Analytics & Reporting

Bot & Fraud Detection

How SMLLR automatically identifies and flags suspicious or bot-generated scans in your analytics.

What is Bot Detection?

Not all scans on your QR codes come from real human customers. Some scans are generated by:
- Web crawlers and bots that automatically follow URLs on the internet
- QR scanner apps that pre-fetch URLs without an actual human scanning
- Automated testing tools used in development
- Fraudulent inflation (rare, but possible in competitive environments)

SMLLR's fraud detection system analyses each scan and assigns it a fraud score from 0 to 100. High-scoring scans are automatically flagged as suspected bots.

Signals Used for Bot Detection

SignalWhat It Means

BOT_UA

The User-Agent string matches a known bot or crawler signature

HEADLESS_BROWSER

The scan came from a headless browser (a browser run by code, not a person)

SUSPICIOUS_UA

The User-Agent string looks unusual or malformed

REPEAT_IP

The same IP address scanned the same QR code many times in a short period

RAPID_SCANS

An unusually high number of scans from the same source in seconds

IMPOSSIBLE_GEO

The scan location changed impossibly fast between consecutive scans (e.g. Mumbai then New York within 1 minute)

Excluding Bot Scans from Your Reports

Excluding bot scans gives you a cleaner picture of your real human audience and more accurate marketing insights.

1

Go to Reporting

Click Reporting in the left sidebar.

2

Find the Fraud Filter toggle

At the top of the analytics dashboard, find the Exclude Bot Scans toggle.

3

Toggle it on

When enabled, all scans with a high fraud score are excluded from the charts, summary metrics, and exports. The raw data is still stored — you are just filtering the view.

False Positives — Legitimate Scans Flagged as Bot

Occasionally, legitimate scans may be flagged — for example, a power user who scans the same menu QR code every day. If you notice a legitimate scan pattern being flagged, you can disable the bot filter temporarily to include all scans.

If you believe your account has a systematic false-positive issue, contact SMLLR support at [email protected].

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