Write your tasks

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Getting your tasks right is vital for gathering useful data. To select tasks, start with the test objectives that you identified earlier on in the process. These objectives will have determined the tree you are testing and they should likewise determine the tasks you set.

Tasks should cover the breadth of the tree that you present to users. There is a risk that, if you present a broad tree to users but only test a few branches, you risk confusing users, or leading them to false assumptions.

In writing your tasks, you want them to reflect how your users might naturally approach your website. You also want to make sure you don’t give the answer away by using the same language that’s in your tree labels.

How to write a task

Take the evidence you gathered when setting your objectives, and write tasks that enable you to improve each one.

For example, you work for a bank and your contact center has had a lot of calls from customers asking where to find ‘Form X’ on the website. You’d then create a task like ‘You need to complete X application, and you want to know if you can do this online’ to garner the following insights:

  • how many people actually found Form X
  • how long it took them to find it
  • the path they took, and whether or not they had to click back up the tree.

You want your tasks to mimic the thought a person might have when they visit your website. So write in a natural, plain English style, and introduce a hypothetical scenario for people to bring to mind.

So instead of writing ‘Click where you think you’d find our office’ you would write ‘You’ve booked a meeting with one of our staff, and you want to find out where to go.’ Keeping the tone conversational will make the task less ‘Click the thing’ and more ‘Find the right thing’.

Use different phrases or words than your tree
People will take whatever language clues they can get from your task to make it easier for them. So if you have the phrase ‘application form’ in your tree, don’t use that phrase in the corresponding task. Why not? Two reasons:

  1. People who select the correct destination might just be matching the phrases. Pattern matching is quite easy for humans (we’re good at it) and it doesn’t require interpreting and acting upon the task.  So if this task scores highly, you can’t infer a lot from the data.
  2. It’s very difficult to know exactly what people have in their minds when they arrive on your website and almost impossible for our labels to mimic the language each of our users have when they visit our websites. 

An example of #2 would be:

Let’s say you have a link on your website labeled ‘Application form for credit card’. And three people see that link and think ‘Yes, that’s exactly what I need!’. Those three people could have had the following three questions or tasks in their minds:

  • What do I have to do to get approved for a credit card?
  • Can I send them my information online, or do I have to call them?
  • What kind of evidence do I need so I can apply for a credit card?

Therefore express the task you intended, rather than asking users if they can find a link with the same name as the task.

Set a maximum of 10 tasks per tree test
Each task will give you data on a different part of your tree, so match the number of tasks to the parts of your tree you want to test. And, as each task is scored individually, you can have as few as one task if you have one specific part of the tree you want to test.

We recommend a maximum of 10 tasks per tree test for two reasons:

  1. More tasks might mean fewer participants complete the entire test
  2. You run the risk of people becoming too familiar with your tree, which would bias the results for your later tasks.

If you do set more than eight or so tasks, we recommend selecting the option to randomize the order the tree is presented to people. Then for each task, people will see the labels arranged in a different order.

You can also randomize the order in which tasks are presented to participants. Generally, we recommend this option is ticked for most studies as it reduces the effect of learning part of the tree, at least spreading this bias out across the whole tree as presented.

If you want to gather more data from a test on the same tree, you could set up a separate test and either recruit different participants, or ask the same people after some time has passed.

The suggested task limit means that larger trees – for example, government or educational websites, might need to be tested in parts. In order to limit the size of the tree being tested to something appropriate, you can either trim off branches at the top level, or trim off lower branches and leaves. 

Selecting correct destinations

Tree testing helps you discover if people can find information in your website structure, so every task needs at least one correct destination. If you’re tree testing a large website, particularly one that’s been updated haphazardly over the years, you’ll probably find that information is repeated on different pages, and so you’ll need to select more than one correct destination. Go through the tree thoroughly to make sure you get them all.

You cannot select category labels (or ‘parent nodes’) as correct destinations because although we do want to know if our category labels make sense, we ultimately want to know if people can navigate the labels to find the right information. You will be able to check if your category labels help or hinder your participants finding the correct destination in the analysis breakdowns , such as ‘First click’. Alternatively, trim the lower branches and leaves off your tree and see if users can correctly identify category labels as being the ‘answers’ for your tasks.

The destinations selected by your participants can give you good insight into the effectiveness of your information architecture and show you areas that need improvement. For example, if you find there’s a high percentage of incorrect destinations it means that participants are feeling confused or unsure of where to find what you’ve asked them to look for. 

If there’s a high percentage of correct destinations, that shows that your tasks and tree are well understood.