How many of you find yourself wondering why your email campaigns show different numbers? Are you thinking context, subject lines used or even the time sent makes a difference? It sure it does. Testing your campaigns can give you answers that are beneficial in building stronger emails.
Here at Kobemail we offer resources like, A/B splits and Nth Sampling to help our clients figure out what works best for them. For those not familiar with A/B split, this is the strategy of sending two or more different emails to the same list. Usually lists are evenly divided. For instance, a list testing 2 campaigns splits would be separated 50:50 for this strategy. As for Nth Sampling, emails are only sent to a specific group. Example - you have a list of 100 users but you specify to send the variable email to every 3rd users up to the 50th users on the list.
When testing there are some suggestions you should keep in mind.
- Always have a control email. Control email needs to be analyzed under the same conditions as the group you are testing, but without the changed variable. This way you are able to quantify the effects of the unknown variable corresponding to the control.
- Send all test campaigns at the same time. Sending campaigns at different intervals will not give you accurate data.
- If you are testing reaction to different days/hour it is advisable to send out the same campaign where only day/hour will vary.
- Test one change at a time. It is always recommended to just have one modification to each email. This way you can get a clear understanding of what factors caused any performance related changes. Small adjustments, like moving around an image, can receive vast reactions.
It is always suggested to embrace testing. This way you can always figure out what works better or what does not works at all. Test often. Make it a part of your process as it is not here to back track your campaigns but to help you achieve productive emails.
Author: Adeline Zeledon
Editor: Roopal Rawani
Open rate refers to the number of recipients that opened a specific email and is measured using an embedded HTML code that requests a transparent tracking image from web servers. When a reader opens the email, the ISP used to display the email requests that image, and an ‘open’ is recorded for that particular piece of mail.
The real question is: How much emphasis should be put towards analysis of open rates as a performance metric? Due to problems regarding interpretation in relation to image files, it’s long been debated the accuracy and weight this measure should hold – and some overlook open rates as valuable statistical information.
Conflicts regarding open rates include:
- Due to the fact that an ‘open’ will only be recorded if the readers ISP is capable of displaying HTML images, if the option is turned off and the recipient chooses to only receive text-only emails there is no way to record the open rates.
- Although the e-mail has been opened by the recipient there is no guarantee that the receiver read or actively engaged with the email in anyway, all this means is that the tracking image was requested, so you must understand not everyone opening is definitely taking in the information.
- Some e-mails come equipped with a preview function in which the email is displayed automatically and therefore downloads the tracking image without the receiver ever having viewed the image or even clicked the message.
Open rates do have some positives that should not be overlooked, mainly concerning their use as indicators. How open rates change over time, for example, can tell you a great deal because changes in the open rate reflect real-time changes in your marketing efforts and show problems or successes in regards to how your campaign is running.
When using methods like A/B splits, looking at long term trends, making open rates a base for other metrics, and even comparing campaign results across multiple ISP’s the measure it quite useful.
Open rate can be an effective measure in performance analysis – just be careful how much weight you ultimately give this statistic.
Author: Caitlin Durand
Editor: Yasifur Rahman
