CTO of customer engagement platform Customer.io, Matthew Newhook, has put together a solid explainer on how email scanners and proxies are skewing open and click data, and I think it's worth sharing here. His post, Combating automated interactions in email marketing analytics, outlines what these non-human interactions (NHI) are, how they happen, and how to detect and reduce their impact.
As you probably know by now, scanners and firewalls will often click every link and fetch every image before a message hits a user's inbox. That leads to phantom clicks and opens, giving marketers a false sense of engagement. Some platforms report this activity as real, which sends people chasing ghosts when trying to figure out what constitutes marketing success or implement if/then automation based on a user's past activity.
Newhook walks through useful approaches like filtering based on response time, checking for known user agents, and correlating behavior across sessions. These methods can help tell apart real interactions from automated ones.
If you're trying to clean up your email metrics and want to avoid making decisions based on bot activity -- especially if you've got the data and control to finesse how tracking is logged or filter that final reporting -- Newhook's article is worth your time.
CTO of customer engagement platform Customer.io, Matthew Newhook, has put together a solid explainer on how email scanners and proxies are skewing open and click data, and I think it's worth sharing here. His post, Combating automated interactions in email marketing analytics, outlines what these non-human interactions (NHI) are, how they happen, and how to detect and reduce their impact.
As you probably know by now, scanners and firewalls will often click every link and fetch every image before a message hits a user's inbox. That leads to phantom clicks and opens, giving marketers a false sense of engagement. Some platforms report this activity as real, which sends people chasing ghosts when trying to figure out what constitutes marketing success or implement if/then automation based on a user's past activity.
Newhook walks through useful approaches like filtering based on response time, checking for known user agents, and correlating behavior across sessions. These methods can help tell apart real interactions from automated ones.
This isn't new territory here. I've already tackled NHI in the context of signup forms in my post Signup Best Practices: Banning Bots and NHI. And if you missed it, Frank Rix wrote a guest blog post back in February called Newsletter Bot Clicks: Which Clicks Are Real? that looked at how NHI can lead to inflated click numbers and messy reporting.
If you're trying to clean up your email metrics and want to avoid making decisions based on bot activity -- especially if you've got the data and control to finesse how tracking is logged or filter that final reporting -- Newhook's article is worth your time.
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