On the fence about card sorting and why you should be using it to improve your user experience? Let’s take a look at why you should take advantage of this powerful user research method.

Simply put, card sorting can help you discover how your users think your content should be organized and categorized. Card sorting gives you insight into how people conceptualize, group and label ideas, enabling you to make confident, informed information architecture (IA) decisions.

What is card sorting?

In a card sort, participants sort cards containing different items into groups. You can use the results to figure out how to group and label the information on your website in a way that makes the most sense to your audience. 

Using card sorting at the start of your website build means you’re able to make decisions based on data, not assumptions. Being informed at the start of your website build can mean saving a lot of time later with revisions or rebuilds. Better to build something intuitive now, than be left wondering later why parts of your website aren’t working as you expect.

When should you use card sorting?

It’s best to do card sort research when you want to answer a specific, information-related question. For example, maybe you’re adding a new range of “natural products” to your Health and Beauty site. On the other hand, you may want to redesign how information is grouped together across your entire website.

Card sorting is at its most effective when you’ve got the information and detail you need but you just need guidance on how it's best (most intuitive) to organize it.

While card sorting is typically used in the early stages of the design process, when there’s no live IA, it’s also common to use the technique to make changes to a live IA down the line. 

Card sorting: A powerful way to understand users’ mental models

Card sorting is a powerful tool to understand your users and how they make sense of information when they arrive on your website. A good rule to keep in mind is that what makes sense to you and your colleagues may not make sense to your users.

Using an online card sorting tool like OptimalSort it can be useful to check in with users to understand where they think information should sit on your website. As product ranges increase or change over time, it can also be useful to undertake card sort research when updating your website. 

Let’s take a look at how card sorting might work for an e-commerce website. 

Imagine you run a health and beauty e-commerce business with an active and successful website with a vast range of products that can be grouped in many different ways. At worst the website is clunky and hard to search, making it difficult for shoppers to find the right product, quickly. At best everything is there but it doesn’t quite answer what the user is looking for. It can be incredibly powerful to have a fuller understanding of how our website is viewed from our shoppers (rather than just internally). The goal of our website should be to showcase our products in a way that makes shopping easy, quick and even intuitive.

We are introducing a full range of natural based products that include products intended for babies, children, women and men. These products have previously  been categorized by who they are intended for. But we want to know if there is a better way that these could be made available, especially with a market shift to an increased demand in  natural based products.

By doing some card sorting with OptimalSort, we gather data from users and the pattern that our audience use to group these products. Through the data analysis we have discovered that a large majority of our users would group by natural products first and then by who the product is intended for (baby, children, women, or men). Armed with this insight (amongst others) we can use it to influence our IA. Ultimately, we end up with a far more intuitive and streamlined user experience (UX).

Three ways to use card sorting

Did you know that there are multiple ways to use OptimalSort card sorting? Let’s take a look– you may be surprised.

1. Building a new website

This is by far the biggest use-case for card sorting. When looking at building a new website or making better use of an existing one, utilizing card sorting at the research stage can be insightful and informative. Seeing your website, products and/or navigation from your end user’s perspective can enlighten, inform and assist in creating an enhanced user experience.

2. Combine card sorting and tree testing

When combined with card sorting, tree testing, with Treejack, can help you to improve your navigation and give you a fuller understanding of how your website is used. Tree testing is a technique for evaluating the findability of topics on a website. It’s also commonly known as reverse card sorting and is the perfect technique to complement card sorting. After you’ve analyzed your card sorting results and transformed them into a draft IA, you can test these insights using a tree test. Using this technique, you task users with seeking as opposed to sorting. This technique aims to replicate the experience of using a website – without visual distractions.

Unlike usability testing, tree testing only focuses on the IA of your website. It makes the process of developing an IA much faster, as you can easily make refinements and tweaks without needing to get bogged down in costly redesigns. 

3. Make collaborative design decisions

You can use OptimalSort to get your team involved and let their feedback feed your designs — logos, icons, banners, images, the list goes on. By creating a closed image sort with categories where your team can group designs based on their preferences, you can get some quick feedback to help you figure out where you should focus your efforts.

Use OptimalSort to run your first card sort

Card sorting can take place in person, or online with a tool like OptimalSort. OptimalSort gives you the flexibility to conduct moderated and unmoderated card sorts online. Now, you can collect the data you need, how and when you need it. Plus it only takes a few minutes to design and launch your study. 

