Free guide
Why so many experiments are inconclusive, and what experimentation teams can do about it
Practical guidance for experimentation leaders who want every test to teach them something useful.
Optimizely's analysis of 127,000 experiments found that 12% produce a statistically significant improvement on the primary metric. For experimentation leaders, a run of flat results makes the value of testing harder to prove to senior stakeholders.
This guide shows how better experiment design gives each test a stronger chance of reaching a clear answer.
In this guide, you'll find:
01
Five design choices that decide whether an experiment reaches a conclusive result, from the metric you measure to the people you include.
02
Worked examples covering product listing pages, shared templates and below-the-fold components.
03
How strong foundations help AI and experimentation work together.
04
A pre-launch checklist your team can use on its next test.