Post by huangshi715 on Feb 15, 2024 12:05:01 GMT
” abe Abe would have been an A/B testing pro. Image source. Jumping headfirst into a series of landing page tests with no data and insight is like chopping blindly away at a tree for hours will a dull ax, hoping that the tree will eventually give way to the blade and fall over. In landing page optimization, collecting data and getting insight is like sharpening the ax. We’re trying to increase our chances of being able to take down the tree in the first attempt – maybe even in the first swing. Your A/B test is only as good as your hypothesis For a long time, I thought that landing page optimization was all about conducting as many tests as possible – boy, was I wrong about that one! After years of trial and error, it finally dawned on me that that the most successful tests were the ones based on insight and solid hypotheses .
In my experience, most A/B tests fail because of the underlying test Kuwait Email List hypothesis – either the hypothesis was fundamentally flawed, or there was no hypothesis to begin with. “A/B tests fail because the hypothesis is fundamentally flawed – or nonexistent.” ContentVerve CLICK TO TWEET In landing page optimization, the test hypothesis is the basic assumption that you base your optimized variant on. It encapsulates what you want to change on the landing page and what impact you expect to see from making that change. Moreover, it forces you to scrutinize your test ideas and helps you keep your eyes on the goal.
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If we stick to the Lincoln analogy, formulating a test hypothesis is like doing a test to check whether your blade really is sharp enough to dig into the trunk and effectively cut down the tree. You can do an on-the-fly test of your optimization idea by filling in the blanks in this template: A/B Testing: Hypothesis example If the expected outcome seems way too good to be true (or simply stupid), it’s a clear sign that that your test hypothesis is too weak to have an impact in the minds of your potential customers. Working with test hypotheses provides you with a much more solid optimization framework than simply running with guesses and ideas that come about on a whim.
In my experience, most A/B tests fail because of the underlying test Kuwait Email List hypothesis – either the hypothesis was fundamentally flawed, or there was no hypothesis to begin with. “A/B tests fail because the hypothesis is fundamentally flawed – or nonexistent.” ContentVerve CLICK TO TWEET In landing page optimization, the test hypothesis is the basic assumption that you base your optimized variant on. It encapsulates what you want to change on the landing page and what impact you expect to see from making that change. Moreover, it forces you to scrutinize your test ideas and helps you keep your eyes on the goal.
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If we stick to the Lincoln analogy, formulating a test hypothesis is like doing a test to check whether your blade really is sharp enough to dig into the trunk and effectively cut down the tree. You can do an on-the-fly test of your optimization idea by filling in the blanks in this template: A/B Testing: Hypothesis example If the expected outcome seems way too good to be true (or simply stupid), it’s a clear sign that that your test hypothesis is too weak to have an impact in the minds of your potential customers. Working with test hypotheses provides you with a much more solid optimization framework than simply running with guesses and ideas that come about on a whim.