A free comprehensive guide for evaluating site structures
If we’re using an online testing tool, we’ll probably need to do some data clean-up before analyzing the results.
Overall scores (based on factors such as success rate, directness, and speed) give us a quick idea of our tree’s effectiveness and how it compares to other trees.
Task scores will show us where the tree is performing well or poorly.
Low task success rates show us where participants got confused, disagreed about where the answer would be found, or were lured down the wrong path by a promising (but wrong) intermediate heading.
First clicks are especially important to analyze, because they greatly affect success rates.
Finding where people backtracked helps us find evil attractors and dead ends.
Spotting areas of our tree that slowed participants shows us where subheadings need to be clarified.
Look for patterns of behavior across several related tasks.
Look for patterns of success or failure across different sections of the tree.
We can compare the results of different user groups (or other criteria) if we run separate tests for each, or if we run a single test and filter results by a corresponding survey question.