Day 29 - How to view the statistics and analytics in research in anySkill Booster 100 Day Challenge

Foreign.

Guys, even though I'm traveling, I just wanted to continue the session. I don't want to leave this gap.

Okay?

So that's the reason we are connecting and particularly we are going live also in the YouTube stream. Welcome for the people who are watching live out there. And we have a very, very interesting topic today, okay. Which is actually called Day 29. Guys, even though I'm traveling, I just wanted to continue this session.

I don't.

Okay, fine. I'm just keeping it open. One second speaker and I will make sure I am getting a spotlight. Okay, that's great. Fine. So this is one of the very important topic. The topic being how to view the research. Like let's say many people have a problem in terms of like let's say not knowing how to do the statistics and analytics in the research in any project research phase, right? Because out there many designers are primarily doing primary mean doing research, doing analysis, taking out the decision for design, final concepts, right? So that is why this skill is very important and that's the reason we are also choosing this particular skill set to teach. Okay, so just. I'm sharing my screen. Just look at my. Confirm me whether you are able to see my screen or not. Could you confirm me that you can able to see my screen?

Yes, I can check.

So much. Thank you so much. Hasnain, I promoted you as a panelist. You can come over there, they'll make you co host so that you can help others also join this call. Is it fine? Fine sir. Thank you so much. Excellent guys. Welcome to the skill booster. So how to view the statistics and analytics and research in any project phase? First of all, what is this concept about? See the reason why it is very important is it is giving you a proper direction to go for the data. Like let's say we take a decision for the project, right? It is completely based on the data. That is why it is very vital to understand we have to view, we have to learn how to view this kind of an information. Let's say if you are viewing many statistics, if I am also viewing many statistics, the way in which I will take a decision is much better than you. The reason being am able to see through the patterns and the connections. That is why it is very, very important.

Okay?

It empowers you to take that. Second, it validates. It supports the design choices with concrete evidence. So you're saying people are liking this particular option. People are liking this call to action. People are liking this button.

How? Why?

How much people are liking this? How much people are not Liking this. If you can able to provide some evidence concretely that helps you validate that.

Okay.

And also it increases the efficiency and improves qualitative understanding of a user behavior trends and design effectiveness. Most, most am. The reason being a lot of people don't fight the design validation with this intention. That is why they get into a big problem.

Okay.

So that is the very first reason why we are doing it. So what are the key techniques to do it? First of all, you can go to Google Analytics or Hard Jar and I think Google Analytics and hardjar, I've given you some open view of it.

Okay.

Either you can go for answerthepublic.com or possibly you can go for something like Bussumo Keys keyhole. If you take any AI tools, it doesn't matter. Or maybe Google Tools or anything. First of all, you will get a lot of behavioral data about the user. Let's say even if you ask ChatGPT, it's going to give you. But the point is first you'll have to collect the data. Without collecting, we cannot view anything.

Okay.

So collection part is very important. You can do it in multiple ways. You can do it in primary, you can do it in secondary, you can do it in tertiary. That is up to you. Based on the project nature. Whatever you want, you are going to ultimately do that. That is a. That is up to the project nature. Second is segmentation. You have to filter by demographics. Like let's say whatever data you have, definitely it will be categorized into multiple junks. Let's say if you're doing some kind of a research for different, different target market. So then demography will come into picture. Then you will have to what you call filter out by device type. Like let's say if people are using mobile app or possibly people are using some kind of a tablet or anything for that sake.

Okay.

So that will totally depend on the kind of research what you do. Sometimes what happens is the customer gives you this segmentation very easily. So you don't have to worry much about it.

Okay?

You can just take it out and implement it. But in certain case you will have to do everything from the scratch, right? So that is very important. The third is you'll have to visualize and interpret. This is the most, most interesting part. See, when you see the data, when you see a graph, you should know how to read them. You should understand how it is going to help my business solve the problem, how my user's problem is getting solved. Because of this. Are there any deltas to validate it? See, sometimes even, even if you have to go back and research it out some information, you will have to do it because it's required, right? The reason being, sometimes what happens is your customer will say this is, this is what we need and you will be also forced to do, okay, this is what we have to do. You will have to back work on that to find out some statistics available. Like let's say if customer is saying no, all of our users are left hand users. So please design that. That much only a client will also say, right? Your customer will also say, so what you have to do as a designer is try and understand and validate. Okay, how much users are available in the left hand side, how much users in this particular country for this reason, for this region, people are available. Okay, you have to backtrack with the data. Sometimes you will have to do the backtracking. That is why I'm telling you it's both the sides. Many times you, you will do the data and drive the project and research. Sometimes the research is already done. You will have to backtrack the work and make it justifiable. That is the reason I told you it is very important to validate with the concrete evidence also and prove it.

Right?

Then what happens? The decision making will be much easier if people are able to invest some kind of an investment for your product or service.

Or.

