Archive for the ‘SubscriberMail Tips’ Category
Posted by John Reynolds on January 23rd, 2012
Reaching your Gmail subscriber’s inbox is critical. Even more important is that your message renders the way you want it to. All of it! If your HTML is more than 102 kilobytes, your email may be cut off by Gmail in mid-sentence. As an email marketer you may focus on the top half of your message, but at the bottom of your message are the tracking image used to record Opens/Renders and the unsubscribe link you need to be CAN-SPAM compliant.
Gmail will automatically clip a message if the total size exceeds 102 kilobytes. Users will see a [Message Clipped] View Entire Message link in order to download the rest of your message (see screenshot below). In Gmail’s smart phone and tablet apps, the same rules generally apply.
To fix this situation, keep your HTML code short by removing extra returns, comments and unnecessary attributes and styles. Applications like Outlook and Apple Mail will show you the size of your message if you’re looking for ways to test. You can also check your file size from an original HTML text file.
Aside from the HTML code, it is also recommended that you save your images in an optimized format. Recipients should not have to wait for the images to render on their desktop or smart phone.
Continue to test how your messages render. It is critical that your message renders properly in Gmail to avoid losing the unsubscribe link, tracking image for Opens/Renders , and any content that is displayed after 102 kilobytes.
Posted by Rob Ropars on August 26th, 2011
We’ve all heard that if you’re in marketing, in particular email marketing, you should constantly be testing to maximize results. The most common test mentioned is the ubiquitous “A/B” split test, meaning a 50/50 list split to test one variable against another (graphics, copy, offer, layout, list, time of day, day of week, etc.).
But is an A/B test all you can or should do? If you have only a few thousand or fewer emails to work with, an A/B test may be all you can do to ensure statistically reliable results. However, if your list is too small, an A/B test might not make any sense. For example, if you only have a few hundred email addresses, splitting and conducting one test will literally tell you nothing (statistically) other than directionally relevant information. Instead you may need to try to replicate the test over time, to aggregate the results and to analyze your collective data over a longer period.
The first consideration is to quantify how many email addresses you need to test to ensure you have a representative sample and more importantly, to ensure the results are reliable. There is a lot of math and science behind this topic, and fortunately a lot of math/science/statistics sites have free online tools such as this one.
You must set up the test(s) correctly (with sufficient sample sizes and assumed response rates) on the front end to ensure that results on the back end are reliable, meaning with a confidence level that you’re comfortable with (we recommend a 95% confidence level if it’s possible). Again, there are resources online to assist such as this one. The key is to avoid the common mistake of merely looking at results and assuming winners/losers based on seemingly different response rates.
Before testing, you have to identify the goal or the question you’re trying to answer. We recommend that you actually write these down and then, as briefly and concisely as possible, describe the various yardsticks you will use to determine your winner. As form follows function, the goals/objectives of the test coupled with the means to measure results should help drive copy, graphics, and/or layout to ensure the messages are properly structured and focused on whatever question you’re trying to answer..
Let’s say your goal is a higher click rate and after an A/B test you find “A” has a 2.7% CTR and “B” has 2.85%. It is a common mistake to use subtraction and declare that “B” was the winner or that “B” was only 0.15% higher and that could lead you down the path of thinking it wasn’t a significant result (i.e. a virtual “tie”). Or maybe you routinely just pick the higher percentage as the winner and run with that. Using proper percent increase/decrease calculations, we find that this is actually a 5.56% increase from “A” to “B.”
That however may or may not be statistically significant, but as you can see it’s a much larger increase than originally assumed. In order to determine if the results are statistically significant, use one of the calculators, plug in each version’s list size and the click percentage (or open percentage, or conversion rate, etc. depending on the key metric you’re analyzing) and it will instantly tell you whether this difference is enough to be reliable (with a 95% confidence level).
In this example, let’s pretend I sent “A” and “B” to a random 2,000 people each. The calculations indicate that this would not be enough of a difference to be statistically reliable. In fact, the “B” cell’s click rate would have to have been at least 3.81% in order for the difference to be reliably significant. However, if you didn’t analyze the results properly you wouldn’t know this.
The other way to ensure you’re maximizing your results is to avoid doing a full scale A/B test. If your database for an email marketing campaign is large enough (again calculate minimum sample size), you can do a different kind of split test. First, split your list 10%/90% (ensuring it’s random). Then split the 10% group in half so you have two small splits and the remaining 90%.
Deploy your test to the 10% splits, give as much time as possible for activity to occur (twenty-four hours if possible), analyze the results and then deploy the winner to the remaining 90%. That way you’ve done your best to maximize the campaign’s results without going “all in” on a typical full file A/B split.
As with gambling, learn the rules, do the math, analyze the data and place your bets. Do it right, and the odds will swing in your favor.
Posted by John Reynolds on June 21st, 2011
SubscriberMail is eager to take email marketing questions from Digital Spin readers. If you have a question that has been boggling your mind, let us help. We are looking to take some of the most interesting questions and answer them here on our blog.
Please submit yours to review@subscribermail.com. Stay tuned for follow-up!
Posted by Nic Winters on June 10th, 2011
Are you a supervisor that likes to stay informed… or perhaps you have a boss that likes to stay informed? SubscriberMail features two tools that clients can utilize to allow them or their co-workers to keep informed regarding emails deployed through their SubscriberMail account.
Clients can find both features within the Admin tab of their accounts—Message Delivery Notifications and Seed List.
If activated, The Message Delivery Notifications tool will send an email alert after your message has deployed to any email addresses you specify. This alert will include the subject line and the size of the list the message was sent to, followed by a second alert prompting the recipient(s) to log into SubscriberMail and view the reporting metrics that have been gathered.
With the Seed List function you can automatically append email addresses to the deployment list of every message sent through your SubscriberMail account – allowing others to receive a copy of each message you deploy.
Both features can provide an easy method to keep yourself or your colleagues informed regarding all of your email campaign activity.
Contact the SubscriberMail Client Support team at support@subscribermail.com for more information about how you can utilize these features and keep informed!
Posted by Nic Winters on February 17th, 2011
When collaborating with clients on their email marketing strategy, the team at SubscriberMail regularly focuses on different methods of testing email campaigns. However, testing can be fruitless without an understanding of your results!
After the deployment of a campaign, users can pull a bevy of data-rich reports that identify percentages related to clicks, renders, etc. But for users more involved in the reporting process and less involved in message creation, clearly interpreting the differences in results for A/B tests related to design or content changes can be difficult without a visual representation of the messages themselves.
To help assist those that have been outside of the in-depth design/construction of the email tests, we urge our clients to utilize our Click Overlay Report to help visualize which items in the message have generated the most click activity (as click data is displayed in callout bubbles over the actual design of the email).
Contact the SubscriberMail Client Support team at support@subscribermail.com for more information regarding how you can visualize email results with the SubscriberMail Click Overlay Report.
