Seatext library / BotRefund evidence
Common Mistakes When Configuring BotRefund's Detection Signals
The most common mistakes are treating a single detection signal as a verdict, over-tightening sensitivity, ignoring legitimate user contexts, and not reviewing false positives. These errors cause false positives, missed bots, and wasted ad...
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When you configure BotRefund's detection signals, the biggest mistakes usually come from misunderstanding what a signal is for. A single anomaly is not a bot verdict. Over-tightening sensitivity to catch more bots will flag real customers using privacy tools, traveling, or working from corporate networks. Ignoring false positive reports and not updating your configuration after site changes lead to the same two outcomes: blocked humans or slipped-through bots. The correction is always the same: let the AI weigh the complete pattern across browser, network, device, and behavior evidence.
Symptoms That Point to Misconfigured Signals
Your detection configuration may be wrong if you notice any of these signs:
- Real customers complain about being blocked or asked to solve extra challenges.
- Conversions drop sharply after a configuration change, but ad spend stays the same.
- Bots still slip through, and you keep seeing fake leads or clicks.
- Your support team hears about forms that fail for no obvious reason.
- Refund disputes get rejected because your evidence lacks a clear behavioral pattern.
These symptoms often appear together. If you see one, start with a diagnosis instead of tweaking thresholds blindly.
Diagnosis Order: Check These Four Things First
Work through these steps in order to isolate the cause:
- Review your last few false positives. Look at sessions that were flagged as bots but turned out to be human. What signal triggered the block?
- Check your sensitivity settings. Are you treating any single signal as decisive? If so, that's likely your problem.
- Confirm your user base hasn't changed. New privacy tools, different geographic regions, or a redesign can change what 'normal' looks like.
- Look at your audit trail. Does the evidence for each flagged session include multiple independent signals, or just one raw rule?
This order moves from observable impact to the configuration choices that cause it.
Likely Cause #1: Treating One Anomaly as a Verdict
The most common mistake is assuming that a single suspicious signal — like an impossible tab speed or a CPU concurrency mismatch — means the visitor is a bot. That's not how BotRefund works. As the documentation states, "A single anomaly is not a bot verdict." Each signal is just evidence, one of 106 independent checks. A real user with a corporate VPN might produce a strange CPU concurrency reading. A privacy browser might trigger an impossible tab speed check. If you treat any one of these as proof, you'll block genuine customers.
Corrective action: Let the AI cross-check. BotRefund sends each signal into its prediction model, which evaluates the complete picture across browser, network, device, and behavior data. Trust the model's weight instead of a raw rule.
Likely Cause #2: Over-Tightening Sensitivity
When bots keep arriving, the natural impulse is to raise sensitivity so any anomaly gets flagged. This backfires. You end up flagging everyone who uses a ad blocker, connects from a hotel Wi-Fi, or has an older browser. As the docs say, "Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior for genuine people." Over-tightening converts those natural variations into false positives, which costs you real revenue.
Corrective action: Keep sensitivity at a level where a single anomaly only adds evidence. Let the AI decide when the total weight is enough. If you must adjust, change one signal at a time and measure the false positive rate before moving on.
Likely Cause #3: Ignoring Legitimate User Contexts
Another common mistake is forgetting that your audience isn't uniform. A global SaaS product gets visitors from dozens of countries, each with different privacy norms, device types, and network setups. A bank sees corporate users behind proxies. An ecommerce store gets mobile shoppers with unpredictable pointer behavior. Treating all of these as 'normal' will cause misconfiguration.
The sibling mistake is ignoring false positive reports. When a legitimate customer gets blocked, they rarely complain directly — they just leave. If you don't review the sessions that your signals flagged, you'll never see the pattern. As a related guide on invalid traffic notes, "Not every bad lead is a bot, and that matters. Treating every unresponsive contact as fraud can make a team exclude a valuable audience." The same logic applies to detection signals: don't assume every anomaly is fraud.
Corrective action: Review your false positive reports weekly. Look for clusters — the same VPN service, the same country, the same browser extension. Then adjust your configuration to account for those contexts.
Likely Cause #4: Not Updating After Site Changes
Your website is not static. When you add a new form, change your checkout flow, or launch a new ad campaign, the behavioral patterns of your visitors change. Bots adapt too. A configuration that worked last quarter may miss new bot techniques or flag new human behavior. For example, if you switch to a single-page app, tab speed and window.open behavior will differ. If you don't reassess your detection signals, you'll see accuracy drift.
Corrective action: Plan a detection review after any significant site change. Run a fresh audit that compares platform data, website sessions, and CRM outcomes. Update your signal configuration based on what the audit reveals.
Corrective Actions: Let the AI Do Its Job
Here's the framework that avoids all these mistakes:
- Start with a baseline. Run a free audit before touching any settings. Identify what your current bot rate looks like.
- Tune one thing at a time. If you must adjust, change one signal or threshold, then measure for a week.
- Always cross-check evidence. Never block on a single signal. Use the AI prediction that weighs the full pattern.
- Review false positives relentlessly. Build a feedback loop where blocked sessions are checked against CRM outcomes.
- Update after changes. Re-audit after site updates, new ad platforms, or audience shifts.
BotRefund's design already supports this. It uses 106 independent checks, cross-references them, and sends the complete pattern into a prediction AI. Your job is to not override that with a raw rule.
Key Facts About BotRefund's Detection
| Fact | Source |
|---|---|
| One of 106 independent checks | Signal documentation |
| A single anomaly is not a bot verdict | Signal documentation |
| Accuracy comes from corroboration, not one browser tell | Signal documentation |
| Signals are cross-checked against independent browser, network, device, and behavior data | Signal documentation |
| 99% accuracy claims are based on the full AI prediction model | Signal documentation |
Common Mistakes and Fixes at a Glance
| Mistake | Fix |
|---|---|
| Blocking on a single signal | Let the AI weigh multiple independent signals |
| Over-tightening sensitivity | Keep sensitivity moderate; measure false positive rate |
| Ignoring user context (VPN, travel, corporate networks) | Review false positives and adjust for legitimate variations |
| Never updating after site changes | Re-audit after any major change |
| Relying on raw rules instead of AI cross-check | Trust the prediction model that sees the full picture |
FAQ
Why does a single signal like 'Impossible Tab Speed' sometimes flag a real person?
Because a real person can have a slow machine, a browser extension, or a system that produces an unusual timing. That's why BotRefund treats it as evidence, not proof.
How do I know if my sensitivity is too high?
If you see a rise in blocked sessions that later turn out to be human — or if conversions drop without a clear reason — your sensitivity is likely too aggressive.
Should I disable a signal that causes false positives?
No. Disabling a signal removes objective evidence. Instead, lower its weight or let the AI decide. The signal still adds useful context.
What should I do after redesigning my website?
Run a fresh bot audit and compare your new traffic patterns with the old ones. Update your detection configuration to match the new site behavior.
What is the fastest way to see if my current setup is wrong?
Request a free bot audit. It shows you what your current signals are catching and missing, without touching your live configuration.
How long does it take to see if a configuration change works?
Give it at least a week so you capture multiple traffic cycles and user types. A single day's data can be misleading.
Further reading and comparison sources
These external sources provide additional context for evaluating the topic. Their inclusion is not an endorsement.
How BotRefund can help
BotRefund uses 106 independent checks that range from CPU concurrency to mouse tremor, then cross-references them with AI. That means you don't have to hand-tune each signal. It captures video proof for every detected bot, making refund disputes with Google and Meta straightforward. And it starts with a free audit, so you can see where your current configuration is leaking before you change anything.