Or maybe any kind of a decision is majorly made by the company will be solely based on that. If you do any kind of assumptions here, it will make a lot of problems in terms of what you call profit revenues and wrongly targeting the market, not yielding anything. Like let's say if nobody is downloading that application or website or nobody's buying that product or service, how the business will grow. Ultimately the business would have invested a lot for the designers and developers and other people to develop this application on the first place, right? So everything will be in V. That is why your data is very, very important and you should know how to visualize it and interpret it. Okay, so tools like tableau or graph based insights, you can do that. But the point is start with basics. You should understand from the Google Analytics data. Like let's say if you take Google Analytics data. Let me open one screen and show you out. Just a second guys. Oh, this is the reason I asking for first time. Many people out there. What happens is marketers typically use it. In fact I don't have the account also in Google Analytics. But the thing is you can, you can first understand from your marketing research data like what Are you really trying to say I'll just open the chat GPT screen and then let me view some kind of statistics for you. Just a second. I'm sharing the screen again so that you can able to view it. Just a second. Fine.

So.

Yeah, you can able to see my screen, right? You can able to see my screen. So what I'm trying to help you is could you take out the data for the people who are left hand side users when it comes to smartphone application usage with respect to a city, Bangalore and also in suburbans of Bangalore who are using smartphones. Could you give me some statistics and data regarding to that? Okay, so what happens is some kind of a data is available.

Okay.

Some statistics, some data has come in front of me now. So what happens? General smartphones, Bangalore being a tech hub has a high importance. Okay. Has a high importance and penetration rate with most users accessible to the digital service through mobile data. Okay, so Bangalore spends 3 hours per day on smartphones. See look, this is one example I'm taking out. Okay, so you got one. Statistics. On average, people in the urban and suburban regions in Bangalore spend around 3 hours per day on their smartphone. Predominantly engaging with social media, e commerce and utility.

Wonderful.

The reason is if you get this data, how do you visualize jagdish? How do you interpret this? You can do one thing. If I have to create a productive application in order to reduce this time to three hours to two hours, what are all the methods I can able to intervene? See, with this insight I'm driving another problem statement. With this insight I'm driving another new project. With this insight I'm trying to build some of the what you call solution or a product or a productivity kind of an application or whatever in order to make these people bring the time from three hours to two hours. Are you guys understanding so which is very important to understand the data point time management features could be made task and goal management tools, mindful usage interventions, behavioral nudges and rewards. See this looks a little interesting, right? So you can find these relevant industries who are working on this. See now you found the gap also, right? So this is how by just looking at the data you can able to question why it is important. So why people are using three hours? Why can't we make it even four hours? Sometimes the productivity needs to be that way also, right? Okay, what if three hours people are using only on a specific app? Why don't we drive their attention to a different application? Maybe a highly productive application or maybe we can come down so you can do anything and you can do everything with the visualization of the data. What you have, you can. You can predict it in multiple different ways. To understand this guys.

Okay.

Even when you take another statistics like let's say in India, smartphone usage is more prominent among 25 to 35 age group of aligning with Bangalore tech savvy industry.

Right?

So you can focus this market 25 to 34. What is their preferences, what is their needs. So how you take this data and device it, it depends on you. That depends on your project statement or your problem statement or your client or your what you call the nature of the project, the timeline. Everything matters a lot. Because many times you will not get much time. You will be only forced to do certain things in your boundary do it.

No problem.

Many times you will get chance also to do a research. So try started understanding this data points in a very very graphical way and try to map it. Maybe you can take a whiteboard, you can write the numbers, you can create a mind map. You can try to put some information around it. You can gather the statistics into a form of a big complex mind map. So you can do anything internally. Visualize yourself as well as showcase your team.

Okay?

That is up to you. Whatever tools you want, you can use Miro, you can use Envision, you can use some of the tools called even figjam. So these are all available for you to do it.

Okay.

And they are also suggesting like tab view for the graph based insights. If you wanted to view some kind of a graph you can able to do that. Like let's say okay I'm taking one online reference here. Whatever I got here. Okay, maybe there is no graphical data here but I can able to see this numbers. Can you see? See guys here 7.9 percentage is between age 0 to 4 in that. In that 80 percentage. See this is a split of this data. 8.7 percentage between this age. So like let's say Your market is 25 to 34 in 16.7%. It is a major market rate. Out of all this, this is the highest number. So you can invest definitely on making this. See now the decision is made. If you present this kind of a data with your team, what happens that will give a greater edge towards taking a decision faster. In fact that is what is majorly important.

Okay.

So that is the reason I'm telling you this is very very important.

Okay.

