ux and web analytics

January 14, 2022

UX and Web Analysis - Experts Zone


Pawel Ingielewicz

Head of Design
UX and Web Analysis - Experts Zone
web development(14)
ux/ui design(12)

“Pay attention to what users do, not what they say”- Jakob Nielsen.
Thanks to web analytics, we can see what users do on our application or website and, most importantly, understand why.

Watch the newest podcast by our Head of Design - Pawel. Pawel will tell us how the world of user experience collides with the web analytics world.


Hello and good news, everyone. Today you have the great pleasure of meeting me. And today we will be talking about user experience and web analytics and how those two worlds actually collide. How web analytics might help you user experience designer with your work, basically. How can you use data gathered from analytics, for example, from Google Analytics to simply create better designs and better understand what your users are looking for or what do they want? So without further ado, let's jump into the intro.

Why do we need web analytics?

So let's begin. Why do you actually need web analysis? So, basically a lot of us - user experience or simply designers - basically do our work based on very complex user research that was done. Maybe by us, maybe it's by some customer experience team or maybe by some other departments. However, we base our work on user research or any marketing research also. So basically on research. And thanks to this research we can create personas and determine what users might want or what might they look for in our website data or in our applications. What needs are we actually going to satisfy? But as Jacob Nielsen said, pay attention to what users do, not what they say.

So basically, without our applications, we mostly can rely on what people say, but not what they actually do. Of course, there is competition analysis or something like that that helps us understand what the users do on applications that are similar to ours. But, for example, what if our application is the first one, maybe have a very original, unique idea or something like that, even after the release of the product in any life cycle like introduction, growth stabilization, or decline.

What to watch out for while working with web analytics?

Thanks to web analysis, we are able to see what the users actually do on our website, what they type in a search bar, or maybe does the goals that we created for the website are being accomplished because, for example, we want users to communicate with us. We want them to talk with us, send us a message, cooperate with them. For example, this is our personal website or a company's website professional website. We want users to do something and while designing, we are designing based on the research. But we are not always sure if this is the maybe right idea. That is why we can use web analysis, such as Google Analytics, to check if those users actually do what we wanted them to do, and maybe why they do it. We can see whether or not expectations have been met through design. This is, of course, a lot of taking care to take in. But there are some things also that we should watch out for while working with web analysis, because when you sometimes read something that has so much data within it that you don't actually know what to look for, because there's so much of it that like every kind of data we could imagine, even though one kind of data might, for example, answer your multiple questions. And here's the thing, what questions should you need to answer for or what data is actually meant for you? Because, for example, if you want to check if people are typing, for example, in the search bar. So maybe you have some kind of a promotion for certain products and you see that this product doesn't get any or maybe a small number of unique views. And why is that? And then you might ask a question. Okay. How can I help myself with this problem by web analysis? So okay. What do I know, actually, is that the people are not typing in the product that I actually have a promotion on.

So what are they typing on this? What are they typing? What do they search for? Those are the kinds of questions that you might ask yourself while working with off site web analytics. So, for example, when you want people to visit a certain website and they are actually not visiting it, you can search for what people are actually clicking on, what they are visiting, maybe a heat map even, to check what they are catching on or what they are looking for because they are looking, for example, at the website of your service. Why are they not looking at your service's website? They are instead looking at, for example, portfolio websites. So we know that. And there are certain things we might do about it. We might, for example, maybe get a link to our services web pages in the portfolio website, where this is, for example, we have a project based on the health system or fintech. And then what you can do is give our services link there under this project, for example, in a portfolio. And by that, people can see. Okay, so they are doing some things for the fintech or the health industry. And then you have to check our services about our approach to the health or fintech industry.

And that way maybe you could get those unique views on your service's website. This is one thing. So this is the kind of thing you might do with it. But also we have a question here from all the data that you can analyze, which ones are meaningful. But as I said, there are many data on that, and which ones might be meaningful for you? Well, this is a tricky question, actually, because it relies basically on you. What kind of answers are you looking for and what kind of data you can do something with for yourself? For example, as I said in the previous example, you want to see what are they doing to people if they are not checking on your service website. So the meaningful data here would be, what are they doing else. What are they doing else, like, what are they checking? For example, for this example, I gave you a moment ago, it was just a portfolio.