Not only is OptimalSort simple to use,  but it’s backed up with the strength of powerful analysis functionality. Taking the pain of trawling screeds of information, OptimalSort pulls out useful, usable insights from your card sorting data. This allows you to quickly identify common groups at a glance with comprehensive and vibrant visualizations and use this data to support design changes and recommendations. 

What is 3D Cluster View?

Part of our OptimalSort analysis is the 3D Cluster View (3DCV). While the addition of ‘3D’ may throw off red flags of being a gimmick, it’s actually entirely appropriate. 

The 3DCV basically allows you to visualize the similarity between cards as three-dimensional spatial relationships. Each point in the 3D visualization represents one of the cards from your original sort. Cards that are closer together were more frequently sorted into the same category. Likewise, when you see 2 cards that are quite far apart, they weren’t sorted together as frequently. If you’d like to find out more take a look at OptimalSort 3DCV, we think it’s pretty clever.

Wrap Up

If you’re now interested in a card sort of your own, we obviously recommend OptimalSort (which you can get started with for free). Or you want to find out more, take a look at our Card Sorting 101.

Happy sorting!

Despite the abundance of user research methods, card sorting remains one of the best ways to get into the minds of your users and discover how they understand and categorize information.

Given that one of our most popular tools is an online card sorting tool called OptimalSort (You may have heard of it), we thought that we’d answer some common questions about online card sorting – the research method that OptimalSort uses.

Let’s begin!

1. What’s the difference between online and offline card sorting?

Traditional card sorting can be done using paper cards and hosted in-person, hence “offline”. Online card sorting is pretty much what it sounds like: a card sort hosted over the internet. But there’s a little more to it.

Primarily, running a card sort online as opposed to hosting one in person means that the process becomes much easier to facilitate. Instead of needing to schedule a time for your participants to come into an office, you can simply send them a link to your card sort. Then, they can complete the test in their own time.

Note that the very benefits of online card sorting mean that you can lose certain insights gained from an in-person card sort, like understanding why your participants sort cards in a certain way. There are ways around this, however. For example, you could pair your card sorting tool with an online video recording solution.

2. When should you run an online card sort?

Card sorting is best suited to answering specific, information-related questions. For example, maybe you want to rearrange the layout of your magazine? Or perhaps you need to add several new shopping categories to your website.

In the latter example, card sorting is the perfect technique to find out where people would commonly expect to find those categories on your website. In the card sort, you present participants with a list of cards containing the names of items within certain categories and task them with sorting those items into groups that make sense to them. The end result? You have a clear picture of how your users or customers would arrange the content on your website.

Card sorting is useful when you’ve got the information you need to organize, but you’re just not sure how to organize it.

3. Do I need to compensate participants for taking part in my card sort?

Compensation is tricky when it comes to online testing methods like card sorting. While there are no hard and fast rules, you may find that it’s the best way to incentivize people to take part in your study. Now, taking part in an online card sort is much easier than trekking across town to sit down for a user interview, so you may want to offer participants the chance to win a prize for taking part instead of compensating them directly.

Note: Offering a discount for your product or service is a great way to compensate users and encourage the use of your product. 

4. How do I make sense of the data?

Most card sorting tools offer powerful analysis functionality built right into the tool itself, so all you have to worry about is actually putting the card sort together, sending out the links and promoting it.

Using OptimalSort as an example, let’s take a look at some of the analysis functionality and why it’s useful. Other card sorting tools will likely have different analysis options available.

• Participants Table: Review all of the people who took part in your card sort and segment or exclude them.
• Participant-Centric Analysis (PCA): See the most popular grouping strategies as well as the alternatives among those people who disagreed with the first strategy.
• Dendrograms: Quickly spot popular groups of cards and get a sense of how similar or different your participant’s card sorts were.

5. Is online card sorting expensive?

Online card sorting tools can be expensive, but it’s all relative. As just one example of this, online research platforms mean that you’ll likely be gaining access to a whole host of other tools by signing up for an online card sorting tool.

There’s also the fact that it’s a cheaper exercise overall than in-person card sorting as you won’t have to pay as much for compensation, or even use as much of your own time. Time is money!

6. Can I still get qualitative insights from an online card sort?

You can draw qualitative insights directly from the results of an online card sort, but you can also use online card sorting tools alongside participant recording software to build a more holistic understanding. By using recording software, you’ll be able to watch participants as they complete a card sort, and ask them to talk through what they’re doing to learn why they placed cards in a certain way. 

