Hi there,

To give some context to the analysis metrics, it is important to understand the context of the demo data.

The hypothetical company, Panoply Technologies, is a vendor of time and event management software. To boost sales they have decided to run an event in New York, and have built a marketing campaign to deliver this. One of the key elements of this is an email marketing campaign. An initial target list was developed from a segmented group created in CRM (screenshot below). Given that the event is being held in New York, the event organiser was able to create a group in CRM based on prospects whose companies are in the NY area (Territory: US East), thereby increasing the likelihood of them attending. 

 

 

Moving to the "E-marketing Wave Activity Analysis" tab (screenshot below) you will see a range of metrics you can use to assess how this campaign performed. Each link brings up its own list of data which can then be used to create new groups (instant segmentation). This data can also be exported to either excel or PDF format for further analysis.

The starting point for your analysis is the "E-marketing Results Summary" line which gives you an instant overview. Of the 36 people on the initial target list, 34 mails were submitted, 31 sent and 5 bounced. There were 27 unique opens and 34 unique clicks, with 68 clicks in total. Let's take a look at some metrics.

 

Opens & Clicks

"Opens & clicks" are the first major indicator of campaign performance (see screenshot below). This report provides you with details about how your email recipients interacted with your email and gives you unique insight into your customers' behaviour and interest.

 

The use case for this metric starts with looking at the relationship between the opens and clicks:

  • High volume of opens/Low volume of clicks - a repeated number of opens suggests that there is an interest in the email content, and that your subject line appears to be successful. However the low number of clicks would suggest that the next step in the process - that of driving behaviour by bringing the customer from the email to the company's own platform - isn't as successful. Check your links and decide if you need to "hold back" some information from the email core content to produce the desired behaviour.
  • High volume of opens/High volume of clicks - this is an engaged reader who could now be termed a prospect. Follow up/retention emails are essential now to keep this person's attention and bring them to a further step in the process - "consideration" of your offer.
  • Low volume of opens/High volume of clicks - this recipient may be a typical email "scanner" who is pressed for time. The subject line is bringing the person to the core content, but he/she is not coming back to the mail more than once or twice. Given the high number of clicks, this individual may become a prospect; however specific segmentation based on the first email's content will be required to engage this person further.
  • Low volume of opens/low volume of clicks - the fact that this person has opened the mail at least once points to some level of interest (and therefore you could have a good subject line). However on opening, this person may realise the content isn't that interesting or relevant. You should try a further segmented mail to see if this behaviour changes - if they move to the "unopened" metric, then it may be time to clear them off your list.

The next action is to "create new group" based on the various profiles above. Use the filtering button on the right to define your open/click ratio before creating the group.

 

Opens by time and unique opens by time

These 2 metrics are related, however have been separated out to improve the usability of the reporting feature.

  • The opens by time screen (below) lists each individual time that a user has opened your mail. So if the person has opened it 5 times, you will see 5 listings for that email address with the time recorded beside it. This is extremely useful for segmenting your data and building groups based on different time-zones or behaviour at different times of the day and week (remember - weekends are generally better for B2C, Tuesday - Thursday working hours in B2B).

 

 

  • The unique opens by time metric is essentially a summary of the above metric. Instead of having the addresses listed multiple times, there is a number beside the address. The time listed in the left column is when the email was first opened.

 

A sample use case for this combined set of metrics is as follows:

  • Click on the unique opens by time metric first and filter by "opens" to list the recipients that appear to show most interest.
  • This is a first opportunity to segment. Noting the "local time" in the left column for a selection of recipients, revert now to the opens by time metric.
  • Pick a number of sample addresses that were good prospects from the first metric. You will note that every time the email was opened is listed on the left hand side. Look at this data for patterns in the open times.
  • Consider the following questions:
    • What time was the first mail opened?
    • What time were the second and subsequent mails opened?
    • How frequently were they opened?
    • Were they opened at the same times roughly on particular days?
    • Are the repeat opens clustered, for example, around weekends?

In the next blog we will look at "Opens by email and unique opens by email" and the "Clicks by link, unique clicks by link, unique clicks by email" metrics.

 

Good luck!

David

Follow me @Sage_CRM_DavidR