What are bot clicks in email, and should you filter them?

Email bot clicks are automated visits to links in your message. Security software can generate them while checking link safety. Filter identified bot events when measuring recipient engagement, but check the scope: reporting, revenue attribution, segments and automation need separate verification. A bot click attached to a customer does not make that customer a bot.

Before rewriting an email because its clicks look strange, establish what the click records represent. The useful question is which events should influence your next decision about the customer.

Why can an email record clicks nobody intended?

Security systems can inspect links without the recipient choosing to visit them. A recorded click can therefore reflect a safety check rather than interest in a product. The University of Colorado’s practitioner guidance describes artificial link activity and recommends looking for a further action, such as a registration or web activity.

Keep this separate from privacy-inflated email opens. A privacy open and an automated link click are different observations; moving from opens to unfiltered clicks does not establish human engagement.

Investigate patterns rather than assigning a bot label from the headline rate:

These are prompts for investigation, not proof that a fast click is automated. Adobe’s documented detection methods use two separate approaches: a match against the IAB user-agent/IP list, or closely timed activity for the same lead and email asset. Either or both can be enabled. Do not turn one provider’s detection rule into a universal time cutoff.

Trace selected suspicious events to any available follow-through. If that evidence is unavailable, record the gap. An absent site visit or purchase alone does not prove that a click was automated.

Should you remove the click or the subscriber?

Filter the identified event before deciding what the person’s activity means. A subscriber can have both automated and human clicks: Klaviyo’s bot-click documentation explicitly describes that mixture on one profile. Deleting everyone associated with a bot click would treat those two cases alike.

Consider one invented send with 10,000 delivered messages. At the same observation cutoff, assume every click has a bot or non-bot label and the recipients fall into these mutually exclusive groups:

Define the rate here as unique clickers divided by delivered messages. Before filtering, it is 800 ÷ 10,000 × 100 = 8%. After removing bot-labelled events and deduplicating the remaining recipients, it is 300 ÷ 10,000 × 100 = 3%: the mixed group still belongs with the non-bot clickers.

Subtracting everyone with any bot-labelled click gives the wrong result: 800 − 600 = 200. It discards the 100 mixed recipients too. This is an event-versus-person error, not evidence that fewer people received the email.

The labels in this example are assumed inputs, not perfect detection in a real account. Have the reporting tool filter and deduplicate events; document how it treats missing classifications. Keep suppression or deletion as a separate contact decision supported by the person’s other available activity.

What does turning on bot filtering actually change?

Bot filtering changes which recorded clicks are included wherever that filter applies. Check reports, revenue attribution, segment conditions and click-triggered automation separately. A cleaner campaign report is not enough evidence that a segment or follow-up uses the intended event definition.

Run this audit for the send you are investigating:

There are concrete reasons to check these separately. The same bot-click guide says Klaviyo custom reports include bot clicks by default, with Bot Click = false available to exclude them. It also documents that condition for click-based segments. Do not assume a dashboard setting has answered either question.

For attribution, the settings guide gives account menu → Settings → Attribution. Its email-bot-click exclusion covers reporting and attribution, and saved changes update attribution historically. The guide says settings can take up to 36 hours to apply. The model-comparison tool offers a preview, but its sent-date snapshot may differ from actual reports. Preserve the earlier report before applying a change; use the email-sales reconciliation guide if the remaining question concerns revenue credit.

In Mailchimp, the documented control is Analytics → Analytics settings (or Bot filtering on) → Bot activity toggles. Its guide says the setting applies across account reporting rather than per report, and filtered historical email-click data starts August 10, 2025. Check the period before comparing an older export with a current view.

Logging and filtering can also be different operations. Adobe’s guide distinguishes those choices and exposes bot properties in its documented email-click/open filters and triggers. Inspect the actual downstream rule instead of taking the word “enabled” as acceptance.

Make settings changes outside an active comparison where possible. Klaviyo specifically advises against turning on the bot-click setting while a live campaign or fixed-date flow experiment is running. A lower rate after changing the measurement rule does not, by itself, establish weaker creative.

What should you save before closing the investigation?

Keep a short record someone else can reproduce:

• Scope: message, audience, report, observation cutoff and delivered denominator.
• Counting: unique recipients or total events; classifier and filter settings; historical coverage and missing-label treatment.
• Customer decisions: the segment, attribution rule or follow-up examined, including how mixed activity is handled.
• Resolution: supporting event details, available subsequent activity, owner and any test still unverified.

Close with a specific result: the change is explained by filtering, suspicious activity remains unresolved, or a downstream rule needs repair.

Frequently asked questions

Does a filtered click rate count only real people?

Treat it as activity retained by the provider’s classifier. Mailchimp describes identifying most bot activity, rather than guaranteeing perfect detection. Keep the filter definition with the reported rate.

Should you suppress a customer who has bot clicks?

A bot-labelled click alone is not enough. The same profile can also have human clicks. Review the person’s other available activity and make the contact decision separately.

Does a click without a purchase prove bot activity?

No. Check event details and whatever follow-through is observable, and record missing evidence. Absence of a purchase alone does not establish who clicked.