Funnels and conversion paths
Use analytics to run better A/B tests
Prepare a measurable hypothesis, choose a primary outcome and avoid common experiment traps.
Analytics can support an experiment, but a variant winning on a convenient metric is not enough. Decide what improvement means before visitors see the change.
Write a falsifiable hypothesis
Name the audience, observed problem, proposed change and expected outcome. For example, clarifying delivery cost before checkout should reduce abandonment without lowering order value. Select one primary metric tied to business value and a few guardrails such as refunds or lead quality.
Put it into practice
See your traffic, funnel steps and site performance clearly in Alexander.
Try Alexander freeCheck assignment and measurement
Make sure variants are assigned consistently, events distinguish the versions correctly and both experiences work across devices. Decide the minimum detectable effect and sample size based on baseline behaviour and the cost of a wrong decision. Avoid changing promotions or the funnel definition midway through the test.
Interpret the result honestly
Run the planned duration, account for seasonality and evaluate the primary metric with an appropriate statistical method. A directional result from a small sample can suggest a follow-up, not prove a win. Record the outcome and learnings even when the test is inconclusive, then decide whether a larger test or qualitative work is worthwhile.