Analysis Workspace Tips & Tricks you need to know in 2021

Transcript

All right, hello everyone, welcome to the session.

I’m Fredrik and I’m your host for today. And I’m going to talk to you about all the Adobe Analysis Workspace tips and tricks that you need to know in 2021. So taking a first look at our content, like we’re going to start with the very, very basic and boring stuff, I’m going to tell you a bit about me and why I love Analysis Workspace so much. Now, let’s talk about the basics of the process. Then we’re going to get straight to the content, talk about some of the cool use cases that we can do with the visualizations in Analysis Workspace, some advanced use cases to round things off. And then we’re going to have a live Q&A, and I’m going to be joined by one of my best friends at Adobe for that. So I’m really looking forward to that part too. So starting with me then, this is me, like this is my picture from like 10 years ago, but it’s still the most kind of professional thing that I have about me. And I’m working at DHL, as you might have guessed, and I’m doing this analysis stuff for quite some time now. And most people by now know me for my blog, fullstackanalysis.io, and I’ve prepared this little QR code for you. So if you want to check that out, check some more cool use cases out, you can just scan that QR code and it will take you straight to the page. And since I’m doing a session alone today, I don’t want to feel so alone on the recording. So I have a friend of mine who is joining me via my soundboard. Hey, this is Eric Matosoff, and my role at Adobe is as the principal evangelist for analytics and data science. So he’s going to join me for a few seconds here and there, and we’re going to start with the content right now. So why should you at all care about analysis workspace today? Like some of the reasons why I love this environment as much is because it really works for all use cases that I can think of for data. Like recently I’ve been in a Twitter discussion around which front end and which tool you should use for your data. And my answer to all those use cases was always analysis workspace. And that’s super cool because it really works for everything that you could do with data and also one interface that you can really throw every business user analyst data science test at, which is just super in terms of enabling a whole company to work with that data. It’s also your gateway into Adobe Analytics because you can create all the content that you need, like dashboards and reports in there, but also other stuff like segments and calculated metrics. You can all do it from one great interface. And since it’s also the front end of customer journey analytics, it’s also super future proof because it’s not alone covering you today, but it will also cover you in the years to come. So it’s super cool to invest your time in that tool today. So to go into this very exciting topic of analysis workspace I’m going to introduce you to our heroes of the day. And I know you know all those visualizations by heart. So just showing you the icons here should already tell you, we’re going to talk a lot about the cohort table, the histogram, the fallout and the flow chart, which is like just my personal superhero team of visualizations in analysis workspace. And you already know if we are talking about the cohort table, that it’s super easy to analyze groups of visitors over time, which is what it’s made for. But it also can save us a ton of work if we want to build some more complicated segments. And also if we want to compare, for example, traffic sources, and I’m going to show you how to do that on top of the base functionality. Then the histogram is also super cool if we want to group visitors or visits just by their engagement, which is kind of the core use case of that, but it can also be used for even more complex segments or also to compare different segments to each other, which is what we’re going to look at today. The flow is of course, sorry, the fallout is of course perfectly suited to just visualize defined parts on your site. So if you already know where your visitors are going, you can just use this to visualize that journey, but it can also be used to create some more segments and also to create some super fancy calculated metrics on the fly, which is also one more use case we’re going to cover. And then to round things off, we also have the flow visualization, which is of course very good to explore path on your site, but it can also be used to create a next and previous item report, for example, like a next and previous pages report, and can also save us a lot of time doing segmentation. So starting things off with the cohort table then. Like this is what you of course know from Adobe Analytics already, like how the interface looks like. If you just start with that very fancy visualization, like you can create a inclusion criteria where you just select which type of users you want to track and a return criteria in terms of how they should qualify to be tracked over time. And one of my favorite, even more advanced use cases for that is for example, if we add a segment to this very, very simple inclusion criteria here, we can for example, analyze what people do after their very first session on a website, which is just a very nice extension to this functionality and one of my favorite things to do. What we will then get is this very nice chart where we can see how our groups of users, our cohorts behave over time. So for example, in my very basic example, how those people who came to the site on a certain day, then later come back if they do come back. So this is what you already know and what you already have learned from other Adobe