Web usability guide

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 UX research we are told again and again that to ensure truly user-centered design, it’s important to test ideas with real users as early as possible. There are many benefits that come from introducing the voice of the people you are designing for in the early stages of the design process. The more feedback you have to work with, the more you can inform your design to align with real needs and expectations. In turn, this leads to better experiences that are more likely to succeed in the real world.It is not surprising then that paper prototypes have become a popular tool used among researchers. They allow ideas to be tested as they emerge, and can inform initial designs before putting in the hard yards of building the real thing. It would seem that they’re almost a no-brainer for researchers, but just like anything out there, along with all the praise, they have also received a fair share of criticism, so let’s explore paper prototypes a little further.

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What’s a paper prototype anyway? 🧐📖

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Paper prototyping is a simple usability testing technique designed to test interfaces quickly and cheaply. A paper prototype is nothing more than a visual representation of what an interface could look like on a piece of paper (or even a whiteboard or chalkboard). Unlike high-fidelity prototypes that allow for digital interactions to take place, paper prototypes are considered to be low-fidelity, in that they don’t allow direct user interaction. They can also range in sophistication, from a simple sketch using a pen and paper to simulate an interface, through to using designing or publishing software to create a more polished experience with additional visual elements.

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Showing a research participant a paper prototype is far from the real deal, but it can provide useful insights into how users may expect to interact with specific features and what makes sense to them from a basic, user-centered perspective. There are some mixed attitudes towards paper prototypes among the UX community, so before we make any distinct judgements, let's weigh up their pros and cons.

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Advantages 🏆

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• They’re cheap and fastPen and paper, a basic word document, Photoshop. With a paper prototype, you can take an idea and transform it into a low-fidelity (but workable) testing solution very quickly, without having to write code or use sophisticated tools. This is especially beneficial to researchers who work with tight budgets, and don’t have the time or resources to design an elaborate user testing plan.
• Anyone can do itPaper prototypes allow you to test designs without having to involve multiple roles in building them. Developers can take a back seat as you test initial ideas, before any code work begins.
• They encourage creativityFrom both the product teams participating in their design, but also from the users. They require the user to employ their imagination, and give them the opportunity express their thoughts and ideas on what improvements can be made. Because they look unfinished, they naturally invite constructive criticism and feedback.
• They help minimize your chances of failurePaper prototypes and user-centered design go hand in hand. Introducing real people into your design as early as possible can help verify whether you are on the right track, and generate feedback that may give you a good idea of whether your idea is likely to succeed or not.

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Disadvantages 😬

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• They’re not as polished as interactive prototypesIf executed poorly, paper prototypes can appear unprofessional and haphazard. They lack the richness of an interactive experience, and if our users are not well informed when coming in for a testing session, they may be surprised to be testing digital experiences on pieces of paper.
• The interaction is limitedDigital experiences can contain animations and interactions that can’t be replicated on paper. It can be difficult for a user to fully understand an interface when these elements are absent, and of course, the closer the interaction mimics the final product, the more reliable our findings will be.
• They require facilitationWith an interactive prototype you can assign your user tasks to complete and observe how they interact with the interface. Paper prototypes, however, require continuous guidance from a moderator in communicating next steps and ensuring participants understand the task at hand.
• Their results have to be interpreted carefullyPaper prototypes can’t emulate the final experience entirely. It is important to interpret their findings while keeping their limitations in mind. Although they can help minimize your chances of failure, they can’t guarantee that your final product will be a success. There are factors that determine success that cannot be captured on a piece of paper, and positive feedback at the prototyping stage does not necessarily equate to a well-received product further down the track.

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Improving the interface of card sorting, one prototype at a time 💡

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We recently embarked on a research project looking at the user interface of our card-sorting tool, OptimalSort. Our research has two main objectives — first of all to benchmark the current experience on laptops and tablets and identify ways in which we can improve the current interface. The second objective is to look at how we can improve the experience of card sorting on a mobile phone.

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Rather than replicating the desktop experience on a smaller screen, we want to create an intuitive experience for mobiles, ensuring we maintain the quality of data collected across devices.Our current mobile experience is a scaled down version of the desktop and still has room for improvement, but despite that, 9 per cent of our users utilize the app. We decided to start from the ground up and test an entirely new design using paper prototypes. In the spirit of testing early and often, we decided to jump right into testing sessions with real users. In our first testing sprint, we asked participants to take part in two tasks. The first was to perform an open or closed card sort on a laptop or tablet. The second task involved using paper prototypes to see how people would respond to the same experience on a mobile phone.

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Context is everything 🎯

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What did we find? In the context of our research project, paper prototypes worked remarkably well. We were somewhat apprehensive at first, trying to figure out the exact flow of the experience and whether the people coming into our office would get it. As it turns out, people are clever, and even those with limited experience using a smartphone were able to navigate and identify areas for improvement just as easily as anyone else. Some participants even said they prefered the experience of testing paper prototypes over a laptop. In an effort to make our prototype-based tasks easy to understand and easy to explain to our participants, we reduced the full card sort to a few key interactions, minimizing the number of branches in the UI flow.

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This could explain a preference for the mobile task, where we only asked participants to sort through a handful of cards, as opposed to a whole set.The main thing we found was that no matter how well you plan your test, paper prototypes require you to be flexible in adapting the flow of your session to however your user responds. We accepted that deviating from our original plan was something we had to embrace, and in the end these additional conversations with our participants helped us generate insights above and beyond the basics we aimed to address. We now have a whole range of feedback that we can utilize in making more sophisticated, interactive prototypes.

