Last week Only Influencers published my blog post, “The Mindset of Great Email Marketers.” In it I discussed the importance of leveraging data to improve your email marketing program.
Some who read the post wanted more information on what data they should be looking at. Here’s a quick article on data trees, how to create them, and how to leverage them to your advantage.
A data tree tracks the recipients’ behaviors from the send all the way through conversion. A very simple data tree example appears below.
So what does this tell us?
The overall open rate isn’t bad at nearly 26 percent, but note the differences between segments. Segment B’s open rate is nearly 50 percent higher than Segment A’s. So the subject and from line motivated both to open, but it motivated Segment B more. Something to file away for future emails sent to these groups; perhaps subject line testing should be done with Segment A to see if its open rate can be boosted.
Click-Through Rate (CTR)
The overall CTR was fine. But the variance between the segments (7.1 percent compared to 3.7 percent) is telling. The content of the message resonated much more with Segment A. Perhaps, in the future, a different approach should be tested with Segment B while keeping this same approach for Segment A.
Click-to-Open Rate (CTOR)
The CTOR supports what we saw with the CTR. Of those who opened the message, the people in Segment A were almost three times more likely to click (34.6 percent versus 12.4 percent). It’s time to separate these segments and test a different approach to Segment B.
Click to “Learn More” Page
Nearly 70 percent of email recipients needed to learn more before they were ready to buy. That’s not too surprising with a complicated or high ticket sale. But let’s keep following this group’s click stream.
Did Not Watch Video/Watched Video Click Stream
Here’s where things get really interesting. Less than a third of the people landing here watched the video. But if we follow this stream, these people clicked on “Buy Now” more than twice as often as those who didn’t watch the video (30.4 percent compared to just 12.9 percent).
And continuing on, those who watched the video completed the transaction at a higher rate (55.2 percent versus 29.5 percent).
Buy Now Click Stream
Those who clicked “Buy Now” from the email were taken directly to the first page of checkout, bypassing the landing page and its contents. They completed the buy process at a pretty good rate, but were less likely to complete the transaction than those who went to the landing page and watched the video (34.6 percent versus 55.2 percent).
This group was a little more likely to complete the buy process than those who went first to the landing page, didn’t watch the video, and subsequently started the buy process (34.6 percent compared to 29.5 percent).
Qualitative Tactics to Test Based on the Quantitative Data
So, what next? What should be tested in an attempt to improve the conversion rate?
Just for starters:
- Separate the segments for the next email send
- Do subject line testing with Segment A to see if open rate and, subsequently, conversion rate can be improved
- Test the content in the body of the email with Segment B to see if click-through rate and, subsequently, conversion rate can be improved
- Leverage the video content more, both on the landing page and in the email message
- Test making the video more prominent on the landing page in an attempt to get more people to watch it and boost conversions
- Test including a screenshot of the video in the email itself, with a link taking people to the landing page where it plays automatically, to see if this boosts conversions
- Watch the video and take notes on key messages and what might be persuading people to buy; test incorporating this information into the landing page and email content
As you dive into this data, or your data, you’ll likely have other questions about behavior; that’s fine. With your own data, just pull more data and add to your data tree to answer them.
When you have a data tree showing behavior from send through conversion it makes the analysis much easier, doesn’t it? It’s not rocket science. And it’s nothing to be afraid of, even if you don’t consider yourself “a numbers person.”
Until next time,
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