Posts Tagged ‘Email Marketing Best Practices’
Posted by Nic Winters on January 16th, 2012
If you have taken the step of including personalization in your email campaigns (even if this is limited to including the recipient’s first name, their sales rep, etc.), your goal was likely to make your emails take on a more personal tone. An additional step that may be the right fit for your email marketing strategy is personalizing the landing pages you link to within your emails.
These personalized pages could be limited to a handful of different versions of your landing page that include slightly different offers or a page that utilizes merge tokens to pull the recipient’s email address or other information into form fields.
When you go to incorporate these personalized URLs (PURLs) into your emails, you can achieve this goal using the same approach used to insert recipient first names and/or other data fields into your emails. With the personalization tokens provided within your SubscriberMail account for each data field you can personalize the URL for a hyperlink as well (inserting the token at the point within the URL where differentiation occurs to make the content of a particular data field related to the PURL pull into the link).
Contact the SubscriberMail Client Support team at support@subscribermail.com for more information regarding how you can incorporate PURLs in your email messages.
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 Dave McCue on October 18th, 2010
Emails viewed in preview pane windows—or those that extend below the fold when viewed in full—often see a lack of reader engagement with content closer to the bottom of the message. Many recipients make the decision to stay or go based on what they see near the top of a message without scrolling further, thereby missing out on potentially valuable content.
If your email messages tend to be on the longer side, including a table of contents near the top of each message is a good way to present recipients with all of the available options in your message. Through the use of HTML anchor tags, clickable items in a table of contents can lead recipients to the point in the message where the content appears, eliminating the need for any scrolling.
For example, if I have “Summer Sweepstakes” listed among my table of contents, I would link that item like this:
<a href=”#Sweepstakes”>Summer Sweepstakes</a>.
Further down in my message code, I would insert an anchor with a name attribute at the point where my Sweepstakes content begins, like this: <a name=”Sweepstakes”></a>.
This second anchor would serve as the destination point for users who click on the Sweepstakes link in my table of contents. Using this approach, recipients who don’t scroll down in the message will at least get an idea of what lies below, and may decide to engage with content they otherwise might not have seen at all.
Posted by Bill Leming on October 6th, 2010
Last month in the first of our three part series, we mentioned that there we’re always getting asked about how to judge the performance of an email marketing campaign. We’ve come to refer to these questions as The Email Big Three. The first question we addressed was “What kind of a response rate should we expect from our list?”
The second question of The Email Big Three is, “How often do you think we should send emails to our audience?” The answer is similar to our answer for the first question in that it is sufficiently vague and is best answered by the answer everyone hates, “It depends.” The answer depends on what you are trying to accomplish, and even more what your audience is expecting to receive.
Email best practices related to content demand that you send what is relevant to the recipient. When they opted in, they probably told you how frequently they would like to be sent messages. Frequency of email communication is a hot-button issue at the moment. If you don’t send often enough, your audience may forget about you, and if you send too frequently you run the risk of over-mailing, and having subscribers opt –out.
So how do you determine the optimal frequency? If possible, you can set up varying frequency preferences within your email platform. Another way is to test for optimal frequency to your list as a whole. Testing various sending patterns and analyzing their impact on open/click through rates and opt-outs, will provide you with the most insightful information on your recipients.
Regardless of what you think you know about your audience, testing will tell you straight from them how often they want to receive your email messages. The key is to test it, quantify it and then re-test it periodically to ensure the results are still valid.
If you ask your subscribers what they want, chances are they will tell you. If you don’t stay inline with what they have stated as their preference be prepared for their backlash in the form of unsubscribing, and that piece of string will probably be long enough to trip you.
Stay tuned for the final question of The Email Big Three next month!
Posted by Bill Leming on September 9th, 2010
In email marketing, there are always a lot of questions about how to judge the performance of an email campaign, and what will make it the most successful. The questions have been answered in any number of ways across the industry, but we have tended to see people with the same three questions since the birth of email marketing.
We’ve come to call them The Big Three:
1. What kind of a response rate should we expect from our list?
2. How often do you think we should send emails to our list?
3. What’s the best time of day/day of the week to send our emails?
This month we’ll tackle question one. Let’s begin by emphatically stating that there are no hard and fast rules regarding the answer to any one of these questions. The answer to question one depends upon how you define “response rate,” how the list was compiled, how it has been used /abused, how relevant the messages have been to the recipients, what performance baseline measurements exist, how many times a day/week/month/year the list has been mailed, what’s been the policy /practice re: subject lines and From addresses and about 100 other issues too numerous to list.
Currently there are no meaningful benchmarks that can be provided because there are simply too many variables at play. So unless you can definitively and accurately answer the question, “How long is a piece of string?” don’t expect anyone with any amount of integrity to answer what email response rate you should expect beyond, “It depends.” It’s simply not an answer that can easily be provided on a time-sensitive basis without performing due diligence and running a series of diagnostics.
In the future, measuring response rate will become a bit easier for those using an email service provider that has adopted the eec’s set of standardized metrics, known as the S.A.M.E Project. It will create a set of standardized email metrics that will create a common language and definition for metrics like response rate and make it easier to benchmark results. SubscriberMail will have complete adoption of the standardized metrics by December 2010. Stay tuned for part two!
