Ready for take-off: Best practices for creating and launching remote user research studies

Card sorting is an invaluable tool for understanding how people organize information in their minds, making websites more intuitive and content easier to navigate. It’s a useful method outside of information architecture and UX research, too. It can be a useful prioritization technique, or used in a more traditional sense. For example, it’s handy in psychology, sociology or anthropology to inform research and deepen our understanding of how people conceptualize information.

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The introduction of remote card sorting has provided many advantages, making it easier than ever to conduct your own research. Tools such as our very own OptimalSort allow you to quickly and easily gather findings from a large number of participants from all around the world. Not having to organize moderated, face-to-face sessions gives researchers more time to focus on their work, and easier access to larger data sets.

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One of the main disadvantages of remote card sorting is that it eliminates the opportunity to dive deeper into the choices made by your participants. Human conversation is a great thing, and when conducting a remote card sort with users who could potentially be on the other side of the world, opportunities for our participants to provide direct feedback and voice their opinions are severely limited.Your survey design may not be perfect.

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The labels you provide your participants may be incorrect, confusing or redundant. Your users may have their own ideas of how you could improve your products or services beyond what you are trying to capture in your card sort. People may be more willing to provide their feedback than you realize, and limiting their insights to a simple card sort may not capture all that they have to offer.So, how can you run an unmoderated, remote card sort, but do your best to mitigate this potential loss of insight?

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A quick look into the data

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In an effort to evaluate the usefulness of the existing “Leave a comment” feature in OptimalSort, I recently asked our development team to pull out some data.You might be asking “There’s a comment box in OptimalSort?”If you’ve never noticed this feature, I can’t exactly blame you. It’s relatively hidden away as an unassuming hyperlink in the top right corner of your card sort.

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Comments left by your participants can be viewed in the “Participants” tab in your results section, and are indicated by a grey speech bubble.

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The history of the button is unknown even to long-time Optimal Workshop team members. The purpose of the button is also unspecified. “Why would anyone leave a comment while participating in a card sort?”, I found myself wondering.As it turns out, 133,303 comments have been left by participants. This means 133,303 insights, opinions, critiques or frustrations. Additionally, these numbers only represent the participants who noticed the feature in the first place. Considering the current button can easily be missed when focusing on the task at hand, I can’t help but wonder how this number might change if we drew more attention to the feature.

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Breaking down the comments

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To avoid having to manually analyze and code 133,303 open text fields, I decided to only spend enough time to decipher any obvious patterns. Luckily for me, this didn’t take very long. After looking at only a hundred or so random entries, four distinct types of comments started to emerge.

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1. This card/group doesn’t make sense.Comments related to cards and groups dominate. This is a great thing, as it means that the majority of comments made by participants relate specifically to the task they are completing. For closed and hybrid sorts, comments frequently relate to the predefined categories available, and since the participants most likely to leave a comment are those experiencing issues, the majority of the feedback relates to issues with category names themselves. Many comments are related to card labels and offer suggestions for improving naming conventions, while many others draw attention to some terms being confusing, unclear or jargony. Comments on task length can also be found, along with reasons for why certain cards may be left ungrouped, e.g., “I’ve left behind items I think the site could do without”.
2. Your organization is awesome for doing this/you’re doing it all wrong. A substantial number of participants used the comment box as an opportunity to voice their general feedback on the organization or company running the study. Some of the more positive comments include an appreciation for seeing private companies or public sector organizations conducting research with real users in an effort to improve their services. It’s also nice to see many comments related to general enjoyment in completing the task.On the other hand, some participants used the comment box as an opportunity to comment on what other areas of their services should be improved, or what features they would like to see implemented that may otherwise be missed in a card sort, e.g., “Increased, accurate search functionality is imperative in a new system”.
3. This isn’t working for me. Taking a closer look at some of the comments reveals some useful feedback for us at Optimal Workshop, too. Some of the comments relate specifically to UI and usability issues. The majority of these issues are things we are already working to improve or have dealt with. However, for researchers, comments that relate to challenges in using the tool or completing the survey itself may help explain some instances of data variability.
4. #YOLO, hello, ;) And of course, the unrelated. As you may expect, when you provide people with the opportunity to leave a comment online, you can expect just about anything in return.

