I literally spend 12 to 16 hours a day sorting through emails, looking for patterns, researching clues as to who is sending what, linking emails back to site traffic data and click stream info in order to see if I can tell which emails are working and which ones aren't and why.
I spoke to a marketer yesterday who had read my article on emails in the dating market sector (see the Dating Game) who wondered how I was able to tell when all those emails were going out. It's called deep emersion: sorting through more than a thousand emails a day, categorizing them, examining them, and researching them. It has become an obsession. As a result, I've begun to see patterns that I don't think have been seen before. This column tries to illuminate those patterns.
For example, I've been looking at some newsletters sent out on house lists by two different but competing candy manufacturers. Both companies utilize third party lists for acquisition campaigns. Both have newsletters that go out to their house lists on a monthly basis with cooking recipes, etc. Both exhibit identical spikes in traffic when they send out their newsletters. Both exhibit almost identical troughs in traffic between newsletters. And because they send out their newsletters to coincide with big events during the holiday season (Christmas, Halloween, etc), charts of their website traffic, put side by side, are almost identical.
But there is a difference, and here is where my daily immersion in email comes in handy. The delivery of company A's newsletter seems very event driven, with no regular pattern. During peak holiday time, the newsletter comes out every two weeks, followed by a two-month period before the next one appears. It is also clear that the website traffic is being driven directly from the email itself (people clicking through on the newsletter to reach the site). Each newsletter represents 65-70 percent of the traffic driven to the site the day it comes out, thus accounting for the spike. And while the peaks in traffic match company B's exactly, the trough between peaks is deeper between emails for company A and the drop-off is much steeper.
Company B's newsletter, on the other hand, comes out with a much more regular rhythm: It comes out every month and is appropriately labeled: October's Issue, November's Issue, etc. They may stagger the dates within the month that it comes out (most likely to reflect peak buying times) but the email is a steady regular pulse. And while the peaks in website traffic match the peaks of company A exactly, there is less of a drop off in traffic (although there is a drop off) between newsletter sends, and even more interesting, there is a steady increase in traffic leading up to the delivery of the newsletter. It's as if company B was staggering the release of the newsletter in order to test it out. The effect is that there seems to be an increase in interest leading up to the release of the newsletter itself, a building of excitement and anticipation that company A's newsletter lacks.
Another interesting fact, in company B's newsletter, is that it would be difficult for an objective researcher to determine that it was an email newsletter that was responsible for the increased site traffic to company B's site by simply examining the click stream data. The click stream data shows an increase in search engine traffic and that's all. Anyone without a thorough knowledge of company B's email newsletter send patterns would assume that traffic was being driven via search engine marketing techniques instead of email marketing!
What does seem to be clear is the importance of regularity in holding on to traffic and in building anticipation. It is also clear that email marketing is the most important tool in driving site traffic and keeping mindshare.
Clear that is, if you can see the patterns.