videos. And now we are going to take things a bit further than that. So one thing that at least in my opinion, not enough users actually do is they use this advanced functionality setting here. What I’ve done here is I just threw the marketing channel dimension into this box down here. And what that will give us is instead of just giving us a cohort table for all users, it’s actually going to break down those marketing channels and show us the retention in this case for all those different cohorts of marketing users, which is super cool. What I really like to do is not only throw the marketing channel in here, but also things like, for example, a marketing campaign. So if you are analyzing a certain channel, you can also analyze individual campaigns. In terms of content, you can also do some fancy stuff like throw entry pages in here, for example, and analyze how people who came by a certain page in the very first visit then later came back and how that changed their retention, which is super cool. You can also do other fancy stuff like, for example, throw the mobile device type in here or the visit number desk, countless use cases for that too. So one thing that I once heard a very wise man say was, when in doubt, just simply right click. And that is exactly what we’re going to do here because in this very nice visualization, we can also right click on certain cells. And this is where it gets very fancy because here we can now select to create a segment from that cell. And I’ve done that in this case. And what you’re going to see is, it’s creating a very complex segment that I would not trust every user to build on their own, but that we can now just create using this visualization where we create a segment for all the users who came by a certain channel on a certain day and then came back later. We can also do that with multiple cells that we can select. And if we just simply right click, we can do the same thing and just create very complex segment for those kinds of users. And this runs our very first example off. And we’re going to look at the histogram visualization next. All right, so for our next use case, we are now going to look at the histogram visualization. And what you already know from the interface is of course this very, very simple select box where you can just select or drag and drop your favorite metric in here. And what it’s then going to do is it’s going to cluster in this case, all our visitors and throw them into buckets depending on how many visits they had on our site. So we can see that this many visitors actually had one visit, so many had two visits and so on. And that’s kind of the base functionality of the histogram. And it’s of course, very well known to all of us. So my first step is of course, to again use the advanced settings here, because here we can change not only the metric, but also the counting methods. So for example, if I want to see not only visits by visitor, but in this case, how many online orders happened in a certain session in a visit, I can just do that by changing the setting around a bit and see how many orders I had in a certain visit, which is super cool. But there’s also some more that we can do because you might know this visualization from the bar chart visualization. And what we can do if we click this little dot up here, we can actually click on show data sources, which is going to show us a new table. And Eric, what do you think of that? Check out that awesome set of buckets. Indeed, because now we can actually see all the different buckets that Adobe Analytics has created for us. And one of my favorite things to do here is if we hover over one, we can actually select this little eye icon here. And what this is going to show is the exact definition of the segment. And what we can also do here is we can select to make this segment a real segment and use it just like any other segment in Adobe Analytics. And this is a super fast way to create segments for a lot of different groups of users. For example, people who just placed one online order in a certain visit, there’s at least to my knowledge, no faster way to do that. For example, also if you want to do it for more than one group, because we have all those visits clustered in those segments for us already. And we’re going to take this even further because here we now have a simple freeform table, right? And we can edit this freeform table like any other one. So for example, I just dragged the mobile device type dimension in here, and now we can break down all those different segments by the mobile device type to show us this nice histogram of the engagement per device type, which is super fast. Like I don’t know any faster way to do that. And it’s super cool to just do that in the interface. One of the kind of basic things that you can do is, for example, this device type example, but you can also drag other dimensions in here. For example, try the visit number dimension to show you how the engagement changes depending on what session a certain visitor falls in, or also things like first and return visits, marketing channels, campaign, it’s all going to work and it’s all really cool here. So since we now have our freeform table ready here, one other thing that we can do is we can drag other types of visualizations in here. And you can see now I just dragged a simple bar chart in here because what that will let me do, as you know, we can now, for example, normalize our data rows here and just see how the engagement for those certain device types differs from one visit to the other, depending on when they came, which is super cool because now we actually normalized it. And I think it’s a bit easier to read than, for example, this chart on the left. So