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How to make the most of your user insights in OptimalSort

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If you’re running a card sort, chances are you already place a lot of value in the voice of your users. To ensure you capture any additional insights, it’s best to ensure your participants are aware of the opportunity to do so. Here are two ways you may like to ensure your participants have a space to voice their feedback:

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‍Adding more context to the “Leave a comment” feature

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One way to encourage your participants to leave comments is to promote the use of the this feature in your card sort instructions. OptimalSort gives you flexibility to customize your instructions every time you run a survey. By making your participants aware of the feature, or offering ideas around what kinds of comments you may be looking for, you not only make them more likely to use the feature, but also open yourself up to a whole range of additional feedback. An advantage of using this feature is that comments can be added in real time during a card sort, so any remarks can be made as soon as they arise.

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‍Making use of post-survey questions

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‍Adding targeted post-survey questions is the best way to ensure your participants are able to voice any thoughts or concerns that emerged during the activity. Here, you can ask specific questions that touch upon different aspects of your card sort, such as length, labels, categories or any other comments your participants may have. This can not only help you generate useful insights but also inform the design of your surveys in the future.

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Make your remote card sorts more human

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Card sorts are exploratory by nature. Avoid forcing your participants into choices that may not accurately reflect their thinking by giving them the space to voice their opinions. Providing opportunities to capture feedback opens up the conversation between you and your users, and can lead to surprising insights from unexpected places.

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Further reading

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• "Moderated card sorts vs online card sorts — why you need both" - An article on our blog that explains some of the differences between these two methods and how to use both for great results.
• "The hidden benefits of remote research" - Kathleen Asjes writes for UX Booth and explains when to use remote research and some of its many benefits.
• "Remote research: real users, real time, real research" - A book by Nate Bolt and Tony Tulathimutte on how to design and conduct remote research studies.

Usability guru Jared Spool has written extensively about the 'scent of information'. This term describes how users are always 'on the hunt' through a site, click by click, to find the content they’re looking for. Tree testing helps you deliver a strong scent by improving organisation (how you group your headings and subheadings) and labelling (what you call each of them).

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Anyone who’s seen a spy film knows there are always false scents and red herrings to lead the hero astray. And anyone who’s run a few tree tests has probably seen the same thing — headings and labels that lure participants to the wrong answer. We call these 'evil attractors'.In Part 1 of this article, we’ll look at what evil attractors are, how to spot them at the answer end of your tree, and how to fix them. In Part 2, we’ll look at how to spot them in the higher levels of your tree.

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The false scent — what it looks like in practice

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One of my favourite examples of an evil attractor comes from a tree test we ran for consumer.org.nz, a New Zealand consumer-review website (similar to Consumer Reports in the USA). Their site listed a wide range of consumer products in a tree several levels deep, and they wanted to try out a few ideas to make things easier to find as the site grew bigger.We ran the tests and got some useful answers, but we also noticed there was one particular subheading (Home > Appliances > Personal) that got clicks from participants looking for very different things — mobile phones, vacuum cleaners, home-theatre systems, and so on:

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The website intended the Personal appliance category to be for products like electric shavers and curling irons. But apparently, Personal meant many things to our participants: they also went there for 'personal' items like mobile phones and cordless drills that actually lived somewhere else.This is the false scent — the heading that attracts clicks when it shouldn’t, leading participants astray. Hence this definition: an evil attractor is a heading that draws unwanted traffic across several unrelated tasks.

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Evil attractors lead your users astray

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Attracting clicks isn’t a bad thing in itself. After all, that’s what a good heading does — it attracts clicks for the content it contains (and discourages clicks for everything else). Evil attractors, on the other hand, attract clicks for things they shouldn’t. These attractors lure users down the wrong path, and when users find themselves in the wrong place they'll either back up and try elsewhere (if they’re patient) or give up (if they’re not). Because these attractor topics are magnets for the user’s attention, they make it less likely that your user will get to the place you intended. The other evil part of these attractors is the way they hide in the shadows. Most of the time, they don’t get the lion’s share of traffic for a given task. Instead, they’ll poach 5–10% of the responses, luring away a fraction of users who might otherwise have found the right answer.

