Email Client Monitor: Making the Most of Your Data (Part 2)

In my previous blog post I looked at some of the insights that can be derived from the data returned when using the standard Email Client Monitor pixel. As valuable and interesting as this data is, you can enrich it to include specific data tags that allow you to more closely look into your subscribers’ behaviors. For this part I am going to concentrate on what you can look at when you start using subscriber IDs and campaign IDs.

So what are subscriber IDs and campaign IDs? 
A subscriber ID is a unique reference number that is allocated to each subscriber in your email list. Similarly the campaign ID is a unique reference for each campaign you send out. These elements can be added into the Email Client Monitor pixel and will allow you to view results at a single subscriber or campaign level.

What does this actually allow you to see? Using the examples from my previous blog post, you can filter the data by campaign to see the platform usage over time to confirm whether there is a difference in how or when recipients open them.

Looking further into the data, here are some some examples of what you can do with the data using just Excel. As in my previous blog post I have split out the date/time format to allow for easier graphing of the data using a 24 hour clock.

Comparing campaign opens
In order to be able to use campaign IDs as both a legend and value within a pivot table, you will need to duplicate this data. So simply add in another column and copy it across, give it a column name of your choosing and you are now ready to pivot your data.

Set your pivot table to have:

  • Axis field = Hour
  • Legend = Campaign ID
  • Value = Copy of campaign ID

If necessary, depending on your data set, you may need to set the pivot table to fill in blank cells with 0 and allow for blank data to be shown. Personally I prefer to compare the open percentages rather than count, which you can easily get by setting the pivot table to show values as percentage of column total. Graph your table and you will be able to compare campaigns to see which performed best and whether there were any differences in the pattern of opens.

campaigncomp (1)

Individual subscriber opens by time
By repeating the previous example using the subscriber ID data instead of the campaign ID you can compare when individual subscribers are opening your mail.


In this example we can see that Subscriber 72 is an early riser, looking at their emails at 7 am and later at 4 pm, whereas Subscriber 13 prefers to read their emails during the daytime, between 9 am and 3 pm.

Subscriber opens by device
In the example above, it’s clear that either these subscribers are reading the same campaigns multiple times, or they are reading multiple different campaigns during the day. To find out which, simply change the hour value from the axis field to the campaign ID.  At this point my preference is to change the chart type to make it easier to read.


Here we can see that these particular subscribers not only read multiple different campaigns a day, but also in some cases open up the same campaign multiple times.

Picking on Subscriber 72, who is clearly very engaged, we can pick out some useful data about this individual:


  • They primarily read these campaigns in the morning and around lunch time.
  • They swap between using a an iPhone in the morning and a Windows computer at lunch.
  • Two of the campaigns they read in the morning were interesting enough to be read both in the morning and at lunch.

Reviewing these types of user interaction with campaigns can help you identify the most engaged users, and how and when they interact with your campaigns. All of which provides valuable data when making decisions about your email program.

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