
404 - Why Most Email Marketing A/B Tests Are a Waste of Time (And How to Run One That Actually Works)
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A client once asked me to A/B test the timing in one of their abandoned cart flows. On paper they had plenty of data - a few thousand people entering the flow every month. But once I mapped out how the flow was actually built, I knew that test would run for weeks, potentially months, and tell us absolutely nothing at the end.
That's the trap most people don't see coming. A/B testing sounds scientific and rigorous, but most email marketing tests are set up in a way that makes a real answer essentially impossible. The list gets sliced too thin. The hypothesis is vague. Two versions of a subject line get tested once and someone calls it a win. One result is basically a coin flip wearing a lab coat.
Noah Kagan, founder of AppSumo, has said only about one in every eight of their tests actually produces a significant result. So if your tests keep coming back inconclusive, you're in very good company. That doesn't mean you shouldn't test. It means you should set up tests so they have a real shot at telling you something.
In this episode I walk through the traps that make most A/B tests a waste of time, how to write a hypothesis that actually means something, and the framework for running a test that gives you a real answer - or at least a useful one.
✨ In this episode, you'll learn:
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The flow A/B test trap that looks like plenty of data until you map out where people actually go
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Why knowing when a test can't answer your question is just as valuable as running one that can
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Why one subject line result on its own is basically meaningless - and what to test instead
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The types of questions that are actually worth testing consistently over time
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How to write a hypothesis using the if-then-because framework before you set anything up
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Why you should only ever change one thing at a time - and why it feels slower but matters
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How to pick your success metric before you hit send - and why open rate isn't always the right one
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How many people each version actually needs to see before a result means anything
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Why flow tests can take weeks or months - and why you should never edit a live test
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What to do with inconclusive results - and why a tie is sometimes the most useful answer
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How one client's three-way sign-up form test saved them money even though all three versions performed the same
Most A/B tests don't fail because the idea was bad. They fail because they were never set up to answer a question in the first place.
Work with Joy Joya: https://joyjoya.com
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