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Find evil attractors easily in your data

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The easiest attractors to spot are those at the answer end of your tree (where participants ended up for each task). If we can look across tasks for similar wrong answers, then we can see which of these might be evil attractors.In your Treejack results, the Destinations tab lets you do just that. Here’s more of the consumer.org.nz example:

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Normally, when you look at this view, you’re looking down a column for big hits and misses for a specific task. To look for evil attractors, however, you’re looking for patterns across rows. In other words, you’re looking horizontally, not vertically. If we do that here, we immediately notice the row for Personal (highlighted yellow). See all those hits along the row? Those hits indicate an attractor — steady traffic across many tasks that seem to have little in common. But remember, traffic alone is not enough. We’re looking for unwanted traffic across unrelated tasks. Do we see that here? Well, it looks like the tasks (about cameras, drills, laptops, vacuums, and so on) are not that closely related. We wouldn’t expect users to go to the same topic for each of these. And the answer they chose, Personal, certainly doesn’t seem to be the destination we intended. While we could rationalise why they chose this answer, it is definitely unwanted from an IA perspective. So yes, in this case, we seem to have caught an evil attractor red-handed. Here’s a heading that’s getting steady traffic where it shouldn’t.

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Evil attractors are usually the result of ambiguity

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It’s usually quite simple to figure out why an item in your tree is an evil attractor. In almost all cases, it’s because the item is vague or ambiguous — a word or phrase that could mean different things to different people. Look at our example above. In the context of a consumer-review site, Personal is too general to be a good heading. It could mean products you wear, or carry, or use in the bathroom, or a number of things. So, when those participants come along clutching a task, and they see Personal, a few of them think 'That looks like it might be what I’m looking for', and they go that way.Individually, those choices may be defensible, but as an information architect, are you really going to group mobile phones with vacuum cleaners? The 'personal' link between them is tenuous at best.

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Destroy evil attractors by being specific

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Just as it’s easy to see why most attractors attract, it’s usually easy to fix them. Evil attractors trade in vagueness and ambiguity, so the obvious remedy is to make those headings more concrete and specific. In the consumer-site example, we looked at the actual content under the Personal heading. It turned out to be items like shavers, curling irons, and hair dryers. A quick discussion yielded Personal care as a promising replacement — one that should deter people looking for mobile phones and jewellery and the like.In the second round of tree testing, among the other changes we made to the tree, we replaced Personal with Personal Care. A few days later, the results confirmed our thinking. Our former evil attractor was no longer luring participants away from the correct answers:

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Testing once is good, testing twice is magic

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This brings up a final point about tree testing (and about any kind of user testing, really): you need to iterate your testing —  once is not enough.The first round of testing shows you where your tree is doing well (yay!) and where it needs more work so you can make some thoughtful revisions. Be careful though. Even if the problems you found seem to have obvious solutions, you still need to make sure your revisions actually work for users, and don’t cause further problems. The good news is, it’s dead easy to run a second test, because it’s just a small revision of the first. You already have the tasks and all the other bits worked out, so it’s just a matter of making a copy in Treejack, pasting in your revised tree, and hooking up the correct answers. In an hour or two, you’re ready to pilot it again (to err is human, remember) and send it off to a fresh batch of participants.

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Two possible outcomes await.

• Your fixes are spot-on, the participants find the correct answers more frequently and easily, and your overall score climbs. You could have skipped this second test, but confirming that your changes worked is both good practice and a good feeling. It’s also something concrete to show your boss.
• Some of your fixes didn’t work, or (given the tangled nature of IA work) they worked for the problems you saw in Round 1, but now they’ve caused more problems of their own. Bad news, for sure. But better that you uncover them now in the design phase (when it takes a few days to revise and re-test) instead of further down the track when the IA has been signed off and changes become painful.

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Stay tuned for more on evil attractors

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In Part 1, we’ve covered what evil attractors are and how to spot them at the answer end of your tree: that is, evil attractors that participants chose as their destination when performing tasks. Hopefully, a future version of Treejack will be able to highlight these attractors to make your analysis that much easier.

