Why am I seeing inflated clicks in my ESP reporting due to bot activity?

Email marketer from MarketingProfs shares that click fraud, often caused by bots, can inflate click rates. They recommend using click fraud detection software and monitoring IP addresses to identify and block suspicious activity.

Email marketer from Email Geeks shares that it’s one of those things that is here to stay, so the sooner ESPs invest in mitigation the better. Since in our space data is key.

Email marketer from Email Geeks shares that Klaviyo and other ESPs saw increased bot activity from May 15th and drop off on June 20th and confirmed this with other ESPs.

Email marketer from Stack Overflow suggests implementing a honeypot technique, which involves adding a hidden link in your email that is only visible to bots. Clicks on this link indicate bot activity and can be used to filter out the bot's other clicks.

Email marketer from Reddit shares a strategy of looking for patterns such as rapid clicks from the same IP address, clicks that occur immediately after email delivery, and clicks from known datacenter IPs to identify bot traffic.

Email marketer from Quora suggests that analyzing click patterns for common indicators can help identify bot activity. Patterns include multiple clicks in quick succession or clicks from a single IP address across numerous emails, which indicate non-human interaction.

Email marketer from Litmus shares that pre-flight testing can help you identify issues with your email design that may be causing inflated click rates, such as incorrect links or rendering problems that lead to accidental clicks.

Email marketer from Neil Patel Blog explains that inflated clicks can be due to bots and crawlers, and suggests segmenting your audience to identify and exclude bot traffic based on unusual click patterns or IP addresses.

Email marketer from G2 recommends using bot detection software that specializes in identifying and blocking malicious bot traffic on websites, which helps maintain accurate click metrics and prevent skewed analytics.

Email marketer from Email on Acid advises diagnosing false clicks by analyzing click times, user agents, and other data points. Often this includes checking if clicks are happening faster than humanly possible, which is a strong indicator of bot activity.

Email marketer from LinkedIn explains that filtering by location and device can help in identifying bot traffic by noting oddities like clicks from strange countries, or specific device details that are typically associated with bots.

Email marketer from Email Geeks says that this wave seemed to be primarily from Microsoft.

Expert from Email Geeks explains that multiple Klaviyo customers have suggested this is a new thing at Microsoft and they implemented a switch to allow users to filter those clicks out of responses.

Expert from Email Geeks shares that others have noticed an increase in Outlook domains testing links and suggests reviewing across multiple clients to see if others are seeing it on your infra too.

Expert from Email Geeks explains that bot traffic is mostly spam filters checking out your content, which is something you want. He advises against throttling traffic and suggests massaging reporting numbers to make customers happy. He clarifies that throttling clicks would likely deny content to spam filters, and upgrading infrastructure is the solution if query rates are a concern.

Expert from Word to the Wise shares that scanner clicks are detectable by their speed (occurring very shortly after delivery) and source (originating from security services). These clicks don't represent genuine engagement but are part of the automated scanning process.

Expert from Word to the Wise explains that scanner clicks are often the cause of inflated click metrics, as security systems check links for safety. This is increasing, and will likely become a standard element of email marketing.

Documentation from DataDome details how advanced bot detection technologies use machine learning to analyze user behavior patterns, browser fingerprints, and other data points to identify and block sophisticated bots that can evade simpler detection methods.

Documentation from Imperva explains their bot traffic management approach, involving behavioral analysis, reputation analysis, and advanced challenge-response techniques, to discern and manage bot traffic to ensure accurate website analytics.

Documentation from Cloudflare Docs explains that Cloudflare's Bot Management feature uses behavioral analysis and machine learning to identify and mitigate bot traffic, which can help reduce inflated click rates and improve the accuracy of analytics reports.

Documentation from Akamai Resource Hub describes several methods of bot detection including header analysis, JavaScript challenges, and behavioral analysis to distinguish between legitimate users and bots, helping to prevent inflated click rates.

Documentation from Google Analytics Help explains how to exclude bot and spider traffic from your Google Analytics reports by using the bot filtering option in the view settings, which relies on a list of known bots maintained by Google.