We’ve made some important updates to how emfluence handles bot traffic, and it’s essential to understand how these changes might impact your email metrics. While you may notice a decrease in your open and click rates, the data will now be more accurate and reliable. Read on to learn about the updates we’ve implemented and how they will improve the quality of your reporting.
Website Tracking
If a bot is identified, we will not only discount their email metrics but we also won’t count any website tracking data from them going forward. In this example, a bot that was scanning and causing a false click may have been increasing the contact’s contact score for visiting a page on your website.
Also, as part of our website tracking improvements, we’ve changed how we record the end date, or bounce, of someone leaving your site. This has increased the accuracy of that metric and dropped the number of unknowns (which were being counted as a 0-second visit, and will now be recorded as ‘NA’) considerably.
Manual Score Adjustment
While not directly related to contact scoring, we created the ability to manually alter a contact’s score by applying positive or negative points.
In the use case of a bot, if you feel a contact’s score has been inflated unnaturally, you now have the ability to lower that score manually. The reverse is also true—if a contact takes an action outside the platform you wish to award them points for, you can now do so.
Continued Improvement of Removing Bot Activity
We continue to improve and add to our process of finding, and once identified, removing bot activity. For example, emfluence currently flags, on average, between 25-30% bot clicks based on a known bot user agent which are removed from your reporting.
Landing Pages & Surveys
Similar to the changes we made to website tracking, emfluence is currently working on changes to bot-related visits to landing pages and surveys. Once completed, if visits or activity are identified as being caused by a bot, they will no longer be counted towards the contact scoring model.
