20 Design Challenges in User Research & Testing & Strategies How to tackle ?

What are the 20 different challenges, what a designer really come across when he does a user research and testing? Lot of people really asked me this in a very different, different angles. This particular podcast is coming out of that particular feedback, okay? Because I've guided over 12,500 students and I've helped personally for them to secure jobs less than 2 months, 1 month, 3 months, even 15, 20 days after getting the guidance. And I personally worked on more than 64 plus projects in my whole career of 8.5 years, including product design, UI UX design, system design, entire product lifecycle management. I've seen from physical to digital products. Let me tell you guys, if you understand this 20 different difficulties what a designer is facing, you will be able to much easily tackle that. And I'm going to be telling you the strategies also how you can tackle that. If you are comprehending with this podcast, your life as a UI UX designer will be very, very, very smooth and you will make a very good trajectory growth in a very shorter span of time. Okay? Because this is coming out of this experience, you need to respect that, take a lot of notes. Let's dive into this podcast. The very first thing for a product designer in user research, the problem is data bias. See, I'll tell you personally, we have a lot of biases inside us. Subconsciously we'll be biased during the data collection and interpreting the data. Let's say if you're talking to your friend for some data collection, you will be more personally attached to that friend so that you will directly take the data and put it right. This is a very, very big problem when you do a user research. That is why what you have to do is you need to have lot of diverse population to take your sample size for getting that input. Because if you're doing a field study, if you're doing a user survey research using online forms or talking to interviewers people, and if you're doing a phone call, interviews or anything for your data collection, you need to keep aware that data bias is a big challenge. And the only way to really come out of this is to choose a wide variety of different audience. Okay? And let me tell you, lack of this diversity in the sample group can lead to a very, very skewed and poor results. You need to understand this. The second important difficulty what people face in user research is data source identification. Let me tell you, identification of the credible and relevant source of information is damn, damn difficult in the age of digital, what do you call, era, right? So we have chat GPT coming. We Have a Google Bard, we have a Google. We have a lot of search engines, YouTube videos and and Udemy Coursera. Materials is. Materials and learning things are not really problem guys. We have a huge. In fact it's a over information overload. The point is how do we really find that exact source of information and how trustable it is? Let me tell you guys, that is why you need to go to statistical websites. You need to go to government statistical websites. Like if you took India National Informatics Center Nic, if it's us, you can go to National Security for data collection. I think the name is not correct but they have a very specific website for that where they have the datas and inputs. There definitely you can trust them. You can take out the information from that to get a global perspective, to get an Indian perspective. Whatever you wanted to do. Even state wise also people have lot of data poured in based on that. So if you take data from here you will have a much credible information. Let me tell you guys before putting a data from Internet spend twice the time how you research the data to check that this is the time one is to two. If you're taking five minutes to do a research and find the data, take 10 minutes to validate if it's really true, really fact, really stat properly validated from a proper source. This is a major major important difficulty which you will have to take care. The third important thing is data availability. See accessing a real time data might be difficult because doing primary research will take a lot of times. Even secondary data validation could be little challenging because you don't have a base primary data, right? So you need to balance that with extensive data. Like let's say if you are a time constrained working project, if you're in a budget constraint project. So you make sure you find the place where you can get a real time data so that you can derive that particular secondary information. And then you can justify with the stat and fact so that it properly converged as a primary information. Because lot of times you cannot visit the user or take a real time information where you can do an interpolation method to find a close to like let us say 80 to 90% close data. By interpolating the information, what you got from that place and the secondary source. This is the best way. Many people really stuck really waiting for a lot of time to get that data which they will never get. You need to understand that time constraint as well. Okay. The fourth important difficulty is data mapping. So transforming your abstract data into a very clear understandable format is A big challenge, guys. That is why you need to use Miro. It's a very good software. You can use Invision, you can use PPTs, you can use Flowchart, you can use PPT or you can use even word file. That's not the problem. Guys point is not about that. It's about how you transform that