this is one of my favorite things to do with the histogram visualization. And we don’t spend any time waiting because now we actually go to our next hero of the day, which is going to be the fallout. And you all know the interface already. Like if you just track the visualization in workspace, you can see all the visitors, and then you can add touch points to do your fallout chart, which for example, in this case, I’m now visualizing how many people went to the homepage and from there eventually ended on the articles page. Like this is what you already do and know about this visualization already. And one more advanced use case that I really like is if I drag segments on the top here, I can then also compare those fallout funnels for those different segments, which at least my opinion is super cool and super easy to do. So this is whenever the business asks me to create this kind of funnel, it’s super easy to do that with segments that you can just drop here. So since we are already very used to those advanced use cases, one other thing that I want to show you is we can actually select different containers here. For example, we can say those kinds of interactions should happen within one visit or for one visitor, which is very nice. And we can also select if this should happen in direct succession with the next hit or somewhere just along the user journey in a visit or visitor, which is quite cool. So I promised you that we’re going to build some segments from that. And again, we’re going to just simply right click on this very nice visualization because what that will let us do is we can create a segment from a touch point. And like in this very basic example, you’re going to say like, this is very basic. I could build this by hand. Like why should we use the visualization for this kind of very simple segment just from people going from the homepage to the articles page? And that’s true. But for example, we can build some more complex funnels here, right? So in this case, I’m visualizing how many people actually went from the homepage to either the events or articles page and then to the forum page. And that’s quite complex already as a journey. And if I build a segment on top of that, now things look a bit different because this is not so simple anymore. And I wouldn’t trust my business users to necessarily build this on their own. But with the NASA workspace, we can just right click on that fall out step and just create a nice segment from that. And this is now quite a step up from what we’ve seen before. But again, let’s go even deeper. So again, we just simply right click, thanks, Eric, on this point in the fall out chart, and we’re going to trend our touch points. And what that is going to give us is a nice, as you might have expected, trended view of those touch points. So we can now see the conversion rates of those different funnel steps visualized over time, which is quite cool. But that’s not all because as you can see, we now have a simple line chart here. And what we can do with the line chart is again, click this little dot and show the data source, which is going to reveal that we have a nice freeform table now with some calculated metrics in there. And those calculated metrics might be super useful to your business, so you might want to keep those. And what you can do is again, click the little eye icon here and say, I want to keep this calculated metric. I want to have this for my business to use. And then they are going to be able to not only create those conversion rate metrics, but also share them in workspace with other users even, and create all those fancy metrics on their own. And I don’t know a faster way to do that. If you know, maybe let us know in the chat right now, because this is a very, very good use case for this type of visualization. So we have one last hero for today, which is going to be the flow chart. And it’s just one of my favorite visualizations all over because it’s just so nice to look at, but let’s take a look at what it can do for us today. So as basis, we know that if we drag it into another workspace, we have those three boxes right here where we can just drag any dimension or dimension item just in one of those boxes. So if I drag something to the middle, you will see that we then get this very nice flow chart where we can see how many people from the homepage in this example went to another page or what was the page before that. And that’s very nice because one of the things that we can then, of course, do is we can click on those icons here and then break that user journey down even further. So for example, now I’m visualizing how many people went from the homepage to the search result and then back to the homepage or maybe even to the category 5 page and then also back to the homepage, which is quite cool. But I also want to just stress here because it’s something that people like to forget is you can also drag and drop items to the left and right boxes. And that will give you a nice entry chart, for example, for my page reports right here. So an entry page report. And we can do the same on the right hand side with an exit page report. And because this is now analysis workspace, we are not limited in the way that we’ve been before. We can only do that, for example, for props. But we can also do this for any other kind of dimension. And we can also do this in customer journey analytics because then we don’t have padding anymore. But since we have the flow visualization, we can just drag and drop any dimension in there to visualize those user journeys. So once again, we’re going to just simply right click on the node here because now we also want to go into segmentation from the flow visualization. And also here, we