How to read marketing email statistics with bot behavior?

Suspicious opening rate? Click rate too high to be real? There are many email stats to keep in mind! Sometimes the statistics of your marketing emails seem irrelevant. This concerns all players (even our world leader colleague Hubspot) and How to read the explanations are not always clear and obvious. An overview of the context and technological solutions to avoid the statistical pollution that makes our marketers’ hair stand on end.

Background and symptoms: statistics sometimes erroneous because of robots

It may happen that on some of your sent emails, How to read you notice a hyper boosted opening rate or on the contrary much too low compared to a shoot that you have already been able to do with another email router.
Same problem for clicks, you will find that some of your contacts have the suspicious tendency (rightly so) to click on all your links in the emails.

This is indeed an abnormal behavior and a real headache for the marketer who tries to measure the evolution of the performance of his dubai email list communication. Worse still, when changing routing platforms, the statistics suddenly seem different, as if the deliverability of the email was better or worse.

dubai email list

This may be the reason, but certainly not the only one.

Indeed, more and more firewalls or antispam solutions like MailInBlack have theunfortunate tendency to  aleart news scan all emails (opening) and check the content behind each link (click) in order to prevent users from being trapped by spam or phishing attempts.
This therefore comes from a good intention for the end user,How to read but undermines the marketer’s ambitions. So, what can we do with our statistics?

Solutions to exclude bot tracking

The first solution is to exclude these robots (also called “bots”). This is sometimes possible because some are well known and have recurring identifiers (IP, etc.) and it is easy to filter them, that is to say to exclude them from the calculated statistics (clicks and openings in particular).

This can explain that (and beyond the quality of deliverability) on the same contact bridging the gap in accessibility base and with the same content, the statistics can vary from one solution to another. Indeed, the solution that blocks robots will have worse statistics (fewer openings and clicks), but this data is more accurate.
However, not all robots are identifiable, even by doing this work upstream, some slip through the net.

 

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