Using A/B Testing for Market Optimization

Using A/B Testing for Market Optimization

The success of any online marketing campaign depends on the results that it gets you. Some campaigns are very successful while others don’t do as well as you hoped. But how to do find out what works for a campaign and makes it a success and what doesn’t. Rather than just put out a campaign and hope that it does well, there is a method to test if some strategies work better than others and if a wider implementation of those strategies can get you a better outcome.

The method is called A/B testing or split testing. A/B testing allows you to compare two versions of the same marketing content and see if one performs better in generating leads and sales. A/B testing can be done at different levels on different types of media and is very effective in providing insights on how to improve a marketing campaign and ultimately increase sales.

What is A/B testing

Marketing variables like images, copy, the layout of emails, CTAs, and so on all play a role in how well a campaign does. Each of these variables can be modified or tweaked slightly and tested for better performance. When a variable is changed, it has to be tested alongside a control or the original content that has not been modified. This type of testing where one version that has a modification is tested against the control is called split testing or A/B testing.

The reason that control is maintained is so that is can conclusively be ascertained that any significant changes in the results were because of the modifications and nothing else. Sometimes, simply sending out an email at a different time of day can make a difference rather than changing the written content of the email. However, if both versions of the email are sent out at the same time of day, then it can be concluded that any changes in results are because of the modified content.

Why is A/B testing important?

A/B testing is important as it helps you to understand better what drives traffic to your website or what gets you more sales. If changing your CTA can help increase sales, then that improves your bottom line. A/B testing can put you at an advantage over your competitors just by changing the subject lines of your emails or the color of your landing page background. By testing, you, by default, start developing more effective content and more user-friendly designs that will help your business in the long-run.

How to conduct A/B testing?

These guidelines will help you when conducting A/B testing:

Test only one variable at a time: If you want to conduct a test on your email marketing campaign, then select just one variable to test against the control. For example, if you want to check if the open rate is better when an email is sent in the morning or the evening, send the same email to half your mailing list in the morning and the other half in the evening and compare the results. Don’t change the time and the subject line or content; keep the latter two the same and only change the timing.

Test each element separately: If the email is to lead to a landing page, then don’t change the timing of your email and the content of your landing page at the same time. Once you have concluded testing your email, you can then start testing variables on your landing page, like moving the content around or changing the image.

Follow the trail: Assume that you have two versions of your landing page and visitors randomly get sent to one or the other. One of them gets more conversions than the other. The next step would be to see what happens after a visitor clicks on the CTA. Does the sale happen, does the visitor furnish their contact details for a callback or the free download, or is there a bounce? Depending on what happens next, you can do further A/B testing to see if a simpler form or buying process would increase sales.

Test randomly: Very often, test results are skewed because more than one variable is inadvertently changed. To prevent this from happening, make sure that the tests are run as randomly as possible. For example, if you are sending out two versions of an email, use a program to randomly select 50% of your email list to get one version and another 50% to get the other. If one group consists of more women than men, this could affect the results depending on the product being offered. If one group have more recipients of a certain age group, it can also affect the results. You need to make sure that recipients from both groups are an even mix for your results to be more accurate.

Decide what is significant: When testing, the test media and the control media have to show a significant difference in results to be useful. You need to decide what that significant difference means for you. To begin with, you need a large enough sample group. So, if you have 100 visitors to your site per day and 50 see one version while the other 50 see the test version, how many more leads do you have to get from the test for it to be a success? If you get ten leads from the control and 15 from the test, that is not a significant difference. However, if you get ten leads from the control and 30 from the test, then you might decide to use the test version for the rest of the campaign because of the higher conversion rate.

Regardless of whether you are using A/B testing for your email campaign, landing page, CTA, or any other variable, always make sure to test a single variable at a time. Always have a control to compare your results to, and when there is a significant difference between the control and test, you know you have a winner.

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