A/B testing is the foundation of data-driven marketing. But running effective tests requires understanding both the science and the common pitfalls.
Hypothesis Formation
Every test should start with a clear hypothesis. What do you expect to happen and why?
Statistical Significance
Understand sample sizes and significance levels. Ending tests too early leads to false conclusions.
One Variable at a Time
Test single elements to understand what drives changes. Multivariate testing requires more sophisticated analysis.
Document Everything
Maintain a testing log. Record hypotheses, results, and learnings for institutional knowledge.
Act on Results
Testing is only valuable if you implement winning variations. Create processes to deploy successful tests.
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