Email and Internet Testing Needs Some Planning
In a previous post, I said that email testing didn’t have to be a monumental task for smaller lists. While that is true, the statement shouldn’t be taken to mean it is easy. Detailed analysis is necessary to get a true picture of how your campaigns are running. An integrated set of reports that takes all of your online initiatives into account is critical to make sound decisions on how to improve your metrics.
As a general rule a complete understanding of your online campaigns hinges on knowing how the numbers affect the bottom line. Here is a real life example.
Company X was running an email campaign and were fairly diligent about reviewing their results. Over the course of a few months they modified their emails and found that their open rate improved by 10% and their click rate improved by 2%. They were thrilled with the results and made the changes permanent.
For about a year after making the changes they saw decreased conversions. Fretting over the trend, they decided to go through a full campaign analysis.
I won’t describe the specific situation but as a generic idea, but here is a genericized comparison. They sent an email to a list with a revised subject line that said fill out a simple form and get $100 (a great offer). The copy was tweaked to make filling out the form a singular focus. The email generated recipient interest and open and click rate sky rocket. Then recipients were directed to a form that said, “Only available to 10-year-old’s from Peru” (It only applied to a small subset of their list). The conversion rate plummeted because they were getting clicks but it was coming from poorly suited prospects.
The in depth analysis revealed that while the email numbers improved, the landing page conversion plummeted by 50%. After understanding that their average lead was worth about four thousand dollars, they estimated that their “improvement” had cost almost one-hundred thousand dollars.
A big picture is critical while testing online campaigns. Making decisions on segments of data might improve that area but could cost a lot overall.