7. How many participants do I need?

In a nutshell, a larger number than you’d probably bring in for a user interview. Aim for between 20 and 30 participants.

Card sorting (whether it’s performed online or offline) is what’s known as a generative user testing method. This means that you’re typically starting without a design, and you’re using the method to get an idea of how people think with regards to the problem you’re trying to solve. A good example of this would be that you’re building a new website, and are using card sorting to learn how people think the content should be grouped and arranged.

Here’s a great quote from Nielsen Norman Group: “There is great variability in different people’s mental models and in the vocabulary they use to describe the same concepts. We must collect data from a fair number of users before we can achieve a stable picture of the users’ preferred structure and determine how to accommodate differences among users”.

8. How many cards should I use?

We recommend aiming for between 30 and 60 cards, as per our comprehensive 101 guide. Why? Because:

• People will be more likely to complete your card sort.
• You’ll only be able to include the most relevant cards, and be forced to discard the rest.
• You’ll get enough useful data and insights to make informed decisions about your website, app or project.

9. What online card sorting tools are available?

There are a number of online card sorting tools available, including our very own OptimalSort, which is one of the tools included in our platform. OptimalSort has a number of useful features to make it easy to set up and run a card sort with participants based all over the world. Once you’ve gathered all of your responses, built-in analysis features can then help you make sense of the data.

Of course, there are other options available. Take a look at this tools map from User Interviews for a comprehensive overview of the major research tools.

10. What do I do after a card sort?

With your card sort done and dusted, it’s time to take that data and build a draft structure of your website or mobile app. Once you’ve put this rough structure together, you can use tree testing to to see how people navigate through it. We’ve got a guide for that too, which you can read here.

Wrap-up

So that’s 10 questions about online card sorting – answered! If you’re interested in diving straight into a card sort of your own, we obviously recommend giving OptimalSort a try (which you can do for free).

Happy testing!

Cards have been created, sorted and sorted again. The participants are all finished and you’re left with a big pile of awesome data that will help you improve the user experience of your information architecture. Now what?Whether you’ve run an open, hybrid or closed card sort online using an information architecture tool or you’ve run an in person (moderated) card sort, it can be a bit daunting trying to figure out where to start the card sort analysis process.

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About this guide

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This two-part guide will help you on your way! For Part 1, we’re going to look at how to interpret and analyze the results from open and hybrid card sorts.

• In open card sorts, participants sort cards into categories that make sense to them and they give each category a name of their own making.

• In hybrid card sorts, some of the categories have already been defined for participants to sort the cards into but they also have the ability to create their own.

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Open and hybrid card sorts are great for generating ideas for category names and labels and understanding not only how your users expect your content to be grouped but also what they expect those groups to be called.In both parts of this series, I’m going to be talking a lot about interpreting your results using Optimal Workshop’s online card sorting tool, OptimalSort, but most of what I’m going to share is also applicable if you’re analyzing your data using a spreadsheet or using another tool.

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Understanding the two types of analysis: exploratory and statistical

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Similar to qualitative and quantitative methods, exploratory and statistical analysis in card sorting are two complementary approaches that work together to provide a detailed picture of your results.

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• Exploratory analysis is intuitive and creative. It’s all about going through the data and shaking it to see what ideas, patterns and insights fall out. This approach works best when you don’t have the numbers (smaller sample sizes) and when you need to dig into the details and understand the ‘why’ behind the statistics.

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• Statistical analysis is all about the numbers. Hard data that tells you exactly how many people expected X to be grouped with Y and more and is very useful when you’re dealing with large sample sizes and when identifying similarities and differences across different groups of people.

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Depending on your objectives - whether you are starting from scratch or redesigning an existing IA - you’ll generally need to use some combination of both of these approaches when analyzing card sort results. Learn more about exploratory and statistical analysis in Donna Spencer’s book.

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Start with the big picture

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When analyzing card sort results, start by taking an overall look at the results as a whole. Quickly cast your eye over each individual card sort and just take it all in. Look for common patterns in how the cards have been sorted and the category names given by participants. Does anything jump out as surprising? Are there similarities or differences between participant sorts? If you’re redesigning an existing IA, how do your results compare to the current state?If you ran your card sort using OptimalSort, your first port of call will be the Overview and Participants Table presented in the results section of the tool.If you ran a moderated card sort using OptimalSort’s printed cards, now is a good time to double check you got them all. And if you didn’t know about this handy feature of OptimalSort, it’s something to keep in mind for next time!The Participants Table shows a breakdown of your card sorting data by individual participant. Start by reviewing each individual card sort one by one by clicking on the arrow in the far left column next to the Participants numbers.