can just click Create Segment for this part. And this is going to create a segment where it’s going to respect that those two steps could have happened in the path. So for example, this is the segment that it would create for that. And just look at all those conditions, all those kind of restrictions in here and how nicely this works and how little time I need to spend on building this. Because even I, as a quite experienced analyst, wouldn’t know, from the bottom of my heart, how to build this by hand. And also, business users who want to use this tool wouldn’t know how to do this on their own. But with this visualization type, they can just click on an item they want to have as a segment and they can just start building it, which is super cool. Now, let’s go even deeper here because you see I’ve visualized my pages here already quite nicely. But what I can do now is I can select Breakdown as another option in the same menu. And what I’m going to show you is what happens if I select the same item again, which is Page. And you’re going to say, well, you already have page on top here. Like, what are you going to show us? We already know what the result will be. And you’re completely right with it because what you can see here is that we have 1,014 hits on that specific page. And we can also see that in the Freeform Table right now. So you’re going to say, what is the added value of doing that? So let me show you not only that the numbers match up, but also that we have a nice segment on top here again. And of course, we can do the same as before. We could make this public and reuse this and all the fancy stuff. But what we can also do is we can edit this segment now. And what you’re going to find in the Segment Builder is then a condition that currently restricts this kind of segment to the certain page that we clicked on, so Home page in this example. But what we can do now is we can just change this segment. So we can change this to just, for example, Page Exists. And this will now also change the table that we’re creating. Because what you can see now is we don’t only have a table that is showing us exactly one item that we clicked on, but it’s showing us all the items. And you’re going to see that those numbers right here match exactly with those numbers right here. So everyone who says we can’t create something like a previous and next page report in a NASA’s workspace, like you’re wrong with that. All you need to do is replicate those exact steps that I’ve shown you. Because we can actually just, for example, in this very example here, we can just visualize what the previous page to the Home page was. And you can see that the numbers match up perfectly. So we have that functionality already. And we also have it in Customer Journey Analytics, which is super cool. Because as we all know, that tool is going to be the future at one point. We better prepare and learn how to use it today so we don’t feel left out in the rain. So that was a lot of content about all those favorite types of visualizations. And we’re going to get to the conclusion already. So of course, if you use a NASA’s workspace, doesn’t matter if it’s in Adobe Analytics or Customer Journey Analytics, make sure that you know all about the build and functionality of those visualizations. Because that’s where all the value is in a NASA’s workspace, right? This is where all the different things come together and where the Adobe team is putting a lot of work in to bring all the data from the back end to those visualizations. So make sure you deeply understand how they work, what they do, and don’t feel shy to just try to break things, try to dig into them, try to hack them, and see what you can do on top of those built-in use cases. Also, of course, be super curious and explore what those data sources contain, like that little hack that I’ve shown you before, where you just click on the little icon and it shows me the data source. See where you can do that also, because I’ve not shown you each and every place in the NASA’s workspace where you can do that. So make sure you explore even more beyond that. Of course, when in doubt, just simply right-click. Thank you, Eric. So of course, if you have the chance to right-click anything in the NASA’s workspace, do that. There’s so much goodness hidden behind those menus. And really make sure that you explore them all, because there’s going to be a lot of value for you. And you want to know all about that. So then also, if you don’t, if you feel stuck and you don’t know how to progress further with your analysis, try to combine different approaches. So for example, what would happen if you create a segment in the, for example, cohort table and use that in the fallout chart? That’s something that you can do. And then maybe you build a histogram visualization on top of that. Like you don’t need to restrict yourself to only use one of those options. You can mix and match them. And that will give you basically unlimited possibilities to analyze your data in a brand new way in NASA’s workspace without even changing your implementation. It’s going to be the same data, but it’s going to create so much more value for you if you just let it do that. So that was all the content that I have prepared for those basic examples that I’ve promised you. And now also some further examples of what you can do in an analysis workspace today. Because what I’ve done on my blog, and you can find all the posts there, is for example, I’ve created this very nice dashboard where I’ve done some kind of stock trading style calculated metrics and visualizations in an analysis workspace where we can just see how our traffic, for example, is trending, how the different trends start and