How to Test BotRefund's Bot Detection Accuracy on Your Own Website

Yes. You can test BotRefund's accuracy on your own site with a controlled experiment. Install BotRefund, send known bot traffic and known human traffic through it, and compare BotRefund's verdict for each session against what you already know to be true. The dashboard shows which of the 106 independent checks fired and how the AI prediction weighed the complete pattern.

Setup takes about a minute, and the free bot audit requires no credit card. Run the test long enough to collect a useful sample, and score bots and humans separately so you can measure false positives and false negatives independently. Without a test, you are relying on a marketing claim rather than your own data.

What BotRefund's Detection Actually Checks

BotRefund does not rely on a single browser tell. It uses 106 independent checks across several categories: hardware and GPU fingerprinting, biometric and behavioral interactions, network and geolocation vectors, and click and session behavior.

Examples you may see in reports include the CPU Concurrency Lie check, Impossible Tab Speed, window.open Tamper, Suspicious Ports, ghost click detection, honeypot traps, robotic linear mouse movement, superhuman input speed (under 1 ms), grid-aligned movement patterns, and unnatural session durations.

Each check adds one objective fact about a visit. The model cross-checks whether the other signals support the same story, then weighs the complete pattern rather than trusting a raw rule. A single anomaly is not a bot verdict — privacy tools, travel, corporate networks, and unusual devices can all produce unexpected behavior for real people.

This nuance matters for your test. Do not expect one signal to prove a visit is a bot. Expect the overall verdict to be right, and use the per-check details to understand why it was flagged.

Prerequisites Before You Start

Before you run any test, you need four things.

• BotRefund installed. Add it to your website in about one minute. No credit card is required to start the free audit.
• Ground truth. You must know which visits are genuinely bots and which are genuinely human. Use testers you control and bots you launch yourself.
• Session correlation. A way to match each test visit to BotRefund's record, such as a URL parameter, a cookie, or a CRM contact ID.
• Enough traffic. A small test gives noisy results. Aim for at least 50 to 100 known visits per side if your site can handle it.

The most common failure is missing ground truth. If you cannot say for certain which visits were bots, you cannot measure accuracy at all.

Step-by-Step: Test BotRefund's Accuracy on Your Site

Step 1: Install BotRefund and start the free audit

Add the snippet to your site. BotRefund runs a live audit and gives you a report of bot activity. Use this as your starting baseline.

Step 2: Build a human baseline

Have a few people who know the test visit your key pages and interact naturally: scroll, move the mouse with small pauses, fill forms with realistic timing, and correct mistakes. Do not rush. Real humans produce imperfect, varied behavior.

Step 3: Send known bot traffic

Generate automated visits using the same methods fraudsters use. Common techniques include headless browsers such as Puppeteer, Selenium, or Playwright; residential proxies; autofill scripts that populate fields in under a millisecond; and interactions with hidden honeypot elements. You want realistic bots, not trivially detectable ones.

Label each bot run clearly so you can separate it from human traffic later.

Step 4: Compare verdicts against ground truth

For each visit, note BotRefund's classification. Then count four outcomes: bots flagged as bots (true positives), humans not flagged (true negatives), humans flagged as bots (false positives), and bots missed (false negatives).

Accuracy is the share of correct verdicts out of all test visits. Look at false positives and false negatives separately, because they have different consequences. A false positive blocks a real customer. A false negative lets a bot through.

Here is a hypothetical example. Send 100 human visits and 100 bot visits. BotRefund flags 98 bots correctly and 3 humans incorrectly. Accuracy is (98 + 97) / 200 = 97.5%. False positive rate is 3%. False negative rate is 2%. Use your own numbers the same way.

Step 5: Read the signal evidence

Open the dashboard or report for flagged sessions. You should see which checks fired and how they fit together. If a human tester was flagged, check which anomaly triggered it — for example, a corporate VPN can look like a network mismatch. If a bot was missed, check which signals it spoofed well.

Step 6: Cross-check with a third-party test

Use a separate bot detection page — such as deviceandbrowserinfo.com/are_you_a_bot — to confirm your test bots are actually detectable at the fingerprint level. This does not show BotRefund's verdict, but it tells you whether your bot scripts are realistic enough to be a valid test. If the third-party test flags nothing, your bots may be too simplistic to prove anything.

Key Facts About BotRefund's Detection

Common Mistakes That Ruin the Test

Terminology You Need for the Test

• Ground truth: the known, correct answer for each visit — bot or human — that you established before the test.
• False positive: a human visit that BotRefund flags as a bot.
• False negative: a bot visit that BotRefund lets through as human.
• Fingerprinting: collecting hardware, browser, network, and behavior details to identify a device or session.
• Honeypot: a hidden page element that real users never see but bots may interact with.
• Headless browser: a browser without a visible window, used by automation tools such as Puppeteer, Selenium, or Playwright.
• Residential proxy: a routing service that sends traffic through consumer IP addresses to hide proxy use.

Limitations and When This Test Doesn't Apply

The 99% accuracy figure is a claim based on corroboration. It is not a per-visit guarantee. Your traffic mix, site structure, and bot sophistication can change results, so the only way to know is to test in your environment.

The test also measures detection, not refund approval. BotRefund can prove bot clicks and negotiate with Google and Meta, but the ad platform decides whether to approve a refund request. A detected bot is not automatically a refunded click.

Privacy tools, travel, corporate networks, and unusual devices can generate anomalies for genuine people. If your audience is heavily corporate or privacy-conscious, expect more false positives. And if your site receives almost no bot traffic, a short test will look inaccurate because of noise rather than a detection failure.

Frequently Asked Questions

How long should the test run?

At least one to two days, or until you have 50 to 100 known visits per side. Low-traffic pages need more time.

Do I need to pay to test?

No. You can start with the free audit and add BotRefund without a credit card. You only pay when you move beyond the audit and into ongoing protection with a selected pricing range.

Can I see which checks flagged a visit?

Yes. The report shows the independent evidence that fired for each session, plus how the AI weighed the complete pattern. That is the best source for understanding a false positive or a missed bot.

What false positive rate should I accept?

Lower is better, but it depends on your audience. A corporate-heavy site will see more network anomalies. Aim for zero false positives on your natural human testers in a controlled test.

Will BotRefund block bots during the test?

Not by default in the audit mode. The audit measures and reports. Blocking happens in the full protection tier, where conversion events for automated signals are suppressed.

How is the 99% accuracy figure measured?

It is based on BotRefund's model evaluating the complete picture across browser, network, device, and behavior evidence. It is not derived from a single check, so your per-site accuracy can differ.