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From here you can easily flick back and forth between participants without needing to close that modal window. Don’t spend too much time on this — you’re just trying to get a general impression of what happened.Keep an eye out for any card sorts that you might like to exclude from the results. For example participants who have lumped everything into one group and haven’t actually sorted the cards. Don’t worry - excluding or including participants isn’t permanent and can be toggled on or off at anytime.If you have a good number of responses, then the Participant Centric Analysis (PCA) tab (below) can be a good place to head next. It’s great for doing a quick comparison of the different high-level approaches participants took when grouping the cards.The PCA tab provides the most insight when you have lots of results data (30+ completed card sorts) and at least one of the suggested IAs has a high level of agreement among your participants (50% or more agree with at least one IA).

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The PCA tab compares data from individual participants and surfaces the top three ways the cards were sorted. It also gives you some suggestions based on participant responses around what these categories could be called but try not to get too bogged down in those - you’re still just trying to gain an overall feel for the results at this stage.Now is also a good time to take a super quick peek at the Categories tab as it will also help you spot patterns and identify data that you’d like to dive deeper into a bit later on!Another really useful visualization tool offered by OptimalSort that will help you build that early, high-level picture of your results is the Similarity Matrix. This diagram helps you spot data clusters, or groups of cards that have been more frequently paired together by your participants, by surfacing them along the edge and shading them in dark blue. It also shows the proportion of times specific card pairings occurred during your study and displays the exact number on hover (below).

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In the above screenshot example we can see three very clear clusters along the edge: ‘Ankle Boots’ to ‘Slippers’ is one cluster, ‘Socks’ to ‘Stockings & Hold Ups’ is the next and then we have ‘Scarves’ to ‘Sunglasses’. These clusters make it easy to spot the that cards that participants felt belonged together and also provides hard data around how many times that happened.Next up are the dendrograms. Dendrograms are also great for gaining an overall sense of how similar (or different) your participants’ card sorts were to each other. Found under the Dendrogram tab in the results section of the tool, the two dendrograms are generated by different algorithms and which one you use depends largely on how many participants you have.

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If your study resulted in 30 or more completed card sorts, use the Actual Agreement Method (AAM) dendrogram and if your study had fewer than 30 completed card sorts, use the Best Merge Method (BMM) dendrogram.The AAM dendrogram (see below) shows only factual relationships between the cards and displays scores that precisely tell you that ‘X% of participants in this study agree with this exact grouping’.In the below example, the study shown had 34 completed card sorts and the AAM dendrogram shows that 77% of participants agreed that the cards highlighted in green belong together and a suggested name for that group is ‘Bling’. The tooltip surfaces one of the possible category names for this group and as demonstrated here it isn’t always the best or ‘recommended’ one. Take it with a grain of salt and be sure to thoroughly check the rest of your results before committing!

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The BMM dendrogram (see below) is different to the AAM because it shows the percentage of participants that agree with parts of the grouping - it squeezes the data from smaller sample sizes and makes assumptions about larger clusters based on patterns in relationships between individual pairs.The AAM works best with larger sample sizes because it has more data to work with and doesn’t make assumptions while the BMM is more forgiving and seeks to fill in the gaps.The below screenshot was taken from an example study that had 7 completed card sorts and its BMM dendrogram shows that 50% of participants agreed that the cards highlighted in green down the left hand side belong to ‘Accessories, Bottoms, Tops’.

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Drill down and cross-reference

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Once you’ve gained a high level impression of the results, it’s time to dig deeper and unearth some solid insights that you can share with your stakeholders and back up your design decisions.Explore your open and hybrid card sort data in more detail by taking a closer look at the Categories tab. Open up each category and cross-reference to see if people were thinking along the same lines.Multiple participants may have created the same category label, but what lies beneath could be a very different story. It’s important to be thorough here because the next step is to start standardizing or chunking individual participant categories together to help you make sense of your results.In open and hybrid sorts, participants will be able to label their categories themselves. This means that you may identify a few categories with very similar labels or perhaps spelling errors or different formats. You can standardize your categories by merging similar categories together to turn them into one.OptimalSort makes this really easy to do - you pretty much just tick the boxes alongside each category name and then hit the ‘Standardize’ button up the top (see below). Don’t worry if you make a mistake or want to include or exclude groupings; you can unstandardize any of your categories anytime.

