First click tab

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The first click table tells you if the first node your participants selected was on the correct path to a destination. This can be useful to gauge how clear your top-level labels are to participants and how clearly they communicate a potential correct path in relation to the task. A high percentage of correct clicks here would mean the top-level labels are clear, given the tasks.

The “visited during” percentage indicates if the node was clicked on at some point in the task. A low percentage here for a correct path would correlate with an unclear top-level label, as participants have possibly come back to the home node after realizing they have gone down the wrong path.

Example of a high-scoring task

You can see in the example below, 100% of participants clicked the correct first node, i.e. the node identified as the first correct click in the correct path when the study was set up.

We can be confident that this label leads people in the right direction.

Example of a low-scoring task

Participants were nowhere near as successful with their first clicks in the example below. They were provided a task that would lead them to applying for travel insurance, and we can see that there was a lot of confusion around which path to start down.

The correct first click was Open and apply. Only 48% of participants clicked on that first, but 71% of them clicked it during the task. So what does this tell us? Over half of the participants thought they’d find information on travel insurance down various incorrect paths. However, while they initially clicked an incorrect first click option, e.g. Everyday banking, the majority of participants were able to realize they were on the wrong path and backtrack to Open and apply (71%). The top-level labels didn’t clearly indicate to participants where to go to complete this task. 

This is very useful data when deciding on the most effective label for that part of your website. As you iterate on the labels, A/B test your trees to ensure you’re improving the clarity of your first nodes with each new tree.