stop. And we can create some very fancy trend detection metrics from that. And your marketing team will be very, very grateful if you give them an indication if they are creating trends with whatever they do, or if it’s just going to be another addition to their baseline performance. So that’s something very cool. And another example that made some waves like last year is you can also do forecasting in Adobe Analytics. You can also do forecasting in an analysis workspace. Like there’s nothing stopping you to just be calculating metrics that use all the predictive power of Adobe Analytics to, for example, predict your sales for the month. Like that’s something you can do today. And you should really check out those posts so you know all about how to do that. So I’m done for now. And now we’re going to head over to the Q&A session. I think I have a very good friend joining me. So I’m handing things over to the present Frederik. And I thank you a lot for attending the session. Awesome. Thank you so much, Frederik. I’m so excited to have you here. It’s great to see you, buddy. How’s it going? Hey, Eric. Doing great, thanks. I think I saw you brought your drumsticks as well. What’s your brand of choice? I’m a Vic Firth 5A kind of guy. Yes, I’m also Vic Firth. So those ones are 5B. So like a little bit on the heavy side because all that heavy metal drumming, like you need some great and reliable sticks for that. Yeah, I see. I was a big Blink 182 cover band kind of a thing. So I didn’t need that much force. Awesome. It’s so great to have you here. What a wonderful presentation. Went through all sorts of crazy things from flow, fallout, and even like getting into some of that crazy forecasting that we saw on your blog for fullstackanalyst.io. So we really appreciate you taking a little time here. We’ve had a slew of questions come in as well as some friendly comments that we’ll go through. And hopefully you don’t feel the need to use that soundboard again since you’ve got the real deal here. So it’s really good to chat with you. Yeah, very excited for the questions. And like if I want to copy and save some of them, I know all I have to do is just simply right click and select copy then. Also, that’s in doc. That’s all you gotta do. Awesome. All right, so our first question, let’s jump into it. Our first question comes from Jordan, who says that they find the flow charts are great visually, which I have to agree, they’re very colorful, but they need a lot of interpretation. And oftentimes they need a little data behind it. Now they’re asking how can we get that next page path easily within a table? And I think this was actually asked earlier on in your presentation and you somehow foresaw that Jordan would be asking this question. And prerecorded the answer. But why don’t you kind of walk us through a couple of the high level steps. Yeah, yes, some predictive analytics and some predictive content creation there. But yeah, it’s actually pretty easy because all you have to do is once you create your multi-step fallout report and analysis workspace, you just right click, break it down by the dimension that you’re already using in the flow chart. And then you can just break that down by the same dimension, edit the segment, remove that one condition there, and it will just give you a next and previous page report. And the great thing that I like about that is it’s so much better than what we had in the old reports and analytics interface, because we can do multi-step flows in there. We can do branching paths in there with all those options that I’ve shown by just clicking on multiple nodes and then breaking that down. That also works. We can do it across dimensions, across visits, across visitors. It’s all working so nicely. And it’s just a few steps. And if you dare to create and edit that segment out of the flow chart, that is going to bring a lot of value. And you can do it with all kinds of dimensions. Really cool. Yeah, yeah, I totally agree. The flexibility that you have there is worth the extra two or three steps to get to it. I’m trying to remember. Do you already have a blog post on building that? I feel like you had one for CJA. Do you have it for Adobe Analytics as well? Awesome. Yes, yes. There’s a post about that a year or a year and a half ago. And that’s on the blog. And it works for Adobe Analytics and Customer Journey Analytics because all workspace, right? Yeah, awesome. Yeah, I love it. Do you have a good example of when you needed to use that in your life over at DHL? Yeah, the prime example always is marketing, landing pages. People are on a campaign. They want to bring a lot of people to a website. But they don’t want them to stay there, right? So at one point, they’re going to ask you, what did people do next? To which page did they go? And what was the actual goal of the campaign? And did we get them to the right section of the page? And you can just bring that on any dimension type in an Azure workspace by just creating that very, very simple table. And that’s going to work for everything. The other use case that I also really like is given that you have owners for certain pages. So for example, you have a product owner that owns the home page. Why don’t you just show them how people get to that page and what they’ve done before and after? You have two nice tables, one previous page, one next page. And you’re going to really show them what role that certain page plays in the whole user journey and how that person is creating content and value for the whole company. That’s really, really cool. Awesome. Yeah, I love it. I think both of those are really great use cases and the types of questions that we get every day as an analyst from business users.