So how to do it J this step by step. Let me tell you guys first of all identify the metrics. Try to understand what are the Metrics which is driving you just like what I showed you. Like let's say it could be engagement levels or session duration. Second, try to understand the conversion rates, cart abandonment rate with respect to your project. Whatever is there, you take it. That is totally up to you. And use data collection tools. I told you Google Analytics, you can take it. But Google Analytics requires marketing data. And your marketing team will already bring you a lot of insights. You don't have to many times worry about that simple ChatGPT or AI tools data you can have make it as real time. Don't bring any fake data. Go to the online website sources and bring that. Second, you can able to use this tools like let's say hard your heat map sessions to understand the insights what people have. Did see these, you don't have to collect. Marketing people will definitely do that. But you will have to know how to interpret, ask questions and bring the insights. This is what being a designer you will have to do.

Okay.

And third is you need to analyze the data and clear it out the visuals. I told you, right? Take the metrics and understand and try to segment it properly. Let's say I took one example. But ideally you can take 15, 20 examples also and map it in a big way to take 15 different decisions for your project.

Okay.

And then you can visualize it and then apply that insight to your project. That's the most important thing. If you're not applying it, it will not happen. You will have to refine some things. You will have to understand split testing method. Sometimes idea Idea B you will have to implement see which works better. Idea B works better. Let's do more then split testing works that way. Okay. So whatever is your decision. And then refinement will happen after that and continuously monitor the progress. Because if you are a data guy, you should understand it will not work always seamlessly one way. You will have to always monitor, refine. Always monitor, refine more. Very, very important.

Okay. Yes.

So the next part is I think I showed you one live demo. Also like what you have to ideally do. Okay. Next is like what are the neuroscience backing behind it? First of all, if you project data in terms of patterns, people can able to provide an insight inside their brain. It is very easy for the people to see it in a graph, numbers, statistics so that people will be able to visualize the impact of what you're seeing. Oh, 16 of the users are using this. Okay, that's great. Okay. More than 20 to 30% of the user are using this. Yes, that makes sense. Oh, 90% of users are not using this. Okay, fine. See the numbers will give you the visualization clearly inside your mind what decision to take.

Okay.

And it provides you a data back position order to provide a rational thinking.

Right?

You can only fight the design teams with the numbers and statistics and data guys. You cannot do it just like that with random things.

Okay?

And I'll tell you, HubSpot 30% efficiency and 20% profitability gains. So HubSpot is projecting if you make your project data driven, you can able to increase efficiency by 30% and profit by 22%. Statista 23% increase in conversion rates with analytics. And also McKinsey is 70% higher than your user retention did, which is data driven decision making. See, very, very important, right? That is why all the businesses are taking decisions based on that. It is not going to be a random call, it's going to be a data call. What people are taking their products and service into the market.

Okay.

And you can use many AI tools. You can use Google Analytics. You can use. There is something called Google Sentimental analytics.

Okay.

You can use Power BI because these are all the tools. You just have to know. You just have. You don't have to be proficient in this. You just have to understand how this works. A simple Google search will help you out to get this clarity. And your marketing team will provide this data. You will have to just sit and understand.

Okay.

Amplitude. So to be very honest, I have not tried this tool. You can try it. Okay. It's a cohort analysis for engagement platform. You can able to try this.

Okay.

And there is a quick hack like let's say Heat Map is very easy to understand and analyze.

Okay.

Hard Jar, Heat Map tonalized web interaction AI powered A B testing, automated test with platforms like Optimizely. So these are all the platforms, particularly Heat Maps. Hard Jar is very interesting. You will know like in a screen where people are pressing more, where people are pressing least you can increase the call to action in that region. So something like that, you can make make a decision making towards this factor.

Okay?

Yes guys, thank you so much. I think we are coming to the end of this session. Please reflect on these learnings. It's a very short session but I gave you clear, powerful insight.

Okay.

And yesterday we had a very powerful webinar, five elements of design. In fact most people, I mean like close to 30 people came there and 40 people have registered for this webinar, Five Elements of Design. So this Sunday also we are trying to have another special webinar in another Team try to come there. The link is available in the description below. Every Sunday 11 o' clock we have a free webinar guys. You guys can jump on and take an action and take an advantage. Second thing is if you wanted to listen to our podcast and YouTube channel and I mean the subtract medium blog and everything, please go and subscribe because every single day we are producing content for you guys to learn and upskill.

Okay?

I wish you all the best for this day to be successful. Implement the skill because if you keep practicing this data visualization techniques, you will be able to get so much good and proficient about it. Just like watching this video will not give you any sense, you will have to take one data, implement it, try to create some stories, try to create some insights, try to create some what you call clusters around it. Try to present your team with the visualized data. You will see a lot of people will accept your idea. Instead of just saying this idea is good, good, good. Tell why it's good. Tell how much people are going to like this or maybe what are the insights which is making you drive this idea. If you talk like this visualization pattern with the PPT or something, you will be much valued like anything.

Okay?

In fact this is one of the question which our student asked and I believe I answered this. Okay, have a great day guys. Thank you so much. Any doubts is there Aravind Fal and people are there in the YouTube live. Go ahead please. I'll be more than happy to help you out.

Okay? Okay.

Sure. Okay guys, take care. All the best. Bye bye.

Bye.