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Why A/B Testing is Necessary for Email Campaign?

Email campaigns are great when you need to retain customers, gain repeat business, and attract new customers. We all have a preset standard, wherein we try to retain old customers, offer them choices, and regain their trust.

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Why A/B Testing is Necessary for Email Campaign?

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  1. Why A/B Testing is Necessary for Email Campaign? Email campaigns are great when you need to retain customers, gain repeat business, and attract new customers. We all have a preset standard, wherein we try to retain old customers, offer them choices, and regain their trust. You know there are a set of people who want information or services from you, so you are more likely to make extra efforts in retaining these customers at any cost. Isn't it? For this reason, A/B testing is a technique we can't afford to overlook. The structure and format of the testing technique help in receiving high conversions and success sooner in an email marketing campaign. The costs incurred are also less. Hence, a well-planned A/B testing method can help you improve your ROI, increase customer interaction, and better outcomes. Let's see a few things about A/B testing that can help you in your next campaign. Why Use A/B Testing? Before we discuss anything else, let's see why you should use A/B testing:

  2. •By analyzing common trends and factors, you can receive better click-through rates and increased open-rates. •If you correctly A/B test your CTAs and website links, you can improve your conversion. This is because you can test the approach and find out the one which yields better results. Thus, better conversion. •One of the biggest benefits of A/B testing is that you can gauge the expectations of your users. This means that you can know what they are expecting, which can help in customizing your efforts in the future. •You can save money and efforts spend on wrong campaigns and elements with A/B testing. What To Test? The first step to starting A/B testing is finding out the things and elements that you want to test. For instance, you can test your subject line or the call-to-action of your email. The basic idea here to pick this element and make two copies of this element and find out which one is working better. If you are creating an email, then you can use two different types of subject lines and send it to two versions of your email list. Then, you can check the open-rate, conversion-rate, and click-through rate to find out the one that is converting. You can use this version in your following emails and to send them to the remaining audience. Here are a few things that you can test: 1. The subject line of your email 2. Preheaders 3. Time and day on which there is more conversion 4. Graphics of the email 5. Content copy of the email 6. Personalization variations

  3. 7. Call-to-actions and other buttons Before you start testing your email campaigns, you need to have a very clear idea in your mind. You should already know the element that you are willing to test and verify. Moving forward, you only need to create two drafts and observe results. Here, you can either use A and B drafts against one another. Alternatively, it is also possible to keep one as a control draft and another one a bit different. Remember to run tests simultaneously to understand the impact of timings. Don't send both the tests at different timings. Split your traffic and see variations. How to Test? Initially, you may have the urge to test campaigns on the entire list. But, here are some of the things that you should consider: •When you have a large list of users and your email provider charges as per the email address, then you should take the largest sample you can. But, ensure that you randomly choose this sample. •When you have extremely changing factors, then consider limiting your audience. You may want to test on a few hundred people first. •When you are testing a limited-period offer, then test a small batch and run on a large audience. Having a large sample gives you accurate results. Hence, when you have a choice to keep a large sample, consider that necessarily. However, don't wander away from your goal. Remember the point is to test which one is better so maintain things accordingly.

  4. Analyzing Results Once you have decided all that and made a campaign, prepare for measuring it. From the start of the campaign till the end, you need to analyze results on the following KPIs: •The open rate •The conversion rate •The click-through rate It is obvious why we are tracking these key performing indications. However, you need to cross-reference these key performing indicators. For instance, if A is giving a 10% click-through and B is giving a 20% click-through, then B should also give double conversions that A. Ideally, we believe that that should be the case. But, it maybe not. It is not necessary to receive the same conversions as your click-through rate. So, you can't make a decision solely on one KPI. In fact, other than these KPIs, you need to evaluate multiple other factors like interactions, time spent on the website, and other such factors. Collectively, these factors can help you gain a clear picture. Best Practices Let's see some best practices that help you run A/B test campaigns easily without any hassle. •Test concurrently to remove time-based factors. So, send out emails of both types at the exact same time and on the same day for better results; not a day after and not a day before. •Keeping the size of the sample large is always beneficial. When you have the budget and other verticals are matching, then use a large sample size. •Don't go by gut instinct. It doesn't work anymore. Listen to what data is saying; analyze thoroughly.

  5. •Use email marketing tools that can make A/B testing simpler and hassle-free. Conclusion Initially, utilize simpler tests with one vertical to test in each case. You can, of course, test through variations of this vertical. For instance, testing three groups for two CTAs. However, when you include more verticals like CTA and subject line both, then tests become complicated. So, to reach that stage, start with simpler tests to improve your results. When you are successful in that, move forward to more dedicated and complicated tests.

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