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

Usability experts play an essential role in the user interface design process by evaluating the usability of digital products from a very important perspective - the users! Usability experts utilize various techniques such as heuristic evaluation, usability testing, and user research to gather data on how users interact with digital products and services. This data helps to identify design flaws and areas for improvement, leading to the development of user-friendly and efficient products.

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Heuristic evaluation is a usability research technique used to evaluate the user interface design of a digital product based on a set of ‘heuristics’ or ‘usability principles’. These heuristics are derived from a set of established principles of user experience design - attributed to the landmark article “Improving a Human-Computer Dialogue” published by web usability pioneers Jakob Nielsen and Rolf Molich in 1990. The principles focus on the experiential aspects of a user interface. 

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In this article, we’ll discuss what heuristic evaluation is and how usability experts use the principles to create exceptional design. We’ll also discuss how usability testing works hand-in-hand with heuristic evaluation, and how minimalist design and user control impact user experience. So, let’s dive in!

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Understanding Heuristic Evaluation

Heuristic evaluation helps usability experts to examine interface design against tried and tested rules of thumb. To conduct a heuristic evaluation, usability experts typically work through the interface of the digital product and identify any issues or areas for improvement based on these broad rules of thumb, of which there are ten. They broadly cover the key areas of design that impact user experience - not bad for an article published over 30 years ago!

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The ten principles are:

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1. Prevention error: Well-functioning error messages are good, but instead of messages, can these problems be removed in the first place? Remove the opportunity for slips and mistakes to occur.
2. Consistency and standards: Language, terms, and actions used should be consistent to not cause any confusion.
3. Control and freedom for users: Give your users the freedom and control to undo/redo actions and exit out of situations if needed.
4. System status visibility: Let your users know what’s going on with the site. Is the page they’re on currently loading, or has it finished loading?
5. Design and aesthetics: Cut out unnecessary information and clutter to enhance visibility. Keep things in a minimalist style.
6. Help and documentation: Ensure that information is easy to find for users, isn’t too large and is focused on your users’ tasks.
7. Recognition, not recall: Make sure that your users don’t have to rely on their memories. Instead, make options, actions and objects visible. Provide instructions for use too.
8. Provide a match between the system and the real world: Does the system speak the same language and use the same terms as your users? If you use a lot of jargon, make sure that all users can understand by providing an explanation or using other terms that are familiar to them. Also ensure that all your information appears in a logical and natural order.
9. Flexibility: Is your interface easy to use and it is flexible for users? Ensure your system can cater to users to all types, from experts to novices.
10. Help users to recognize, diagnose and recover from errors: Your users should not feel frustrated by any error messages they see. Instead, express errors in plain, jargon-free language they can understand. Make sure the problem is clearly stated and offer a solution for how to fix it.

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Heuristic evaluation is a cost-effective way to identify usability issues early in the design process (although they can be performed at any stage) leading to faster and more efficient design iterations. It also provides a structured approach to evaluating user interfaces, making it easier to identify usability issues. By providing valuable feedback on overall usability, heuristic evaluation helps to improve user satisfaction and retention.

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The Role of Usability Experts in Heuristic Evaluation

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Usability experts play a central role in the heuristic evaluation process by providing feedback on the usability of a digital product, identifying any issues or areas for improvement, and suggesting changes to optimize user experience.

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One of the primary goals of usability experts during the heuristic evaluation process is to identify and prevent errors in user interface design. They achieve this by applying the principles of error prevention, such as providing clear instructions and warnings, minimizing the cognitive load on users, and reducing the chances of making errors in the first place. For example, they may suggest adding confirmation dialogs for critical actions, ensuring that error messages are clear and concise, and making the navigation intuitive and straightforward.

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Usability experts also use user testing to inform their heuristic evaluation. User testing involves gathering data from users interacting with the product or service and observing their behavior and feedback. This data helps to validate the design decisions made during the heuristic evaluation and identify additional usability issues that may have been missed. For example, usability experts may conduct A/B testing to compare the effectiveness of different design variations, gather feedback from user surveys, and conduct user interviews to gain insights into users' needs and preferences.

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Conducting user testing with users that represent, as closely as possible, actual end users, ensures that the product is optimized for its target audience. Check out our tool Reframer, which helps usability experts collaborate and record research observations in one central database.

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Minimalist Design and User Control in Heuristic Evaluation

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Minimalist design and user control are two key principles that usability experts focus on during the heuristic evaluation process. A minimalist design is one that is clean, simple, and focuses on the essentials, while user control refers to the extent to which users can control their interactions with the product or service.

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Minimalist design is important because it allows users to focus on the content and tasks at hand without being distracted by unnecessary elements or clutter. Usability experts evaluate the level of minimalist design in a user interface by assessing the visual hierarchy, the use of white space, the clarity of the content, and the consistency of the design elements. Information architecture (the system and structure you use to organize and label content) has a massive impact here, along with the content itself being concise and meaningful.