So this is like something when you're starting on this because basically the web analysis is a huge thing and you do not have to learn or know everything. As a user experience designer. And when you first go there, there's a lot of thinking. There are actually people whose position is basically web analytics processes. All of that they do is basically looking and getting Google analysis or any kind of other software, and they're analyzing the stuff. So sometimes you won't be even doing that by yourself. You will just ask them about a certain question and they will gather this data for you. When you are starting with this, don't get overwhelmed. Maybe ask someone who has some experience in it to help you.

But basically what you must do is not get overwhelmed by all this data and segregate it in your head, like, what do I need? Actually, because there's so much of it. I can't even give you all the examples., there are so many of them. just look at it in your own head and search for what you're actually looking for and then check. Okay, which data might actually be satisfying for that. And this also relates to the second thing, which is which data can actually answer your question. There's so much of it that you need to actually think sometimes really hard. What data actually might answer the question that I have. And the third one, basically, as I said, take the time to learn it. The user interface design and user's experience are really complex. It's not an easy go to the app and do this or something like that. No, this is very complex data because it is mostly created for professionals. It's not created for common people. So just take your time, learn it, learn how to use it. Because as I said, as a user experience designer, you won't actually need all of it. And then as a user experience designer, how does one can simply think of a sort of like, if there's so much of it, then how can you actually work with it?

Web metrics in the measurement plan

We can have web metrics in this web measurement plan. So this is an example taken from the Newson Norman Group. But this is actually a pretty solid one. This actually kind of involves a lot of aspects. So this goal that we are having is 100 consulting leads per month. This is our goal. And what we have, as I mentioned, step before is desirable. Action is to visit consulting service section. This is the action that we want to do. And how do we use it in our web metrics? Well, basically, by web metrics, you can search for unique page views. What are unique page views? Unique page views are the visits of a certain user. But this user needs to be quite unique. So if one user is going in and out to the service section, it doesn't count as a unique view. The unique view is, for example, if I check it, this is one unique view, and then you check it this is another unique view, but if I leave it and go with there again, this is not a unique view. This goal is that we have 100 consulting views per month.

We already like to establish an action for the user, which is to visit consulting service section. And how do we check what we are doing, how the design is performing? We simply can check web metrics by checking unique page views. And this is one of the steps to achieve your goal. Remember, just visiting the service section won't actually get you consulting leads. What the user needs then is, for example, to read something that will convince him to contact you. So the first, the second step, for example, second, desirable action would be, for example, any kind of article, any kind of something that would let the user think that okay, I want to work with this. Some kind of a catch, like gathering her attention, then interest, for example, as in AIDA, it means attention, interest, desire, action. So we get the attention of the user. He's clicking on the service section. Then we need to get his interest. Maybe we can get his interest by showing him some of the work that we did. Maybe we can just write some text that will convince him. So thanks to this text, for example, we get their interest and can gather a desire, and then we will get an action, which would be given us this consulting lead.

So those are the next, for example, desirable action from the user would be actually to read what we have there or watch the video or watch the gallery that we have there. That's an example. And the next desirable action would be to contact us. For example, there will be three of them and we can use and we can use a on site web analytics process and identifying key performance indicators for all of them.

This is just one of the examples that we have, which is unique page views. For example, you can check if you've started playing the video or if you looked at the Photo Gallery, or if you want to see if you read text or most people, if they read something, they will actually put the cursor on it.

So you can see for the heatmap if they are actually spending any time on it or some pages allow for eye tracking, which isn't maybe too comfortable for your users. But those are the things of human computer interaction. Those are the desired actions that you can check by the web analytics tool. And then you can see if your design is actually meeting your goals, your desirable actions that users should perform. And this is something that you should keep in mind while simply looking at the analysis. And then you can perform some maybe corrections to your design and check if something maybe isn't working. This happens. Nothing wrong with that. And if something doesn't work, let's make it work. And this is why web analytics tools can help us with that. Simply like in this example checking unique page views. If people are actually seeing our service section.


Another thing which we need to kind of talk about is segmentation. So kind of narrowing down some things on it. We have so many users and we have so many personas, we need to narrow it sort of down to gather the more specific info about our users. So as I said here, if you are basing your segmentation, your persona that was created by user research, you can do this. So explore statistical data to show trends.

