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In the digital age, where attention is a scarce and precious commodity, companies face the challenge of standing out among the avalanche of messages flooding email inboxes. In this context, email-marketing stands as a powerful tool to reach potential customers, but how do you ensure that your emails are opened and read? The answer lies in business experimentation and the intelligent use of AB Testing.
In this article, we will focus on understanding a use case of experimentation in companies and, in particular, on a challenge for marketing teams: improving the open rate of their email messages.
AB Testing, or A/B testing, is a simple technique that seeks to bring to the business world the logic used by the sciences to build knowledge and distinguish what works to move a target variable.
Based on hypotheses that we will generate about what may or may not be interesting or attractive to our customers, we will seek to identify causal relationships that will allow us to find explanatory variables ("email subject styles" for example) that will help us to impact an independent or "explained" variable (open rate).
We will use a treatment group (a sample of customers who will see a modified version of our e-news) and a control group (who will continue to see the usual version). The experiment will address both groups simultaneously, paying attention to several points to ensure the reliability of the result we obtain.
First, we will ensure that the samples are comparable with each other (i.e., that they are not "biased"), that they meet the minimum statistically required sizes for the result to be reliable.
At the same time, we will try to ensure that both participate in the experiment simultaneously, so as to avoid the effects of "extraneous variables", or that if there are any, they affect both groups equally. At the end of the day, what we want to see is whether, all else being equal, the intervention we are testing (e.g. a different email subject line between the two groups) generates significant differences in performance on the target metric (e.g. email open rate), ignoring any other changes.
Contrary to the notion of "testing for the sake of testing", AB Testing requires a well-constructed hypothesis behind each tested variation. In the context of email marketing, this involves testing different elements of the email, such as subject line, content, call to action, among others, and analyzing the results to make informed decisions.
Now that you understand the power of AB Testing, it's time to put it into practice. Here are some ideas for experiments you could try in your email marketing campaigns:
Email subject: Test different subject lines to see which one generates a higher open rate. For example, you could test a more descriptive approach versus a more creative or emotional one.
Email content: Experiment with the length and tone of email content. Does your audience respond best to short, direct emails or do they prefer more detailed and descriptive content?
Call to action (CTA): Vary the text and design of your CTA to see which version generates more clicks. For example, you could try different action verbs or eye-catching colors to highlight your CTA.
Timing: Try different times of the day and days of the week to send your emails. Is your audience more receptive in the morning, at lunchtime or in the evening? Experiment to find out.
Imagine a fictitious company, an online clothing store called "ModaExpress," that wants to increase the open rate of its promotional emails. The company's hypothesis is that a more personalized approach in the email subject line could generate a higher response from subscribers. To test this hypothesis, ModaExpress conducts an experiment where it sends two versions of its promotional email: one with a generic subject line ("Unmissable offer at ModaExpress!") and one with a personalized subject line that includes the subscriber's name ("[Subscriber Name], discover your exclusive offer at ModaExpress!").
After analyzing the results of the experiment, ModaExpress discovers that the version with the personalized subject line has a 20% higher open rate than the generic version. As a result, the company decides to implement the use of personalized subject lines in its future email-marketing campaigns to increase interaction with its customers.
In a world where competition for consumer attention is fierce, business experimentation becomes indispensable to stand out in the marketplace. Through AB Testing in email marketing, companies can identify which approaches and strategies generate the best results and continuously optimize their campaigns to maximize target audience engagement.
It is important to note that many email-marketing platforms offer integrated AB Testing functionalities, making it easy to implement experiments without significant additional costs. Are you ready to start experimenting and take your email-marketing campaigns to the next level?
The attention of your potential customers is out there, waiting to be captured! With MyBusinessLab as your ally, you are in a privileged position to stand out and succeed in the competitive world of email marketing.
Write us in our contact form ("Let's get started" section) and let's start together to create your experimentation program!
Buenos Aires, Argentina. 2024