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Whether our success with using paper prototypes was determined by the specific setup of our testing sessions, or simply by their pure usefulness as a research technique is hard to tell. By first performing a card sorting task on a laptop or tablet, our participants approached the paper prototype with an understanding of what exactly a card sort required. Therefore there is no guarantee that we would have achieved the same level of success in testing paper prototypes on their own. What this does demonstrate, however, is that paper prototyping is heavily dependent on the context of your assessment.

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Final thoughts 💬

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Paper prototypes are not guaranteed to work for everybody. If you’re designing an entirely new experience and trying to describe something complex in an abstracted form on paper, people may struggle to comprehend your idea. Even a careful explanation doesn’t guarantee that it will be fully understood by the user. Should this stop you from testing out the usefulness of paper prototypes in the context of your project? Absolutely not.

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In a perfect world we’d test high fidelity interactive prototypes that resemble the real deal as closely as possible, every step of the way. However, if we look at testing from a practical perspective, before we can fully test sophisticated designs, paper prototypes provide a great solution for generating initial feedback.In his article criticizing the use of paper prototypes, Jake Knapp makes the point that when we show customers a paper prototype we’re inviting feedback, not reactions. What we found in our research however, was quite the opposite.

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In our sessions, participants voiced their expectations and understanding of what actions were possible at each stage, without us having to probe specifically for feedback. Sure we also received general comments on icon or colour preferences, but for the most part our users gave us insights into what they felt throughout the experience, in addition to what they thought.

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Further reading 🧠

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• Why You Only Need To Test With 5 Users - Nielsen Norman Group’s Jakob Nielsen explains reveals his thoughts on only using five users when conducting usability testing.
• Sketching for better mobile experiences - Lennart Hennigs explains how to sketch and why you should do it in an article for Smashing Magazine.
• Paper prototypes work as well as software prototypes -  Bob Bailey explains why he thinks paper prototypes are just as good as software prototypes for usability testing.

“Dear Optimal Workshop,I want to test the structure of a university website (well certain sections anyway). My gut instinct is that it's pretty 'broken'. Lots of sections feel like they're in the wrong place. I want to test my hypotheses before proposing a new structure. I'm definitely going to do some card sorting, and was planning a mixture of online and offline. My question is about when to bring in tree testing. Should I do this first to test the existing IA? Or is card sorting sufficient? I do intend to tree test my new proposed IA in order to validate it, but is it worth doing it upfront too?" — Matt

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Dear Matt,

Ah, the classic chicken or the egg scenario: Which should come first — tree testing or card sorting?
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It’s a question that many researchers often ask themselves, but I’m here to help clear the air!You should always use both methods when changing up your information architecture (IA) in order to capture the most information.

Tree testing and card sorting, when used together, can give you fantastic insight into the way your users interact with your site. First of all, I’ll run through some of the benefits of each testing method.

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What is card sorting and why should I use it?

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Card sorting is a great method to gauge the way in which your users organize the content on your site. It helps you figure out which things go together and which things don’t. There are two main types of card sorting: open and closed.

Closed card sorting involves providing participants with pre-defined categories into which they sort their cards. For example, you might be reorganizing the categories for your online clothing store for women. Your cards would have all the names of your products (e.g., “socks”, “skirts” and “singlets”) and you also provide the categories (e.g.,“outerwear”, “tops” and “bottoms”).

‍Open card sorting involves providing participants with cards and leaving them to organize the content in a way that makes sense to them. It’s the opposite to closed card sorting, in that participants dictate the categories themselves and also label them. This means you’d provide them with the cards only — no categories.

‍Card sorting, whether open or closed, is very user focused. It involves a lot of thought, input, and evaluation from each participant, helping you to form the structure of your new IA.

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What is tree testing and why should I use it?

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Tree testing is a fantastic way to determine how your users are navigating your site and how they’re finding information. Your site is organised into a tree structure, sorted into topics and subtopics, and participants are provided with some tasks that they need to perform. The results will show you how your participants performed those tasks, if they were successful or unsuccessful, and which route they took to complete the tasks. This data is extremely useful for creating a new and improved IA.

‍Tree testing is an activity that requires participants to seek information, which is quite the contrast to card sorting — an activity that requires participants to sort and organize information. Each activity requires users to behave in different ways, so each method will give its own valuable results.

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Should you run a card or tree test first?

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In this scenario, I’d recommend running a tree test first in order to find out how your existing IA currently performs. You said your gut instinct is telling you that your existing IA is pretty “broken”, but it’s good to have the data that proves this and shows you where your users get lost.

An initial tree test will give you a benchmark to work with — after all, how will you know your shiny, new IA is performing better if you don’t have any stats to compare it with? Your results from your first tree test will also show you which parts of your current IA are the biggest pain points and from there you can work on fixing them. Make sure you keep these tasks on hand — you’ll need them later!

Once your initial tree test is done, you can start your card sort, based on the results from your tree test. Here, I recommend conducting an open card sort so you can understand how your users organize the content in a way that makes sense to them. This will also show you the language your participants use to name categories, which will help you when you’re creating your new IA.

Finally, once your card sort is done you can conduct another tree test on your new, proposed IA. By using the same (or very similar) tasks from your initial tree test, you will be able to see that any changes in the results can be directly attributed to your new and improved IA.

Once your test has concluded, you can use this data to compare the performance from the tree test for your original information architecture — hopefully it is much better now!

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