffic that lands on pages where the snippet is installed. Organic, direct, or email traffic is not analyzed.
• Suppression works at the pixel level. If your CRM ingests leads via a separate server-side webhook that bypasses the pixel, you must also filter on the br_verified parameter there.
• Refund approval is ultimately decided by Google and Meta. BotRefund's 83% historical rate is not a guarantee for any single claim.
• The 99% confidence threshold means some sophisticated bots may pass if they perfectly mimic human behavior across all 110+ vectors. No system catches 100%.
• Enterprise pricing applies above $10,000/mo ad spend. Smaller accounts use the self-serve tier with the same detection engine but fewer negotiation hours.

Terminology quick reference

• Pixel poisoning: Invalid conversions feeding the ad platform's optimization algorithm, causing it to bid more for bot-like audiences.
• GCLID / FBCLID: Google Click ID and Facebook Click ID — unique identifiers appended to landing-page URLs that tie a session to a specific paid click.
• Refund-ready report: A PDF/CSV package formatted to match the evidence structure Google and Meta reviewers expect.
• Suppression: Preventing the conversion pixel from firing for a specific session based on real-time bot scoring.
• Invalid activity credit: Google's term for reimbursements on clicks/impressions deemed non-genuine.

FAQ

How long until sales sees cleaner leads?

Typically 24–48 hours after the snippet is live and suppression is enabled. The first week includes a learning period where the model calibrates to your traffic patterns.

Does BotRefund block bots from visiting the site?

No. It observes, scores, and optionally suppresses the conversion event. Blocking at the edge (WAF/CDN) is a separate layer; BotRefund's job is evidence and pixel protection.

Can I use BotRefund without filing refund claims?

Yes. Many teams use it solely for lead-quality filtering and pixel protection. The refund-ready reports are generated automatically; you decide whether to submit them.

What happens if a real user is falsely flagged?

The 99% confidence threshold is conservative. In the rare event of a false positive, the session recording and signal breakdown in the dashboard let you override and re-fire the conversion manually.

How does this integrate with HubSpot, Salesforce, or custom CRMs?

BotRefund passes a URL parameter and/or data-layer event. Your form handler or CRM webhook reads br_verified=true and routes accordingly. No native CRM plugin is required.

Is there a free trial or audit?

BotRefund offers a free bot audit that scans a sample of your paid traffic and delivers a one-time report. The full suppression and refund workflow requires a paid plan.

What ad spend level justifies the cost?

If bot clicks are consuming 10%+ of budget (BotRefund sees up to 20% across accounts), the recovered spend and saved sales time usually cover the subscription within the first refund cycle.

To use BotRefund to identify ad traffic with no real conversion promise (bot clicks, fake leads, and automated sessions that will never turn into customers), start by installing its tracking script on your landing pages to capture 110+ behavioral, browser, and network signals for every visitor who clicks through from a paid ad. The tool cross-checks these signals to flag automated sessions with 99% confidence, then generates refund-ready reports formatted for Google and Meta review teams. You do not need to manually parse server logs or guess at fraud patterns; BotRefund builds the evidence record for you.

What "No Promise" Ad Traffic Looks Like

Invalid ad traffic that delivers no conversion promise falls into three common categories: bot clicks that exhaust your budget without any user engagement, fake lead submissions with disconnected numbers or invalid email domains, and automated browsing sessions that never scroll, read, or interact with your offer. Unlike low-intent real users who may simply not be ready to buy, these sessions leave repeatable technical and behavioral patterns: unusually fast form completion, identical field structures, or conversion events with no meaningful page engagement.

This traffic is costly: bots load pages but do not convert, which raises your customer acquisition cost (CAC) and lowers your campaign return on ad spend (ROAS). Without browser-level auditing, you will pay for these visits without knowing they are wasting your budget.

Prerequisites Before Setting Up BotRefund

You only need two things to start using BotRefund for invalid traffic detection: access to your website’s codebase to install the tracking script, and active Google Ads or Meta ad campaigns with conversion tracking enabled. The tool works with all major landing page builders and ad platforms, and does not require you to replace your existing CDN, WAF, or edge security tools.

If you have already noticed suspicious patterns in your ad data — such as a high lead count paired with no connected calls, demos booked, or qualified opportunities — you can upload historical campaign data to BotRefund for a retroactive audit. No prior fraud detection experience is required.

