The first thing we want to know is how the tree did in general:
Not surprisingly, the most important thing to look at is success rate – how many participants chose the correct answer, across all tasks?
Most tools will give you this as a rating out of 10 or 100. For example, a score of 69 means that 69% of the time, participants chose a correct answer:
Once you see a tree’s overall success rate, the natural question is “Well, is that good, bad, or just average?”
As any consultant will tell you, it depends. Mainly, it depends on two things:
But we do need to start from somewhere. In our experience, over hundreds of tree tests, the following rough markers have emerged for trees of average size and complexity:
| A high score doesn’t mean “no revisions needed”. We’ve never run a tree test where everything worked so well that we couldn’t improve it a bit more. There are always a few lower-scoring tasks that suggest further improvements. |
What the overall success rate doesn't tell you is how much the success rate of the individual tasks varied. For example, a 60% overall score may mean that all tasks hovered around 60%, or that half your tasks were 90% and half were 30%. To find out, you need to drill down to the task level - see Task success rate later in this chapter.
People are often surprised that we consider 65+ to be a "good" score. Shouldn't the bar be set at 80 or 90?
Effective trees don't usually score higher than 80 because we're testing a top-down text-only tree with no other aids. Our participants are making choices without the benefit of:
Once we refine our text tree to be effective (i.e. perform well in tree testing), we should then be able to add these other design elements to further improve the findability of items in our website.
| In our experience, success rates from the final website tend to be 20-30% higher than the scores we see in tree testing. |
Lisa Fast at Neo Insight has written an ~interesting blog post comparing tree-test scores to usability-test scores. Here's a graph showing what she found:
To get a general idea of the effectiveness of your tree, it also helps to look at how directly your participants found the right answer. Did they go straight there, or did they have to try a few different paths first?
How this is scored depends on the tool you’re using:
While the overall directness score gives you a rough idea of how clear and distinguishable your headings are, you’ll need to drill down to specific tasks to determine where the most backtracking happens. For more on this, see Directness – where they backtracked later in this chapter.
Most tree-testing tools show you the average (or median) time taken by your participants to complete the tree test.
If you’re testing several trees against each other, and the trees are approximately the same size (in breadth and depth), you can compare these overall times to see if some trees are “slower” than others. This suggests that participants either had to:
This is a very rough measure, however, and to make sense of it, you’ll need to drill down to see which tasks (or specific areas of the tree) are slowing down your participants. For more on this, see Task speed - where they slowed down later in this chapter.
A more practical use for the average time taken is making sure that your tree test is not taking too much of your participants’ time.
In general, we recommend an overall duration of 5 minutes for a tree test. This is typically how long it takes the average participant to do 8-10 tasks (our recommended amount) for a medium-size tree (200-500 items).
If you have a larger tree, your test time may exceed this, but we still recommend that you keep it under 10 minutes to avoid participant fatigue and boredom.
If your average duration is longer than this because you are asking each participant to do a lot of tasks (say, 12 or more), you are likewise inviting participant fatigue and boredom. More importantly, your results may be skewed by the “learning effect” – see How many tasks? in Chapter 7.
Some tools present a single overall score, combining several measures: success rate; directness; speed; and so on. This overall score typically uses some kind of weighting, with success rate usually being the biggest factor.
This is useful when testing trees, because it makes us consider more than just the success rate itself. If people can find items in our tree, but they have to do a lot of backtracking, or they have to ponder each click, there’s something wrong and the score should reflect that.
Note that the various online tools differ in how they calculate their overall score, making it harder to compare scores between tools:
Next: Analyzing by task