42% of Your Email Clicks Might Be Bots. Here's How to Tell

We pulled the click report for a brand's monthly newsletter to build a follow-up call list, and ran one extra filter before handing it over. It was worth the thirty seconds: 42 percent of the recorded clicks were machines. Not subscribers, not window shoppers, but link scanners working through the email before or instead of any human. Without the filter, nearly half of the "warm leads" on that call list would have been security software.

The short answer: safety scanners click everything

Inbox providers, spam filters, and corporate security tools routinely fetch and click the links in an email to verify none of them are malicious, before the message ever reaches a human's eyes. Microsoft documents this behavior openly for Safe Links, which scans and rewrites URLs in inbound mail for Microsoft 365 organizations, and it is one product among many. From your email platform's point of view, a request to the tracking URL is a click. The scanner does not know it is polluting your marketing analytics, and your dashboard does not know the clicker had no pulse.

Klaviyo knows, mostly. It flags suspected machine activity with a Bot Click property on click events, and its help center is candid that bot clicks make profiles look more engaged than they are. But unless you have turned on Klaviyo's setting to exclude bot activity, the headline campaign metrics include them, and the flag only helps if you actually filter on it, which is the difference between reporting on what a dashboard shows and what actually happened.

Where the damage actually lands

Inflated click rates are the visible symptom and honestly the least harmful one. The real cost shows up in the decisions built on top of clicks:

1. Sales follow-up lists. A "clicked the product link" list is a warm-lead list only if the clickers are human. At a 42 percent bot share, nearly half your calls open with confusion.
2. A/B tests judged on clicks. Scanners click every link in every variant, indifferent to your subject line or copy. That is pure noise, and on a close test it is enough to crown the wrong winner.
3. Engagement segments. Profiles that "clicked in the last 90 days" stay in your engaged segment even if the only clicks were machine safety checks. Your engaged audience quietly fills with corporate mailboxes.
4. Click-triggered automations. Any flow that fires on a click event can fire for a scanner, which means follow-up messages sent because software touched a URL.
5. Deliverability reads. Bot clicks arrive in bursts right after send, which can make a mediocre campaign look like it landed brilliantly in the first hour.

How to check your last send this week

1. Build a single-metric report on Clicked Email for the campaign, filtered where Bot Click equals true, and divide by total clicks. That is your bot share; Klaviyo documents the report configuration. Expect it to vary by list composition: audiences heavy on corporate addresses scan more.
2. Look at click timestamps. Machine clicks cluster within seconds of delivery and often hit every link in the message in order. Humans click one or two links, minutes to hours later.
3. Re-pull any list you recently handed to sales with the bot filter applied, and compare sizes. The difference is the number of awkward calls you just avoided.
4. Re-check your last few A/B "winners" on human-filtered clicks or, better, on conversions. If the winner flips, the test was measuring scanner behavior.

The fix is a standing filter, not a one-off

Make human-filtered clicks the only click number that appears in reporting, and treat the unfiltered metric as plumbing. Gate click-triggered flows with a second engagement signal, such as a site visit or an open pattern consistent with a human, before anything consequential fires. And when engagement metrics drive revenue decisions, prefer signals bots cannot fake: sessions on your site, add-to-carts, orders.

If your engagement numbers feel too good

Suspiciously strong click rates are one of the classic signs that a metrics pipeline needs a per-user look. We run fixed-scope tie-out audits across email, analytics, and ad platforms that separate what machines did from what your customers did, and hand you numbers you can act on. The 42 percent figure above took one filter to expose; knowing to run the filter is the whole game.