Predictive Marketing

Email Campaigns and Customer Engagement

Many marketers pay a lot of attention to email stats such as opens, click-throughs, unsubscribes, etc. When you have embarked on a campaign that involves multiple emails to the same list, one of the factors you should consider is how deeply you are engaging the universe of customers that you are trying to reach.

For example, it’s typical to see open rates that decline by a fairly constant percentage on each effort, such as 20% on the first email, 15% on the second, 11% on the third, and so forth. If you were to graph the percentage of opens as a function of the number of emails sent to this customer list, it would look something like this:

multiple-emails-to-same-list-graph-v2The question now is, how many unique names on this list have actually opened the email? Or, equivalently, what percentage of the list (i.e. market) has been engaged?

At one extreme, the minimum penetration would be 20%. In other words,after the first email, no one new is opening the email. The opens on the second through fifth efforts are coming from an ever decreasing proportion of the orignal 20% who opened the first email.

At the other extreme, the maximum penetration would be 61%. In this case, the prospects opening the email are different on every effort. No one opens any of the five emails twice.

As you might expect, the actual percentage of penetration is somewhere in between. Upon examination of the email addresses from the prospect list that opened up each email, the actual cumulative penetration is 33%. The penetration after each effort looks like this:

cumulative-market-penetrationSeveral facts are clear from this. First, the 33% penetration is a lot closer to the lower of the two extremes. Second, it is clearly approaching a limit. Finally, the limit in this case, judging from the trend of the graph, is surely less than 40%, even if additional efforts are made.

There are a number of ways to use this information:

  • To keep emailing this list with essentially the same subject lines, “from” field, etc. is like beating your head against a wall. Instead of continuing to beat your head against a wall, you can test new subject lines to try and motivate new prospects to open the emails.
  • If additional information about the prospects is available, by statistically analyzing the differences in the characteristics of those who opened the emails versus those who didn’t, a predictive model can be developed to devise a messaging strategy to motivate those who didn’t open the email to open subsequent emails with the new message.
  • This information can also be used in predicting which third party lists will be most responsive and in devising messaging strategies for other lists with similar characteristics.
  • Even if you find that you can’t penetrate this list (or market) any further, you can save time and money by not trying to do so.

These graphs may look very different for your lists. But the information you need to analyze a major list in this way is readily available, and you can use it to your advantage.

Predictive Marketing