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Once you’ve standardized a few categories, you’ll notice that the Agreement number may change. It tells you how many participants agreed with that grouping. An agreement number of 1.0 is equal to 100% meaning everyone agrees with everything in your newly standardized category while 0.6 means that 60% of your participants agree.Another number to watch for here is the number of participants who sorted a particular card into a category which will appear in the frequency column in dark blue in the right-hand column of the middle section of the below image.

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From the above screenshot we can see that in this study, 18 of the 26 participant categories selected agree that ‘Cat Eye Sunglasses’ belongs under ‘Accessories’.Once you’ve standardized a few more categories you can head over to the Standardization Grid tab to review your data in more detail. In the below image we can see that 18 participants in this study felt that ‘Backpacks’ belong in a category named ‘Bags’ while 5 grouped them under ‘Accessories’. Probably safe to say the backpacks should join the other bags in this case.

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So that’s a quick overview of how to interpret the results from your open or hybrid card sorts.Here's a link to Part 2 of this series where we talk about interpreting results from closed card sorts as well as next steps for applying these juicy insights to your IA design process.

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Further reading

• Card Sorting 101 – Learn about the differences between open, closed and hybrid card sorts, and how to run your own using OptimalSort.
• Which comes first: card sorting or tree testing? – Tree testing and card sorting can give you insight into the way your users interact with your site, but which comes first?
• How to pick cards for card sorting – What should you put on your cards to get the best results from a card sort? Here are some guidelines.

In Part 1 of this series we looked at how to interpret results from open and hybrid card sorts and now in Part 2, we’re going to talk about closed card sorts. In closed card sorts, participants are asked to sort the cards into predetermined categories and are not allowed to create any of their own. You might use this approach when you are constrained by specific category names or as a quick checkup before launching a new or newly redesigned website.In Part 1, we also discussed the two different - but complementary - types of analysis that are generally used together for interpreting card sort results: exploratory and statistical. Exploratory analysis is intuitive and creative while statistical analysis is all about the numbers. Check out Part 1 for a refresher or learn more about exploratory and statistical analysis in Donna Spencer’s book.

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Getting started

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Closed card sort analysis is generally much quicker and easier than open and hybrid card sorts because there are no participant created category names to analyze - it’s really just about where the cards were placed. There are some similarities about how you might start to approach your analysis process but overall there’s a lot less information to take in and there isn’t much in the way of drilling down into the details like we did in Part 1.Just like with an open card sort, kick off your analysis process by taking an overall look at the results as a whole. Quickly cast your eye over each individual card sort and just take it all in. Look for common patterns in how the cards have been sorted. Does anything jump out as surprising? Are there similarities or differences between participant sorts?

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If you’re redesigning an existing information architecture (IA), how do your results compare to the current state? If this is a final check up before launching a live website, how do these results compare to what you learned during your previous research studies?If you ran your card sort using information architecture tool OptimalSort, head straight to the Overview and Participants Table presented in the results section of the tool. If you ran a moderated card sort using OptimalSort’s printed cards, you’ve probably been scanning them in after each completed session, but now is a good time to double check you got them all. And if you didn’t know about this handy feature of OptimalSort, it’s something to keep in mind for next time!

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The Participants Table shows a breakdown of your card sorting data by individual participant. Start by reviewing each individual card sort one by one by clicking on the arrow in the far left column next to the Participants numbers. From here you can easily flick back and forth between participants without needing to close that modal window. Don’t spend too much time on this — you’re just trying to get a general impression of how the cards were sorted into your predetermined categories. Keep an eye out for any card sorts that you might like to exclude from the results. For example participants who have lumped everything into one group and haven’t actually sorted the cards.

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Don’t worry- excluding or including participants isn’t permanent and can be toggled on or off at anytime.Once you’re happy with the individual card sorts that will and won’t be included in your results visualizations, it’s time to take a look at the Results Matrix in OptimalSort. The Results Matrix shows the number of times each card was sorted into each of your predetermined categories- the higher the number, the darker the shade of blue (see below).