I’m not sure if I’ve seen it in Experience League already. But I would recommend to anyone watching, search through Experience League. And if you don’t see an answer similar to what Frederick just described and is on his blog, you may even want to submit it as an idea that would get pushed over to our product team for them to think about, like, is there a way that we can reduce those two or three steps that Frederick walked through to just simply one? You drag and drop, and you’ve got the next page or previous page or any dimension. And so I would highly recommend checking that out. It’s worth spending a little time there. In fact, I would say that for really any of the questions that we talked about earlier with Andy based on the content that Christo shared, dig into some of that Experience League content and upvote some of the great ideas there. All right. So you ready for the next one? It’s going to be a toughie for you. Absolutely. Good. All right. This is Dump the Frederick is what we should have called this session. Great. So the next question comes from Amils, who asks, ooh, and there’s a good one. Aren’t cohorts in Adobe not very accurate since it’s going to depend on visitors, it’s going to depend on cookies and browsers, and there’s incognito, there’s ad blockers, there’s privacy and consent. And so those cookies are being blocked and auto-cleared more today than they were yesterday and more tomorrow than they were today. And so Frederick, what’s been your take on trying to better utilize cohort tables and especially cross-visit, multi-visit analyses in this world where cookies are less reliable than ever? That’s the real challenge for any analytics tool out there right now. And I think one of the technical answers that the whole industry is steering towards is bring your customer identifiers to your analytics tools, use login functionality and all that. And especially in customer journey analytics, which is created in that world where that challenge already exists, is super tailored towards that use case. And in the AP, it’s super easy to just join your data on custom identifiers and really redefine your visitor logic. Like for Adobe Analytics, we also have cross-device analytics, which is in the ultimate product layer, if I’m not mistaken, which is the perfect example for Adobe Analytics making use of basic user functionality to bring your custom identifiers to the page. And also for mobile apps, you largely don’t have that problem. Of course, cookies are really a challenge if you want to analyze long user journeys on the web. But also if you use Adobe Analytics for mobile apps, it’s going to be a whole different game because there for some reason Apple doesn’t restrict us in terms of how long identifiers can live because they want us to use mobile apps. So besides all the technical discussion there, what you can actually do is you can limit your analysis to only devices, for example, where you know that cookies can live longer. So for example, if you exclude Apple devices by a segment, that will already give you longer cohorts and longer retention times of cookies just because that’s how it works. Also, you can try to cookie-proof your implementation by using C names, using proxies in front of your data collection servers, and all that. But yeah, for the actual analysis itself, it really helps to segment down to devices where you know that cookies have a long lifetime and then see if you really need three months of retention or if the same analysis can be done on a shorter date range. Sorry, that was a lot of answers to that, but yeah, it sparks a lot of thought. Yeah, no, I appreciate the extensive answer because I think it covers really all different angles of how the changes with cookies, the changes with browsers, the changes with government regulations apply to our day-to-day lives of analyzing that data. The only thing that I’ll add to that, and thanks for mentioning CJA and cross-device analytics as well, both of which are good options for applying that like a hashed ID that is based on a login or based on an email click-through or something along those lines to stitch that data historically together. Another consideration for you to consider is just, I think last week, it might’ve actually been two weeks ago, here at Adobe, we kicked off a beta for what we’re calling today, the customer device ID, where you have the opportunity to basically set your own Experience Cloud ID and then save that in your own cookie or storage location. And what that means is, generally, Frederick and I can wink at each other that we’re talking about ITP here, that in an ITP and Apple’s Safari and iOS 15 world, your cookie, even first-party cookies may die within seven days, but if it’s set using a server-side HTTP response header cookie, then it can last two years or even longer like the old days. And so the way that we’re working with that customer device ID beta is we’re having customers own the value that is passed into the Experience Cloud ID service, that way it can persist significantly longer than those seven days. And so to expand on what Frederick was saying, that the answer is not a quick answer. There’s lots of different ways to be thinking about this, and from customer journey analytics, cross-device analytics, limiting the length of your analysis for cross-visit analyses, and then also the beta version of that customer device ID solution. There’s lots of different ways to tackle it. And so it’s really important to familiarize yourself with what’s possible, where the limitations are, how that’s affecting your data.