information. You need to first mind map whatever you are, right? You are thinking you need to list down the points clearly. You can use some boxes, colors, shapes and then you can describe that in a more illustrative way or even in a textual way. No problem. The moment when you do it immediately after your research, mapping of the data is more easy. Lot of people don't do it. They do it after five days, 90% content. They forget. They do it after 10 days, they completely forget everything. You need to do it immediately. You need to do it with some software, whatever you have and just map it down. Then and only the mapping will become more understandable for the others. Because you have 80 to 90% you have just put whatever you've collected, right? Because we have a tendency to forget quickly the meaningful visualizations you can make, guys, when you put it in a form of map. The fifth important difficulty is poor presentation skill. A lot of people don't have this. See guys, choose any software. It's not a problem of that. Even you can choose a software called figjam. It's AI enabled figma board where you can use lot of presentation methods and you can use lot of plugins in figma. Also you can use a tool called Gamma AI G A M M A dot AI. I'll paste you in the description for this podcast. It's a beautiful AI tool. You can just put your text, it will spit you a PPT out of there. So what is the point? Tools are there, but what you really wanted to put there is the point there. Okay, so you need to effectively communicate with your team members or using presentations, or using chart paper or pencil pen. Whatever you choose, make sure you tailor that communication to the level of understanding. Let's say if you're talking to a kids for your project research, make sure you explain in a very simpler and very what you call primary way. Because they understand like that only, right? If you're talking to a CEO, your communication should be different. So understand this. And the sixth important part is synthesizing your inputs. See, even if you collect a lot of information, a lot of people have problem in synthesizing that insight. People don't know how to correlate it. That's why you need to write it down. You need to find some patterns and then you need to find some insight. Like let's say what could possibly go wrong or what can possibly not happen. That is where your creativity touch should be there. Because insights, lot of people don't know how to derive it out there. They just see the raw information. Primary research, secondary research. You need to draw some parallels between the real time data and think intuitively, okay? How does it really happen, why it is not this way, why it is not that way, why competitors do done this way. That's why SWOT analysis helps you sometimes to get some actionable insights and mapping out of it. Okay, so a lot of people have this problem of doing it. The seventh important difficulty what user research people who do have is medium or the software tools. Right? Lot of people get confused with software options. Whichever they don't have. Let me tell you guys make it very simple. You can use a tool called Miro M I R O. It's a beautiful tool and it's just a drag and drop templates. You can use that to collect your information very clearly. Because if you're doing a user research, extensive data will be there. You'll get graph, you'll get information, you will get proper pictures also. But where do you really put it? Sometimes PPT will be really heavy loaded. So use Miro where you can put everything in one canvas. Okay. Very similar software literally which is called Invision. You can use that as well. Okay, you can try it out. Now let's come to the eighth difficulty which is crediting the source. See, lot of people get problems after user research because somebody else data you are using, right? You will get problem when you present it somewhere. If you are presenting in a very reputable institute or maybe your client, definitely people will check your data trustworthiness. What happens is people really treat you in a different way when they find out that you have taken from some source and then you are not crediting that properly or else you have to remove that. If you are not able to credit it. A lot of times it becomes a big trust issue. When I worked in a college called Hanover Hochschule Hanover in central part of Germany. You know what happened? There was a professor and there was a assignment upload which we have to do. People really told me that Jagdish remove all this content or credit it properly because we are uploading it in our website. I was like so shocked. Why do I have to really do this if it is not a client project even for A college material which I have to upload. Why should I extensively do this? They said no, it's going into the college website. You need to remove all these Google images or if you're putting that image, you have to credit it properly for the user or unless you'll have to pay the royalty for it. A lot of people take it very granted. Credibility of that source is very, very, very vital when you are putting that in your presentations or in your research work. Okay, this is the most most important thing, guys. And the ninth important thing when you do a user research is you have a shy to go and talk to people to collect information, right? That's why you need to have a fear of judgment left out completely. When you really leave that. And whenever you are really having that high energy to go and get information for your project, your motivation of the