A/B testing is used to find the best marketing strategies. It is be used to test everything from website copy to sales emails. This allows you to find the best-performing version of your campaign before spending your entire budget on one that don’t work.
While A/B testing is time-consuming, its advantages are enough to offset the time investment. Proper A/B tests make a huge difference in the effectiveness of your campaign. Narrowing down & combining the most effective elements of a campaign creates a higher return on investment, lower risk of failure, & a stronger marketing plan.
A/B testing is a marketing strategy that pits two different versions of a website, advert, email, popup, or landing page against each other to see which is most effective.
A/B testing works by randomly showing two versions of the same asset (ad, website, pop-up, offer, etc.) to different users. The random part is important because this provides more accurate information without skewing the results.
One version is the “control” group, or the version already in use. The second version changes a single element. You can change multiple elements, but it does make it harder to tell what change made the difference. This is called multivariate testing (more on this later).
Decide what to test, create two versions, decide on how long to run the test, collect enough data, analyze.
If you’re running an on-site test, you’ll want to think of all the sales-related pieces of your website, and then figure out which elements to split test:
With off-site tests, you’re probably testing either an ad or a sales email. Send out two versions and then track which one converts better. Then you can adapt your structure, copy, images, or offers.
Testing more than one thing at a time, such as headlines and calls to action, is called a multi-variate test, and is more complicated to run.
You’ll also need to consider how your systems can handle split tests as well as have staff on hand able to analyze multiple results and compile the data into digestible amounts.
Multivariate testing puts a lot more on your plate at once: but it shouldn’t necessarily be avoided. If you have the right procedures in place to handle the extra workload, then go ahead – but if you want a more simplistic approach: one A/B test at a time is just fine.
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