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Incorporating minimalist design principles into heuristic evaluation can improve the overall user experience by simplifying the design, reducing cognitive load, and making it easier for users to find what they need. Usability experts may incorporate minimalist design by simplifying the navigation and site structure, reducing the number of design elements, and removing any unnecessary content (check out our tool Treejack to conduct site structure, navigation, and categorization research). Consistent color schemes and typography can also help to create a cohesive and unified design.

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User control is also critical in a user interface design because it gives users the power to decide how they interact with the product or service. Usability experts evaluate the level of user control by looking at the design of the navigation, the placement of buttons and prompts, the feedback given to users, and the ability to undo actions. Again, usability testing plays an important role in heuristic evaluation by allowing researchers to see how users respond to the level of control provided, and gather feedback on any potential hiccups or roadblocks.

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Usability Testing and Heuristic Evaluation

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Usability testing and heuristic evaluation are both important components of the user-centered design process, and they complement each other in different ways.

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Usability testing involves gathering feedback from users as they interact with a digital product. This feedback can provide valuable insights into how users perceive and use the user interface design, identify any usability issues, and help validate design decisions. Usability testing can be conducted in different forms, such as moderated or unmoderated, remote or in-person, and task-based or exploratory. Check out our usability testing 101 article to learn more.

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On the other hand, heuristic evaluation is a method in which usability experts evaluate a product against a set of usability principles. While heuristic evaluation is a useful method to quickly identify usability issues and areas for improvement, it does not involve direct feedback from users.

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Usability testing can be used to validate heuristic evaluation findings by providing evidence of how users interact with the product or service. For example, if a usability expert identifies a potential usability issue related to the navigation of a website during heuristic evaluation, usability testing can be used to see if users actually have difficulty finding what they need on the website. In this way, usability testing provides a reality check to the heuristic evaluation and helps ensure that the findings are grounded in actual user behavior.

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Usability testing and heuristic evaluation work together in the design process by informing and validating each other. For example, a designer may conduct heuristic evaluation to identify potential usability issues and then use the insights gained to design a new iteration of the product or service. The designer can then use usability testing to validate that the new design has successfully addressed the identified usability issues and improved the user experience. This iterative process of designing, testing, and refining based on feedback from both heuristic evaluation and usability testing leads to a user-centered design that is more likely to meet user needs and expectations.

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Conclusion

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Heuristic evaluation is a powerful usability research technique that usability experts use to evaluate digital product interfaces based on a set of established principles of user experience design. After all these years, the ten principles of heuristic evaluation still cover the key areas of design that impact user experience, making it easier to identify usability issues early in the design process, leading to faster and more efficient design iterations. Usability experts play a critical role in the heuristic evaluation process by identifying design flaws and areas for improvement, using user testing to validate design decisions, and ensuring that the product is optimized for its intended users.

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Minimalist design and user control are two key principles that usability experts focus on during the heuristic evaluation process. A minimalist design is clean, simple, and focuses on the essentials, while user control gives users the freedom and control to undo/redo actions and exit out of situations if needed. By following these principles, usability experts can create an exceptional design that enhances visibility, reduces cognitive load, and provides a positive user experience. 

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Ultimately, heuristic evaluation is a cost-effective way to identify usability issues at any point in the design process, leading to faster and more efficient design iterations, and improving user satisfaction and retention. How many of the ten heuristic design principles does your digital product satisfy? 

“Dear Optimal, 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, and 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 organized 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. Card sorting is 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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Comparing tree testing and card sorting: Key differences

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Tree testing and card sorting are complementary methods within your UX toolkit, each unlocking unique insights about how users interact with your site structure. The difference is all about direction.

Card sorting is generative. It helps you understand how users naturally group and label your content; revealing mental models, surfacing intuitive categories, and informing your site’s information architecture (IA) from the ground up. Whether using open or closed methods, card sorting gives users the power to organize content in ways that make sense to them.

Tree testing is evaluative. Once you’ve designed or restructured your IA, tree testing puts it to the test. Participants are asked to complete find-it tasks using only your site structure – no visuals, no design – just your content hierarchy. This highlights whether users can successfully locate information and how efficiently they navigate your content tree.

In short:
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• Card sorting = "How would you organize this?"
• Tree testing = "Can you find this?"

Using both methods together gives you clarity and confidence. One builds the structure. The other proves it works.

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Which method should you choose?

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The right method depends on where you are in your IA journey. If you're beginning from scratch or rethinking your structure, starting with card sorting is ideal. It will give you deep insight into how users group and label content.

If you already have an existing IA and want to validate its effectiveness, tree testing is typically the better fit. Tree testing shows you where users get lost and what’s working well. Think of card sorting as how users think your site should work, and tree testing as how they experience it in action.

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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.

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Why using both methods together is most effective
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Card sorting and tree testing aren’t rivals, view them as allies. Used together, they give you end-to-end clarity. Card sorting informs your IA design based on user mental models. Tree testing evaluates that structure, confirming whether users can find what they need. This combination creates a feedback loop that removes guesswork and builds confidence. You'll move from assumptions to validation, and from confusion to clarity – all backed by real user behavior.

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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.