Step-by-Step Process to Identify No-Promise Traffic

1. Install the BotRefund tracking script: Add the provided snippet to your website’s global header or Google Tag Manager container. The script runs client-side to capture behavioral data (scroll depth, click timing, mouse movement) and technical data (browser APIs, device properties, network context) for every visitor who clicks through from a paid ad.
2. Link your ad accounts: Connect your Google Ads and Meta Ads accounts to BotRefund so it can automatically associate visitor sessions with campaign, ad set, creative, placement, and click ID data. This preserves attribution so you can tie invalid traffic directly to specific ad spend.
3. Run an initial audit: After 24-48 hours of data collection, BotRefund will generate a report of flagged sessions, including session recordings, signal-by-signal reasoning, and timestamps. Review the flagged sessions to confirm they match your definition of invalid traffic (e.g., no scroll activity, superhuman input speed, disconnected contact details).
4. Suppress invalid conversion events: Use BotRefund’s integration to block flagged sessions from firing conversion events in your ad platform. This prevents bot traffic from poisoning your campaign optimization data and inflating your CAC metrics.
5. Generate a refund-ready report: For any flagged invalid traffic that has already incurred ad spend, export BotRefund’s formatted report. The report includes all the evidence Google and Meta review teams require: click IDs, campaign details, session recordings, and a breakdown of the signals that indicate automated activity.

How to Verify Your BotRefund Findings

BotRefund flags sessions as potentially invalid based on a weighted analysis of 110+ signals, not a single rule. To verify a finding, check the session replay included in the report: real users will show natural scroll pauses, mouse movement jitter, and field corrections, while bot sessions will have uniform click paths, no scrolling, and form submissions completed in under 1 millisecond.

You can also cross-reference flagged sessions with your CRM data: if a lead has a disconnected phone number, invalid email domain, or no follow-up engagement, it is likely a fake submission with no conversion promise. BotRefund’s reports are structured to make this cross-check easy, with all session data tied to the corresponding lead record.

Key Limitations of BotRefund’s Detection

BotRefund is not a guarantee of refund approval: final decisions rest with Google and Meta’s review teams, who may request additional evidence or deny claims for other policy reasons. The tool also does not catch 100% of invalid traffic, as sophisticated bots that perfectly mimic human behavior may occasionally slip through, though its 99% confidence rate is among the highest in the market.

Additionally, BotRefund is designed for ad spend recovery, not general website security. It does not block DDoS attacks, filter malicious server requests, or replace WAF tools. If your primary goal is infrastructure protection, you will need a separate edge security solution.

Common Mistakes to Avoid When Using BotRefund

• Treating every unresponsive lead as fraud: Not all low-quality leads are bots. Real users may submit incomplete information or not be ready to buy, so always cross-reference BotRefund’s technical signals with your CRM outcomes before filing a refund claim.
• Pausing campaigns before preserving evidence: If you suspect invalid traffic, keep your campaigns running long enough for BotRefund to collect full session data. Pausing campaigns early can delete the attribution data you need to tie bot activity to specific ad spend.
• Submitting generic refund claims: Google and Meta reject most invalid traffic claims because they lack specific evidence. Use BotRefund’s pre-formatted reports, which include the exact click IDs, timestamps, and signal breakdowns their teams require to process claims quickly.

Frequently Asked Questions

Does BotRefund guarantee I will get a refund?

No. BotRefund provides the evidence required to support a refund claim, but final approval decisions are made by Google and Meta. Its 83% client recovery rate is based on historical claim outcomes, but individual results may vary depending on the specifics of your invalid traffic and the platform’s current policies.

How long does it take to see BotRefund flag invalid traffic?

Most users see flagged sessions within 24-48 hours of installing the tracking script and linking their ad accounts. Retroactive audits of historical campaign data can take 3-5 business days to complete, depending on the volume of traffic reviewed.

Will BotRefund slow down my website?

No. The BotRefund tracking script is lightweight (under 10KB) and loads asynchronously, so it does not impact page load speed or user experience for real visitors.

Can BotRefund detect fake leads from Meta lead forms?

Yes. BotRefund analyzes both on-site landing page sessions and Meta lead form submissions, flagging fake leads with the same 110+ signal analysis. It can also tie fake lead form submissions back to specific ad campaigns and placements to support refund claims for lead generation ad spend.

Do I need technical expertise to use BotRefund?

No. The setup process takes less than 10 minutes for most users, and BotRefund’s interface is designed for marketing teams, not developers. The tool also provides pre-built report templates and claim support for users who are new to the refund process.

How is BotRefund different from server-side bot detection tools?

Server-side tools only analyze IP addresses and request headers, which misses advanced botnets that use residential proxies or mimic real user behavior. BotRefund uses client-side behavioral analysis to capture how real users interact with your page (scroll depth, mouse movement, click timing) — signals that bots cannot easily replicate. This makes it far more accurate for identifying invalid ad traffic that server-side tools miss.