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In Part 2, we’ll look at how to spot evil attractors in the intermediate levels of your tree, where they lure participants into a section of the site that you didn’t intend. These are harder to spot, but we’ll see if we can ferret them out.Let us know if you've caught any evil attractors red-handed in your projects.

In 2009, Bob Bailey and Cari Wolfson published apaper entitled “FirstClick Usability Testing: A new methodology for predicting users’ success on tasks”. They’d analyzed 12 scenario-based user tests and concluded that the first click people make is a strong leading indicator of their ultimate success on a given task. Their results were so compelling that we got all excited and created Chalkmark, a tool especially for first click usability testing. It occurred to me recently that we’ve never revisited the original premise for ourselves in any meaningful way.

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And then one day I realized that, as if by magic, we’re sitting on quite possibly the world’s biggest database of tree test results. I wondered: can we use these results to back up Bob and Cari’s findings (and thus the relevanceof Chalkmark)?Hell yes we can.So we’ve analyzed tree testing data from millions of responses in Treejack, and we're thrilled (relieved) that it confirmed the findings from the 2009 paper — convincingly.

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What the original study found

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Bob and Cari analyzed data from twelve usability studies on websites and products ‘with varying amounts and types of content, a range of subject matter complexity, and distinct user interfaces’. They found that people were about twice as likely to complete a task successfully if they got their first click right, than if they got it wrong:

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If the first click was correct, the chances of getting the entire scenario correct was 87%If the first click was incorrect, the chances of eventually getting the scenario correct was only 46%

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What our analysis of tree testing data has found

We analyzed millions of tree testing responses in our database. We've found that people who get the first click correct are almost three times as likely to complete a task successfully:

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If the first click was correct, the chances of getting the entire scenario correct was 70%If the first click was incorrect, the chances of eventually getting the scenario correct was 24%

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To give you another perspective on the same data, here's the inverse:

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If the first click was correct, the chances of getting the entire scenario incorrect was 30%If the first click was incorrect, the chances of getting the whole scenario incorrect was 76%

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How Treejack measures first clicks and task success

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Bob and Cari proved the usefulness of the methodology by linking two key metrics in scenario-based usability studies: first clicks and task success. Chalkmark doesn't measure task success — it's up to the researcher to determine as they're setting up the study what constitutes 'success', and then to interpret the results accordingly. Treejack does measure task success — and first clicks.

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In a tree test, participants are asked to complete a task by clicking though a text-only version of a website hierarchy, and then clicking 'I'd find it here' when they've chosen an answer. Each task in a tree test has a pre-determined correct answer — as was the case in Bob and Cari's usability studies — and every click is recorded, so we can see participant paths in detail.

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Thus, every single time a person completes an individual Treejack task, we record both their first click and whether they are successful or not. When we came to test the 'correct first click leads to task success' hypothesis, we could therefore mine data from millions of task.

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To illustrate this, have a look at the results for one task.The overall Task result, you see a score for success and directness, and a breakdown of whether each Success, Fail, or Skip was direct (they went straight to an answer), or indirect (they went back up the tree before they selected an answer):

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In the pietree for the same task, you can look in more detail at how many people went the wrong way froma label (each label representing one page of your website):

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In the First Click tab, you get a percentage breakdown of which label people clicked first to complete the task:

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And in the Paths tab, you can view individual participant paths in detail (including first clicks), and can filter the table by direct and indirect success, fails, and skips (this table is only displaying direct success and direct fail paths):

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How to get busy with first click testing

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This analysis reinforces something we already knew that firstclicks matter. It is worth your time to get that first impression right.You have plenty of options for measuring the link between first clicks and task success in your scenario-based usability tests. From simply noting where your participants go during observations, to gathering quantitative first click data via online tools, you'll win either way. And if you want to add the latter to your research, Chalkmark can give you first click data on wireframes and landing pages,and Treejack on your information architecture.

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To finish, here's a few invaluable insights from other researchers ongetting the most from first click testing:

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• Jeff Sauro details a useful approach to running a first click test, and shares the findings from a test he ran on 13 people.
• An article on Neoinsight describes three common usability problems that first click testing can solve.
• Gianna LaPin describes a first click test she ran on Netflix, VUDU, and Hulu Plus.