Don’t test that! Recognizing when A/B testing won’t help
We all want to be more data-driven, and A/B testing can be a powerful tool in our data arsenal — when it’s done effectively. Other times, it can suck up your time, distract from other tactics, or even mislead your team with inconclusive or misunderstood results. Come learn what makes an effective A/B test, how to tell ahead of time whether your test is likely to give you useful insights, and how best to share and use the results of your tests.
- Understand what kinds of tests will produce useful results
- Tell whether your test results are significant
- Anticipate when a test won't be useful, or may even be detrimental