UX research methods: card sorts, tree tests and prototype testing

0:00 UX research methods

What if you could find out whether a new navigation, a new checkout or a new booking flow works, before a developer writes a single line of code? That's what UX research methods are for. In this lecture you'll learn how to choose the right method for your question, how card sorting and tree testing fix navigation with evidence, how to test clickable prototypes in rounds, and how AI is changing research, including where it helps and where it's a trap. Then you'll watch me write an unmoderated prototype test plan.

0:40 Why UX research in CRO

Why add these methods to CRO? Because many conversion problems start before the page. They start in the structure: what the menu is called, how products are grouped, how many steps a flow has. And some ideas are too expensive to build just to A/B test. A new checkout might take weeks of development. If a prototype test with five people shows that three can't find the delivery options, you've saved those weeks. UX research is cheap insurance for expensive changes.

1:15 Question → method

Start with the question, then pick the method. How do customers group our products? That's an open card sort. Can people find something in our proposed menu? A tree test. Where would people click first to do a task? A first-click test. Is the value proposition clear? A five-second test. Can people complete this flow in the new design? A prototype usability test. How does behaviour unfold over days? A diary study. Think of it like a toolbox. A hammer is great, but not for every job.

1:53 Sample sizes

A word on sample sizes, because this confuses people. The famous five users guidance is for qualitative usability rounds, where the goal is finding problems. Five people typically reveal the most serious issues, so you fix them and test again. But when you need numbers, like a success rate in a tree test or click accuracy in a first-click test, you need larger samples. Common guidance is around fifteen or more for card sorts, and fifty or more for tree tests. Match the sample to whether you're finding problems or measuring rates.

2:33 Card sort + tree test

Card sorting and tree testing work as a pair. In an open card sort, participants group thirty to sixty cards, each a product or piece of content, and name the groups. You learn how customers think, and what they call things. Then you draft a new menu, and run a tree test: a text-only version of the navigation, with realistic tasks like you need a desk that fits a small bedroom, where would you look? You measure success rate, directness and time. And always tree test the current menu too, as a baseline. Otherwise you can't prove the new one is better.

3:17 Prototype testing

Prototype testing is for flows. Build a clickable prototype, for example in Figma, and test it with about five target users per round, giving scenarios, not instructions. Fix what you find, then test again. Two or three rounds usually remove the serious problems before development starts. You can run tests moderated, where you watch live and ask questions, or unmoderated, where participants record themselves using a testing platform. Moderated gives depth. Unmoderated gives speed and scale. Then, once it's built, A/B test to measure real impact.

3:54 Simple example: Dubai booking prototype

A simple example. A home-services company in Dubai redesigns its booking flow. Before building, they test a Figma prototype with five recent customers. Task one: book a three-hour deep clean for a two-bedroom apartment next Saturday morning. Three of five can't tell whether cleaning supplies are included, and two think they've booked when they've only added to basket. The team adds a supplies line and a clear confirmation screen. In round two, all five complete the task, and all five are confident the booking is confirmed.

4:31 Realistic example: Karachi electronics navigation (illustrative)

Now a realistic scenario with illustrative details. An electronics retailer in Karachi has a menu grouped by brand and internal categories like digital lifestyle. Site search shows people typing earbuds under five thousand and laptop for students. The team runs an open card sort with twenty customers. Groups form around use and budget, not brand. They draft a new tree and run tree tests on both the old and new structure. The new structure helps more participants find a laptop for university within a budget, and with fewer wrong turns. They rebuild navigation around use cases, keep brand as a filter, and watch search usage and listing conversion after launch.

5:19 Watch me do it: unmoderated test plan

Watch me write an unmoderated test plan. Goal: can first-time mobile visitors book a home-cleaning slot in the new flow? Prototype: the Figma mobile frame, starting at the service page. Participants: five per round, residents who booked a home service in the last six months, and I screen out UX and marketing professionals. Tasks, written as scenarios: book a three-hour deep clean next Saturday morning; check whether supplies are included before paying; change the booking to Sunday. Success criteria: completed without help, with time and wrong turns noted. Follow-ups: what was confusing, and how confident are you the booking is confirmed, from one to five? Analysis: issues by severity and frequency, fix, retest.

6:08 AI in UX research

AI is now everywhere in research tools. It can draft discussion guides and screener questions, transcribe and summarise sessions, cluster notes, and even moderate interviews or tests. Use it to prepare and to process. But be careful with one trend: synthetic users, AI personas that answer as if they were customers. They reflect training data, not your customers, and they can't tell you that your date picker is confusing. Don't use them as a substitute for real participants. Keep a human reading raw quotes and watching key moments. And get consent for recording and AI processing.

6:50 Research + experiments

How does this connect to A/B testing? UX research reduces the risk of what you test. If a prototype fails with real people, you don't build it. If it passes, you build it with more confidence, then A/B test to measure the size of the impact on real behaviour. Research tells you whether something is usable and understood. Experiments tell you whether it changes outcomes. Mature programmes use both, in that order.

7:21 Common mistakes

Common mistakes. Testing with colleagues or friends. Giving instructions like click Products instead of scenarios. Using five participants to claim a success rate. Skipping the baseline tree test. Testing prototypes that are too rough to be realistic, or so polished that nobody wants to criticise them. And treating AI summaries or synthetic personas as findings.

7:45 Recap

Recap. Choose the method from the question: card sorts for grouping, tree tests for findability, first-click tests for the first move, five-second tests for clarity, prototype tests for flows, diary studies for behaviour over time. Match sample size to whether you're finding problems or measuring rates. Use AI to prepare and process, never as a stand-in for real customers. Try this now: pick one flow you plan to change, write an unmoderated test plan using the template in the lesson text, and run one round with five target users before any development starts.