And then I’ll throw one more plug in there. We have a relatively new workspace template that actually is called, if my memory serves, ITP Impact, and it’s built for you to learn more and analyze how ITP has affected your business. So if you haven’t checked that out, Frederick is nodding, and that may mean that this is news to him. Frederick, have you heard about that before? Indeed, sounds exciting. I need to take a look. All right, how about that? I’m teaching Frederick, I’m feeling good. Great, okay, so next question comes from Yulu, and they’re asking for fallout, next hit. They want to know, does that really mean next hit, or does it mean next page view? And so Frederick, I’ll turn it over to you to help answer that one. Yeah, that’s a quick one, like next hit is next hit, like in normal segmentation as well, if you select next, it needs to be in the next hit.

Yep, yep, nice and easy answer, and good question though, because that’s really where the power of workspace comes in, is we’re no longer focused on just page view, page view, page view, page view, but you can focus on hit, you can focus on multiple dimensions, in a world where maybe fallout is limited to just page dimension, and you don’t have the flexibility of workspace to mix and match your dimensions to your heart’s content, then with just pages, maybe next page would be fine, but next hit is totally necessary for what we’re trying to do, and as we talked a little bit with Andy after Christos’ session earlier, if you want to learn a little bit more about how fallout works, just simply right click, and you can create a segment, and it’ll kind of give you that underlying view as to what’s happening beneath the scenes.

Awesome, didn’t need to use the sound port. Yes, yes, exactly, yeah, I pressed the button myself.

Cool, all right, so we’ve got some great feedback from Tim, excellent stuff on the fallout tables, I couldn’t agree more, well done, Frederik. Thank you. And all right, another question from Yulu here, where they’re asking for the flow report, why is the metric path view used, which is something kind of similar to occurrences, or wouldn’t the metric page view actually make more sense here, and what are the benefits to using path view? And that kind of aligns with our conversation about next hit and eventual, so Frederik, I’ll let you kind of take it from there. Yeah, so the flow visualization basically doesn’t restrict you to just viewing paths across pages, right? So you can drop any dimension in there, you can drop visit level dimensions, like entry page, exit page, you can all drop that in there, and that would still work. So based on that kind of prerequisites, like if you would just be able to use page views, that would really restrict you, because maybe you want to see people who went to the homepage on which item did they click, so what was the next link click, or what was the next exit link, for example, and for that you need to have a metric that is kind of abstract in that regard, and that’s exactly what occurrences, which in this case I call path views, are which are just very, very abstract and usable in each and every context, so that makes a lot of sense in this analysis here, but I agree if you limit yourself to pages, like it should be equivalent to page views, and you can change it out if you like, but you don’t do anything wrong with past views or occurrences. Yeah, yeah, I totally agree. I think it’s path views instead of page views simply because it is meant to be more flexible there for you.

Cool, let’s see, we will, you know what, I think we’re actually out of time. There was one question, Frederick, about someone missed the QR code for your blog. If my memory serves, it’s fullstackanalyst.io, and I can feel confident that Frederick will be tweeting that out shortly to make sure that anyone that missed it will be able to check it out. There’s some superb, superb content on there. So, Frederick, thank you so much for taking the time to present, and then join me live here for the Q&A.

But rock out, dude. All right, it’s good to see you. Always a pleasure. Thank you for having me.