How to get started with tree testing 🌱

It’s been a long day. Scratch that - it’s been a long week! Admit it. You loved every second of it.

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Twelve hour days, the mad scramble to get the prototype ready in time, the stakeholders poking their heads in occasionally, dealing with no-show participants and the excitement around the opportunity to speak to real life human beings about product or service XYZ. Your mind is exhausted but you are buzzing with ideas and processing what you just saw. You find yourself sitting in your war room with several pages of handwritten notes and with your fellow observers you start popping open individually wrapped lollies leftover from the day’s sessions. Someone starts a conversation around what their favourite flavour is and then the real fun begins. Sound familiar? Welcome to the post user testing debrief meeting.

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How do you turn those scribbled notes and everything rushing through your mind into a meaningful picture of the user experience you just witnessed? And then when you have that picture, what do you do next? Pull up a bean bag, grab another handful of those lollies we feed our participants and get comfy because I’m going to share my idiot-proof, step by step guide for turning your user testing notes into something useful.

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Let’s talk

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Get the ball rolling by holding a post session debrief meeting while it’s all still fresh your collective minds. This can be done as one meeting at the end of the day’s testing or you could have multiple quick debriefs in between testing sessions. Choose whichever options works best for you but keep in mind this needs to be done at least once and before everyone goes home and forgets everything. Get all observers and facilitators together in any meeting space that has a wall like surface that you can stick post its to - you can even use a window! And make sure you use real post its - the fake ones fall off!

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Mark your findings (Tagging)

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Before you put sharpie to post it, it’s essential to agree as a group on how you will tag your observations. Tagging the observations now will make the analysis work much easier and help you to spot patterns and themes. Colour coding the post its is by far the simplest and most effective option and how you assign the colours is entirely up to you. You could have a different colour for each participant or testing session, you could have different colours to denote participant attributes that are relevant to your study eg senior staff and junior staff, or you could use different colours to denote specific testing scenarios that were used. There’s many ways you could carve this up and there’s no right or wrong way. Just choose the option that suits you and your team best because you’re the ones who have to look at it and understand it. If you only have one colour post it eg yellow, you could colour code the pen colours you use to write on the notes or include some kind of symbol to help you track them.

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Processing the paper (Collating)

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That pile of paper is not going to process itself! Your next job as a group is to work through the task of transposing your observations to post it notes. For now, just stick them to the wall in any old way that suits you. If you’re the organising type, you could group them by screen or testing scenario. The positioning will all change further down the process, so at this stage it’s important to just keep it simple. For issues that occur repeatedly across sessions, just write them down on their own post its- doubles will be useful to see further down the track.In addition to  holding a debrief meetings, you also need to round up everything that was used to capture the testing session/s. And I mean EVERYTHING.

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Handwritten notes, typed notes, video footage and any audio recordings need to be reviewed just in case something was missed. Any handwritten notes should be typed to assist you with the completion of the report. Don’t feel that you have to wait until the testing is completed before you start typing up your notes because you will find they pile up very quickly and if your handwriting is anything like mine…. Well let’s just say my short term memory is often required to pick up the slack and even that has it’s limits. Type them up in between sessions where possible and save each session as it’s own document. I’ll often use the testing questions or scenario based tasks to structure my typed notes and I find that makes it really easy to refer back to.Now that you’ve processed all the observations, it’s time to start sorting your observations to surface behavioural patterns and make sense of it all.

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Spotting patterns and themes through affinity diagramming

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Affinity diagramming is a fantastic tool for making sense of user testing observations. In fact it’s just about my favourite way to make sense of any large mass of information. It’s an engaging and visual process that grows and evolves like a living creature taking on a life of its own. It also builds on the work you’ve just done which is a real plus!By now, testing is over and all of your observations should all be stuck to a wall somewhere. Get everyone together again as a group and step back and take it all in. Just let it sit with you for a moment before you dive in. Just let it breathe. Have you done that? Ok now as individuals working at the same time, start by grouping things that you think belong together. It’s important to just focus on the content of the labels and try to ignore the colour coded tagging at this stage, so if session one was blue post its don’t group all the blue ones together just because they’re all blue! If you get stuck, try grouping by topic or create two groups eg issues and wins and then chunk the information up from there.

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You will find that the groups will change several times over the course of the process  and that’s ok because that’s what it needs to do.While you do this, everyone else will be doing the same thing - grouping things that make sense to them.  Trust me, it’s nowhere near as chaotic as it sounds! You may start working as individuals but it won’t be long before curiosity kicks in and the room is buzzing with naturally occurring conversation.Make sure you take a step back regularly and observe what everyone else is doing and don’t be afraid to ask questions and move other people’s post its around- no one owns it! No matter how silly something may seem just put it there because it can be moved again. Have a look at where your tagged observations have ended up. Are there clusters of colour? Or is it more spread out? What that means will depend largely on how you decided to tag your findings. For example if you assigned each testing session its own colour and you have groups with lot’s of different colours in them you’ll find that the same issue was experienced by multiple people.Next, start looking at each group and see if you can break them down into smaller groups and at the same time consider the overall picture for bigger groups eg can the wall be split into say three high level groups.Remember, you can still change your groups at anytime.