So there might be some kind of different reports that you might encounter different results from different segmentations.

For example, segmentation of technological segmentation, which says, for example, these people are whether using a desktop or mobile versions or are they using Android or Apple devices. You can take that into consideration, compare and interpret web usage, for example, the general report or any other segmentation like, for example, demographical segmentation, geographic location etc.

So, for example, you take demographic segmentation and then you see that most people in, for example, United States, in, for example, Oregon, are checking your website via Apple devices, and then by that, for example, if you have any promotion that is like, for example, area locked, sort of. So, for example, you have a promotion that only applies in New York, then you can check for your segmentation, which is what kind of devices users are working looking for and where are they? So this should help you to show some trends. Another thing is finding the right answers to specific questions. So finding the right answer to specific questions is basically narrowing down this for a specific persona, just as it says here, it's better to always look in a specific case sometimes, and it's always better to look at a specific case than in general audit. General things don't give you any specific information, any key information.

It just gives you the general one. And general ones are actually quite tricky. And if you specify it, maybe some kind of a persona that you actually know that is using it. You might find some very surprising results and find answers there. If you look, if you cannot find something basically in a general audience, you could see it in a very specific, narrow down persona segmentation. But else, as I said, I mentioned segmentation, technological segmentation, demographical segmentation, or persona segmentation. So what kind of examples of segmentations are there? Well, as I said first, would be the persona segmentation because it's mostly not always, but a mostly good thing to first narrow it down to a certain persona and then check within that persona and another kind of segmentation, as I mentioned earlier, comparing different segmentations, different results of segmentations to each other. So, for example, here, this is personal segmentation. However, personas are mostly really complex. Sometimes, for example, we have a persona that specifies a lot about this persona.

So it's not always a good idea to base it only on a persona. So when you have user research that allows you to create those personas, now you can divide those into different segmentations. Like, for example, like in here, technology segmentation. Technology segmentation is what I mentioned earlier. Basically, when you, for example, check if they're using Apple or Android devices, whether or not they're using a desktop or mobile version of your application. And okay, this might be or might be not crucial. But then you think that some things are different on iOS or Android devices. So for example, if your audience in 90% uses Android devices and only 10% uses Apple devices, then you should make your app, for example, similar to the Android devices. Some things are on the left on Android, and some things are on the right on Apple. For example, when you go to the mail application on Apple, if you want to get to the options of a certain mail in your inbox, you have to swipe from right to left in Android, it's from left to right. So this is a difference. And if you have something similar, then you would like to do it from left to right because most of your users are Android users.

So this is an example of what technological segmentation is, but also segmentation could be various things and it's up to you. It could be demographical, could be technological, could be by age, for example, segmentation by age. And this is like depends on what you want. But basically, if you have a persona, you can divide it into those segmentations that you actually might need answers for, or simply not even by persona. But checking what kind of people are using, what kind of devices were at which age, and stuff like that, and then basing on this, you might get some great results for your application for your designs. Of course, as I said, as I mentioned earlier, it's quite tricky, because sometimes you might catch yourself getting so much data and comparing everything to each other. That then you have, like, 100 pages of a report. But from those 100 pages, there's actually very little to be done about the design because it's not very specific about log file analysis. For example, you have 100 ideas and which ones you should take and improve in design because you say that 2% there is using this.

And for example, 10% there is using that. So it's really hard, but it's manageable to get this meaningful information that you think might actually help the user. So set up some goals, then set up a desirable action, and then use your web metrics to see if this desirable action has been actually performed by the user. Don't just go around and look at everything because it will be first, overwhelming, and second, it won't give you a lot of answers. It won't give you the answers that you might be looking for. The best thing is always to set up a goal that you want to achieve and disable action for the user to do so, the goal will be achieved. And the last thing would be by web metrics checking whether or not this goal was achieved, and if not, this desirable action was achieved on a web server. And if not, then check, web metrics, what else we might do to get this desirable action going. So that's it for me today. Thank you very much. And we post our videos every Friday, so keep out for those and have a nice day. Thank you very much. And see you soon. Bye.

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Meet the author


Pawel Ingielewicz

Head of Design

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