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This table enables you to quickly and easily get across how the cards were sorted and gauge the highest and lowest levels of agreement among your participants. This will tell you if you’re on the right track or highlight opportunities for further refinement of your categories.If we take a closer look (see below) we can see that in this example closed card sort conducted on the Dewey Decimal Classification system commonly used in libraries, The Interpretation of Dreams by Sigmund Freud was sorted into ‘Philosophy and psychology’ 38 times in study a completed by 51 participants.

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In the real world, that is exactly where that content lives and this is useful to know because it shows that the current state is supporting user expectations around findability reasonably well. Note: this particular example study used image based cards instead of word label based cards so the description that appears in both the grey box and down the left hand side of the matrix is for reference purposes only and was hidden from the participants.Sometimes you may come across cards that are popular in multiple categories. In our example study, How to win friends and influence people by Dale Carnegie, is popular in two categories: ‘Philosophy & psychology’ and ‘Social sciences’ with 22 and 21 placements respectively. The remaining card placements are scattered across a further 5 categories although in much smaller numbers.

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When this happens, it’s up to you to determine what your number thresholds are. If it’s a tie or really close like it is in this case, you might review the results against any previous research studies to see if anything has changed or if this is something that comes up often. It might be a new category that you’ve just introduced, it might be an issue that hasn’t been resolved yet or it might just be limited to this one study. If you’re really not sure, it’s a good idea to run some in-person card sorts as well so you can ask questions and gain clarification around why your participants felt a card belonged in a particular category. If you’ve already done that great! Time to review those notes and recordings!You may also find yourself in a situation where no category is any more popular than the others for a particular card. This means there’s not much agreement among your participants about where that card actually belongs. In our example closed card sort study, the World Book Encyclopedia was placed into 9 of 10 categories. While it was placed in ‘History & geography’ 18 times, that’s still only 35% of the total placements for that card- it’s hardly conclusive.

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Sometimes this happens when the card label or image is quite general and could logically belong in many of the categories. In this case, an encyclopedia could easily fit into any of those categories and I suspect this happened because people may not be aware that encyclopedias make up a very large part of the category on the far left of the above matrix: ‘Computer science, information & general works’. You may also see this happening when a card is ambiguous and people have to guess where it might belong. Again - if you haven’t already - if in doubt, run some in-person card sorts so you can ask questions and get to the bottom of it!After reviewing the Results Matrix in OptimalSort, visit the Popular Placements Matrix to see which cards were most popular for each of your categories based on how your participants sorted them (see below 2 images).

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The diagram shades the most popular placements for each category in blue making it very easy to spot what belongs where in the eyes of your participants. It’s useful for quickly identifying clusters and also highlights the categories that didn’t get a lot of card sorting love. In our example study (2 images above) we can see that ‘Technology’ wasn’t a popular card category choice potentially indicating ambiguity around that particular category name. As someone familiar with the Dewey Decimal Classification system I know that ‘Technology’ is a bit of a tricky one because it contains a wide variety of content that includes topics on medicine and food science - sometimes it will appear as ‘Technology & applied sciences’. These results appear to support the case for exploring that alternative further!

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Where to from here?

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Now that we’ve looked at how to interpret your open, hybrid and closed card sorts, here are some next steps to help you turn those insights into action!Once you’ve analyzed your card sort results, it’s time to feed those insights into your design process and create your taxonomy which goes hand in hand with your information architecture. You can build your taxonomy out in Post-it notes before popping it into a spreadsheet for review. This is also a great time to identify any alternate labelling and placement options that came out of your card sorting process for further testing.From here, you might move into tree testing your new IA or you might run another card sort focussing on a specific area of your website. You can learn more about card sorting in general via our 101 guide.

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When interpreting card sort results, don’t forget to have fun! It’s easy to get overwhelmed and bogged down in the results but don’t lose sight of the magic that is uncovering user insights.I’m going to leave you with this quote from Donna Spencer that summarizes the essence of card sort analysis quite nicely:Remember that you are the one who is doing the thinking, not the technique... you are the one who puts it all together into a great solution. Follow your instincts, take some risks, and try new approaches. - Donna Spencer

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Further reading

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• Card Sorting 101 – Learn about the differences between open, closed and hybrid card sorts, and how to run your own using OptimalSort.

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• Which comes first: card sorting or tree testing? – Tree testing and card sorting can give you insight into the way your users interact with your site, but which comes first?

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• How to pick cards for card sorting – What should you put on your cards to get the best results from a card sort? Here are some guidelines.