 We have two general methods for conducting user research, which is gathering feedback on a digital product with the intention to improve it. So we have qualitative and quantitative. Qualitative research involves conversing, questioning, and observing users as they interact with your digital product. This usually comes in the form of interviews or live tests. It's also called participatory design, or having your users participate in your user's development or the product's development, sorry. And on the other hand, we have quantitative research, which is capturing user activity, patterns, and behaviors with a product using data or analytics. Also, I'm going to stop using the term users for the rest of this talk because I think it sounds pretty detached. And my hope for Web 3 is that we can stop defining our community members by the lone fact that they use the product and start to see them in the broader context of their complex lives and their complex needs. So we're learning about people to better serve them with our digital products, right? Let's unpack those two methodologies. Starting with quantitative, some benefits to quantitative research is that we can determine patterns to investigate further. So data can reveal what's going right or wrong based on our product's goals. It can also reveal insights that leave us curious. Some drawbacks to quantitative research is that the data doesn't tell you why something is happening. And to guess why is fine. Hypothesis are really important, but it's important to treat them as untested, subjective results to look into further. Another drawback is that people are conscious of their privacy. So a lot of people in the Web 3 community use Brave or VPNs that make their actions off-chain untraceable. So this means that the data that we pull off-chain may never be the full picture of community usage. Data collection and analysis is expensive and time-consuming. And it can also be unethical. So a person's data can be used to improve a product and increase a company's profits. If that data has value and the person who is creating that data is not collecting any of that value, is that ethical? Maybe if they sold it to you or if they agreed to give it to you without compensation, but that's the argument here. That to collect somebody's valuable data, you should either compensate them or gain their consent. Data can also be biased. And there's a lot of types of bias that can occur here, but the most common is called confirmation bias, which is when somebody is interpreting the data set and then comes to conclusions based on their own ideas or interests. What can also happen here is the manipulation of data visualizations to further justify that bias and present it as truth. Moving over to qualitative research. So some benefits to this is that it can validate those hypotheses you made about your data. So you can get to the bottom of why by asking people, by watching them interact with your product and asking them to think out loud. It is super effective. You're also compensating people for their time and gaining their consent to participate in your study. So with those two criteria, your research is becoming more ethical and legitimate. It's also encouraging community development and responding to that eagerness to contribute in Web 3. So people want to be involved, so let them. Reward them, encourage them. This builds loyalty and buy-in. And you're building empathy with your community by talking to them, by seeing their faces. It's really important to see your community as humans and not data points. And it's really easy to detach yourself from this valuable concept if your data is the only source to understand the people that you work for. So it does have some drawbacks. Participatory design can be really challenging in an online international community. Many people don't feel comfortable sharing their identities. They don't want to show their faces on camera. They may not speak your primary language. These can all be really intimidating barriers. And there can also be confirmation bias here. So it's important to keep in mind that this line of research is informal. We're not using rigorous controls under the scientific methods. This research is always somewhat subjective and open to biases. So my approach for participatory design in Web 3 is always changing, but here's what's been working so far. So I found that by offering tiered approach for users to participate, it lets you meet people where they're at. So you can have these bottom tier rewards for submitting to long-form text surveys. This lets people translate them, and they can also remain anonymous. You can offer mid-tier rewards for having people record looms of themselves screen sharing and performing certain tasks. This can also be translated. This can also be anonymous, and they can speak at their own pace if they choose to. And then you have a top rewards tier where you have a video call, and they perform certain tasks, answer questions. And this works best when you can both communicate freely with each other. Systems like this are best managed in Discord or other community tools like Crew 3. Ultimately, I advocate for a participatory design-focused approach with data to guide you towards those areas of improvement. I think there's a really beautiful opportunity in Web 3 to practice genuine empathy for our community by working alongside them. Thank you. Thank you. What kind of tools do you use on the quantitative side for user research? On the quantitative side? At the moment, we typically hire somebody to do that for us. So we've been working with ArcX. We've been really great at accumulating data for our products. On the smaller scale side, we use Google Analytics. That's easy for me to interpret. But yeah, for the bigger questions, we outsource.