project is right. Your real need for the project is there in the market. Unless if you collect this information and solve this problem, nobody else is going to solve automatically. You will get motivated, guys. You will not feel any shy to go and talk to people. Lot of people have a problem in going and talking to strangers. This is a big problem in user research. And then they cut short their research. They just get their comfort zone. They are known people their online resources and do it. They don't even do the primary research and conclude the entire project. And when you do this, you will have a terrible problems doing that. Okay, yes. This is the ninth important problem. The last important point is documentation. The moment when you leave this documentation, what happens is you will not get any data or you will not even forget as I already mentioned and you will not have a proper proof to even substantiate it to your stakeholders to show what you collected is true or not. People are very bad in documentation. Just drag and drop some content guys, put it, dump it in a word file. That would be enough. Maybe you can rearrange it later. But having that data is most most vital than presenting it how? Right? So documentation is just 10th different 10th important pain area. And the big struggle. What really design is fine. This is the first part of this podcast. Let me repeat you quickly guys. The first is data biasness. Second is data source identification. Third is data availability. Fourth is data mapping. Fifth is presentation skills. Sixth is synergizing your insight. Seventh is using a tool called Miro. Eight is giving a credit properly for that. Ninth is talking to a stranger. And tenth is documentation of your thing. When you're able to overcome these things and challenges, definitely you will do a much extensive User research properly for your projects. Okay, this is the first part of the podcast. Now let's go to this testing part of it. The big challenge, what people really face in testing. Because if you're doing user research, then what you do is you make an ideation, then you go for a testing, right? So the big part of this testing is selecting participants for your user groups, right? A lot of people have the problem in that. So what you can do, you need to ensure a diverse representation of a group for testing. You need to avoid biases by selecting those participants carefully so that you will have more different kind of feedbacks. We did a user research in our company recently, choosing different age group, different genders, different interest, different department people to get a very diverse perspective of it. That is why you need to have a proper planning before you do a testing. Or else what will happen is your testing result will be very homogeneous. There is no point in doing the testing. You could have assumed it on yourself, right? Yes. The second important thing is quality of group. See, you need to ensure quality should be high. And quantity also sometimes matters if you're doing an extensive testing, okay? Because a lot of times even 10 users will give you good quality insights where you can close it. Sometimes you need hundred people to tell you that answer because your complexity of that nature of that product is very high. So you need to choose quantitative or qualitative, which really makes that. And you need to well, define, mention what you require in your project so that the user will also be tuned for that. The third important point is defined criteria and error tolerance. See, lot of people have a difficulty in this. Establishing a very clear criteria and a goal for your project, whether it's a success or failure is very important. And people don't really take care of their errors. So I'll tell you when you complete a testing, lot of times people really have a problem in accepting the data as it is, right? It shouldn't be like that. It should. It should be exactly homogeneous. It should have that realness. It should have that errors also in that lot of people do not take that. They just do it just like that. And what happens is the testing will not give you any fruitful insight. That is why the tolerance for the error should be there so that it looks like we are designing for humans. Humans are possible to make mistakes and your testing should really take care of that. Nobody can perform like a robot, guys. That's not how the human testing will happen. Okay? So the fourth important problem is avoid shortcuts. It's a big Brick what you call difficulty guys. You need to resist a temptation to take shortcuts. Testing is a process. It's like a meditation. You need to patiently wait till the end. If you make a Hariburi decision, even you will not get a quality output in your testing. That will lead to a improper result, which will lead to an improper designed, which will lead to improper product experience. So that the functionality will not be done and user will not be able to complete the task. Let us say if you are making a payment and if it gets interrupted and your payment is gone, what you will do. So you need to maintain that commitment in depth. Testing. The fifth important difficulty is validation of the raw data which we have already discussed. Even in the testing. You need to ensure the accuracy and reliability of the collected data by going through it one more time. Or even you can go through your research work of your primary and secondary to validate it. Unless if you're not validating, it becomes a big problem. The sixth important thing is resource allocation. See, when