How to Run Effective A/B Testing

Apr 6, 2015

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How to Run Effective A/B Testing

A/B Testing – another tool in your marketing toolbox.

Before we get started on the guidelines for how to run effective A/B testing, it’s important to first confirm what A/B Testing is. An A/B test is when you slightly alter a portion of one of your marketing tools – emails, newsletters, call-to-actions, etc. You look at the results to see which of the two versions performs better with your audience. Then, you use that winning test going forward. 

To make A/B Testing effective, though, there are a number of guidelines to follow. For instance, you don’t want to change too many portions of the marketing tool. Instead, conduct only one test at a time so that you know exactly which piece of change caused an increase of engagement and interaction with your audience.

Keep reading for more guidelines on how to run effective A/B testing. 

How to Run Effective A/B Testing

Run one test at a time.

For the same reason as testing only one variable at a time, you also only want to run one test at a time. Make it easy on yourself – with only one test to monitor, you won’t get confused, making it easier to keep the results straight.

Try testing minor details.

Which color is more powerful? Should you use different imagery? Running A/B tests on minor details like these can have major impact.

A/B test an entire element.

Compared to testing minor details, this one is huge. Try testing two completely different emails. You’ll see major differences when you run A/B tests like this, so try starting with these types of tests before running the minor details tests.

Set up a control.

Run tests against the control so that you always have something to compare the results to.

Run tests at same time.

Timing is everything. Run the tests at the same time, or your results might simply be the result of a better time of day, month of the year, day of the week, etc.

A/B split your audience randomly.

You need equally-sized, randomly selected audiences to run A/B tests. The sample sizes can be whatever size you’d like (but not too small: at least 1000 participants is best), but make sure that they are equal.

 

There’s a number of different ways to run A/B tests – ranging from manually selecting participants and running the test yourself, to automating some of the grunt work with a platform like HubSpot. Got more questions? Contact us here. 




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