Why your link click numbers are probably wrong

You share a link in a company Slack channel. Within seconds your dashboard shows fifteen clicks. Nobody has read anything.

This happens constantly, and most link analytics never mention it.

What is actually clicking

Every time a URL appears somewhere, automated systems fetch it:

• Preview cards. Slack, Discord, X, WhatsApp, LinkedIn, Telegram, and iMessage all fetch a link immediately to build that little card with a title and image.
• Search crawlers. Googlebot and friends follow links they discover.
• Security scanners. Corporate email gateways open links to check them for malware, sometimes every recipient's copy.
• Uptime monitors and scripts. Anything checking the link is alive.
• AI crawlers, which have grown quickly in the last couple of years.

To a naive shortener these all look identical to a person tapping a link.

Why it distorts decisions

The problem is not that the number is too high. It is that inflation is uneven.

A link shared in one busy Slack workspace might collect twenty bot fetches. The same link posted to a quiet channel collects two. Compare those two campaigns and you conclude the first performed ten times better, when the human numbers might be identical.

Email is worse. Security gateways at large companies can open every link in every message, so a newsletter to enterprise subscribers can show click rates far above reality.

How filtering works

Bots identify themselves in the user agent string, the short description a client sends with each request. Known crawlers, preview fetchers, monitors, and scripting libraries all announce themselves recognisably. Requests with no user agent at all are also suspect.

That is not perfect, since a determined bot can lie. But it catches the overwhelming majority of automated traffic, which is honest and not trying to hide.

Snipvio classifies every request this way and reports human clicks, QR scans, and bot traffic as three separate numbers. Charts and breakdowns use humans only.

Reading the rest of your data

Referrers are incomplete. Many apps strip referrer information. A large "Direct" share is normal and does not mean the data is broken.

Countries are approximate. Location comes from network-level geography, which VPNs and corporate networks distort.

Numbers will not match your website analytics. A click is someone leaving for your site; a session is someone arriving. People abandon in between, and the two tools measure different moments. Neither is wrong.

Practical advice

• Compare like with like. One channel against itself over time beats one channel against another.
• Watch trends, not absolutes. Direction is more reliable than any single figure.
• Be suspicious of instant clicks. A burst in the first seconds after posting is usually preview fetches.
• Ask your tool whether it filters bots. If it does not, treat every number as an unknown overestimate.

The goal is not perfect data, which does not exist. It is data honest enough that decisions made on it are not accidental.

Snipvio filters bots on every plan, including free.