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Thinning the herd (Merging)

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Once you and your team are happy with the groups, it’s time to start condensing the size of this beast. Look for doubled up findings and stack those post its on top of each other to cut the groups down- just make sure you can still see how many there were. The point of merging is to condense without losing anything so don’t remove something just because it only happened once. That one issue could be incredibly serious. Continue to evaluate and discuss as a group until you are happy. By now clear and distinct groups of your observations should have emerged and at a glance you should be able to identify the key findings from your study.

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A catastrophe or a cosmetic flaw? (Scoring)

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Scoring relates to how serious the issues are and how bad the consequences of not fixing them are. There are arguments for and against the use of scoring and it’s important to recognise that it is just one way to communicate your findings.I personally rarely use scoring systems. It’s not really something I think about when I’m analysing the observations. I rarely rank one problem or finding over another. Why? Because all data is good data and it all adds to the overall picture.I’ve always been a huge advocate for presenting the whole story and I will never diminish the significance of a finding by boosting another. That said, I do understand the perspective of those who place metrics around their findings. Other designers have told me they feel that it allows them to quantify the seriousness of each issue and help their client/designer/boss make decisions about what to do next.We’ve all got our own way of doing things, so I’ll leave it up to you to choose whether or not you score the issues. If you decide to score your findings there are a number of scoring systems you can use and if I had to choose one, I quite like Jakob Nielsen’s methodology for the simple way it takes into consideration multiple factors. Ultimately you should choose the one that suits your working style best.

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Let’s say you did decide to score the issues. Start by writing down each key finding on it’s own post it and move to a clean wall/ window. Leave your affinity diagram where it is. Divide the new wall in half: one side for wins eg findings that indicate things that tested well and the other for issues. You don’t need to score the wins but you do need to acknowledge what went well because knowing what you’re doing well is just as important as knowing where you need to improve. As a group (wow you must be getting sick of each other! Make sure you go out for air from time to time!) score the issues based on your chosen methodology.Once you have completed this entire process you will have everything you need to write a kick ass report.

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What could possibly go wrong? (and how to deal with it)

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No process is perfect and there are a few potential dramas to be aware of:

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People jumping into solution mode too early

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In the middle of the debrief meeting, someone has an epiphany. Shouts of We should move the help button! or We should make the yellow button smaller! ring out and the meeting goes off the rails.I’m not going to point fingers and blame any particular role because we’ve all done it, but it’s important to recognise that’s not why we’re sitting here. The debrief meeting is about digesting and sharing what you and the other observers just saw. Observing and facilitating user testing is a privilege. It’s a precious thing that deserves respect and if you jump into solution mode too soon, you may miss something. Keep the conversation on track by appointing a team member to facilitate the debrief meeting.

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Storage problems

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Handwritten notes taken by multiple observers over several days of testing adds up to an enormous pile of paper. Not only is it a ridiculous waste of paper but they have to be securely stored for three months following the release of the report. It’s not pretty. Typing them up can solve that issue but it comes with it’s own set of storage related hurdles. Just like the handwritten notes, they need to be stored securely. They don’t belong on SharePoint or in the share drive or any other shared storage environment that can be accessed by people outside your observer group. User testing notes are confidential and are not light reading for anyone and everyone no matter how much they complain. Store any typed notes in a limited access storage solution that only the observers have access to and if anyone who shouldn’t be reading them asks, tell them that they are confidential and the integrity of the research must be preserved and respected.

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Time issues

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Before the storage dramas begin, you have to actually pick through the mountain of paper. Not to mention the video footage, and the audio and you have to chase up that sneaky observer who disappeared when the clock struck 5. All of this takes up a lot of time. Another time related issue comes in the form of too much time passing in between testing sessions and debrief meetings. The best way to deal with both of these issues  is to be super organised and hold multiple smaller debriefs in between sessions where possible. As a group, work out your time commitments before testing begins and have a clear plan in place for when you will meet.  This will prevent everything piling up and overwhelming you at the end.

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Disagreements over scoring

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At the end of that long day/week we’re all tired and discussions around scoring the issues can get a little heated. One person’s showstopper may be another person’s mild issue. Many of the ranking systems use words as well as numbers to measure the level of severity and it’s easy to get caught up in the meaning of the words and ultimately get sidetracked from the task at hand. Be proactive and as a group set ground rules upfront for all discussions. Determine how long you’ll spend discussing an issue and what you will do in the event that agreement cannot be reached. People want to feel heard and they want to feel like their contributions are valued. Given that we are talking about an iterative process, sometimes it’s best just to write everything down to keep people happy and merge and cull the list in the next iteration. By then they’ve likely had time to reevaluate their own thinking.

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And finally...

We all have our own ways of making sense of our user testing observations and there really is no right or wrong way to go about it. The one thing I would like to reiterate is the importance of collaboration and teamwork. You cannot do this alone, so please don’t try. If you’re a UX team of one, you probably already have a trusted person that you bounce ideas off. They would be a fantastic person to do this with. How do you approach this process? What sort of challenges have you faced? Let me know in the comments below.

“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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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.