Comparing two versions of something with separate groups to measure which better achieves a defined outcome.
A/B testing starts with a defined question and a measurable outcome, such as whether a different campaign message increases purchases. Separate groups receive the control version and a variation, and their behaviour supplies the comparison. Standard tests change a single variable so that the result is easier to interpret. Teams then analyze performance against the chosen goal before deciding whether to adopt the variation. This differs from asking customers which design they prefer, because the evidence comes from what people actually do during the experiment.
In job listings
Holt Renfrew’s Manager, Digital Analytics & Organic Growth role in Toronto called for a test-and-learn approach to conversion rate optimization. Its responsibilities explicitly included A/B testing, analyzing results and recommending which successful initiatives to scale, connecting the experiment to a subsequent business decision. Search local jobs for “marketing”.
