Preference testing answers stated-choice questions
It is useful for concepts, names, creative, clarity and early-stage directional decisions, especially before sufficient traffic exists.
Live A/B testing measures behavior
When implemented correctly, a randomized experiment can estimate causal effects on real metrics such as conversion or retention.
Preference is not conversion
A design people say they prefer may not produce more purchases. Treat the result according to the question that was actually measured.
Use preference tests to narrow options
They are particularly useful when you have many candidate directions and only enough traffic to run a small number of production experiments.
Use both in sequence
A strong workflow can use preference evidence before launch and behavioral experimentation after launch, with each method doing the job it is suited for.
Related tools and guides
Have two options to compare?
Create a focused preference test and collect evidence before the decision gets expensive.
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