you're doing a testing, definitely you need to first allocate your resource. Let's say if it's a complex testing scenario, you need to have two, three people. Or sometimes you can outsource this to a third party user, third party agency where they can help you complete this entire test. Because you need to allocate that resource. Or else lot of time you do a kitchen job. You need to be very clear on that. If you are a user research guy who is doing an extensive testing. Seventh is asking some open ended questions in your testing. Okay, like let's say if you ask an open ended question it gets you a very rich response. Closed ended question is like hey, do you like bike or not? User can only answer yes or no, right? Open ended question is something different. Why do you like bikes? See, that's why it's very important to ask open ended questions where what makes it interesting why it is so important? Like let's say what are all the things which you like? Like when you ask this open ended questions, people really go into deeper answers. When you ask specifically did you like this or not? Are you willing to do this or not? So that is like a close to net questions. Okay, so you need to understand this 8 is build a trust. See a lot of people make this big problem without establishing a trust. When you go and talk to a participant, sometimes they'll tell I'm not interested to participate in this test. So first create that environment. Talk to them, hey, introduce yourself, tell what is the purpose of your Test tell using their inputs how you can able to. I mean how this input of them can help your project grow in a different way. How. How thousands of people can get benefited by participating in that test appreciate them and genuinely tell the intention of that. If they're not still comfortable, leave them. It's okay. You can move to the other participant, right? So establishing the trust and making them comfortable is a very important thing. Non observance. It's a very, very big difficulty. Is the ninth difficulty. Encourage detailed observation guys rather than surface level analysis. See, lot of people have a problem in observation. Even during my career start I used to be very what you call surface level. I used to make decisions as a designer. I need to take some favorite sides of me so that my what you call zone will be very comfortable. But you have to go to the deep level to really observe the patterns inside it. You need to train yourself guys designer needs to have that very subtleness in observing that user behavior. And when you observe it very carefully, you will find a real pain area what the users are facing. Sometimes the user will not tell. You can find. I'll give you an example. Sometimes user will go into a habit of everyday today lifestyle where they will say I don't have any problem. Why are you improving this product? It's fine. But if you go and observe, he might be really enduring a lot of pain while going through that. Like let's say if it's an application process of a school where the parents are going through a 15 day process for application where they will be saying Chelle, 15 days is fine. That is earlier it was more than a month. Now it is 15 days when I will do it. But if you can able to provide an application or a system where it can be done in half an hour time, people will be really happy, right? So user will not ask you design me an application for half an hour. To do this process, you need to understand observing that pain point. That's why observation is very important. The 10th important and the last important pain area is dealing with with incomplete or inaccurate answers. See, strategizing a way to handle that situation where participant will not provide you sometimes complete information. So develop that probing technique to dig deeper. When you find that vagueness, try to collect with secondary information or a primary information to justify that information. Then you will be able to close the loop properly. Okay, this is the major challenges which people face in testing. Let me repeat you right. Choosing the right participant. Choosing the quality of the group for using quantitative or qualitative method. Defining the criteria and error tolerance into consideration and avoiding shortcuts and being patient and validating the raw data. Sixth is finding out resource allocation how to do it and seventh is asking open ended and closed questions. Eight is building trust, ninth is observing very closely and tenth is dealing with incomplete answers and validating with your research. If you're able to do this perfectly guys, let me tell you, you are making a very good user research as well as a testing which is there in your career very fast and you will feel this process is more butter like experience when you move along. A lot of people make shortcuts. They don't have an intention and a mindset to really understand it. They really don't have a big future in their design because they'll be one or the other way thrown out of this field easily because people can sense you very very easily. Because design, you can't hide anything. You need to be honest. You need to be showcasing or else you will not be growing ahead. Okay, so take care of these principles, listen to this podcast one more time and take actions for your paper. Okay? So thank you so much guys. If you find this useful, give me a best rating in Apple and Spotify. And if you need my mentorship support in helping you, send me that email. I'll be more than happy to help you. Okay, take care. All the best. Bye bye guys.