Tree Testing is a qualitative research method used to evaluate how easily people can find information within a content hierarchy.
Rather than asking participants to create structure (as in card sorting), tree testing examines how users navigate an existing structure to locate specific items - and why they choose the paths they do.
In Lookback, tree testing focuses on reasoning during search, not just success or failure.
What tree testing helps you understand
Tree testing helps answer questions such as:
Where do users expect information to live?
Which labels guide or mislead navigation?
How confident or uncertain are users as they search?
Where does hesitation or backtracking occur?
The goal is not simply to measure whether something can be found, but to understand how people reason through your hierarchy.
Tree testing vs card sorting
These two methods are related, but serve different purposes:
Card sorting explores how users organize content
Tree testing evaluates how users search within an existing structure
Tree testing is typically used:
after an initial structure exists
when validating or refining navigation
when disagreements exist about “where things belong”
Think-Out-Loud is essential
Tree testing without narration only tells you what happened.
Tree testing with Think-Out-Loud tells you why it happened.
In Lookback, participants are encouraged to:
explain what they expect to find under each heading
verbalize uncertainty or confidence
talk through why they choose one path over another
This turns navigation paths into interpretable qualitative evidence.
How tree testing works in Lookback (conceptually)
Lookback does not provide a built-in tree-testing interface.
Instead, researchers typically:
use a text-based or prototype-based hierarchy hosted elsewhere
observe participants navigating that structure during a Lookback session
This allows Lookback to:
record screen and audio
capture reasoning and hesitation
stream sessions live for immediate analysis
generate findings tied to specific moments in the search process
The value lies in what is observed and explained, not in the tool used to host the tree.
Moderated and unmoderated tree testing
Tree testing can be run in different ways depending on intent.
Moderated tree testing
Best when you want to:
probe reasoning in real time
ask why a label felt right or wrong
explore alternative paths live
The researcher can gently prompt narration when needed.
Unmoderated tree testing
Best when you want to:
reach more participants
compare patterns across groups
test multiple tasks asynchronously
Clear prompts and Think-Out-Loud instructions are essential.
AI moderation can help prompt clarification when responses are incomplete.
In both cases, the evidence model is the same.
Example task prompts
Effective tree-testing prompts focus on search intent, not correctness.
Examples:
“If you needed to contact customer support, where would you start? Talk through what you’re looking for.”
“Where would you expect to find our services agreement? Explain why you chose that path.”
“If you were looking for job openings at our company, where would you go first?”
“Where would you expect to find case studies? What made that section feel right to you?”
Evaluating tree testing results qualitatively
When reviewing sessions, look beyond success rates.
Consider:
Did participants expect the item to exist where it did?
Which labels caused hesitation or reinterpretation?
Where did participants backtrack - and why?
Did different participants use similar reasoning, even when choosing different paths?
Patterns in reasoning often matter more than identical paths.
How tree testing fits with other methods
Tree testing often works best alongside:
card sorting (to explore alternative structures)
Think-Out-Loud usability testing (to see structure in use)
follow-up interviews (to reflect on expectations)
Because all evidence lives at the Project level, insights remain connected across methods.
Key takeaway
Tree testing in Lookback is not about proving a structure “works.”
It is about understanding how people search, interpret labels, and reason through hierarchy - and capturing that reasoning as evidence you can share, revisit, and act on.
Where this fits
This article explains the method.
For setup instructions, tooling examples, and participant wording, see:
Setting Up & Running Studies
Templates & Assets