Rockstar Segment

Transcript

Next up is the Adobe Analytics Rockstar session, where we have assembled three Adobe Analytics super users that we like to call rockstars. And if you haven’t seen my shirt, this is my Analytics Rockstar shirt, one of like a thousand that I have. And what we will be doing is going through step by step, each of the three presenters. And then at the end, you’ll have the opportunity to vote on your favorite. So first up, we have Danny Cessmero-Alvarez.

And Danny is a Senior Associate for Analytics over at Merkle UK. And he’s been working with Adobe Analytics since starting at Merkle EMEA over three years ago. And he focuses mainly on implementation of Adobe Analytics and other experience cloud and experience platform solutions, as well as delivering training and holding both the BP and developer analytics certifications. He’s a big fan of board games and of gaming. So Danny, please kick off our Rockstar session.

Hi everyone, my name is Danny Cessmero-Alvarez and I’m a Senior Associate at Merkle UK. Today, I’m gonna be talking to you about the three R’s of Adobe Workspace projects.

Essentially, we’re gonna be covering some tips and tricks that will hopefully help you whenever you need to build any Adobe Workspace projects, either for yourself or any of your colleagues at work.

Cool. So you’re probably wondering, what are the three R’s? And the first one is gonna be reuse. Probably sounds familiar from whenever you were in high school and we were covering all these recycling themes and topics.

In today’s session, reuse is gonna be all around. Reusing free form tables and reusing any data sources that you’re gonna be using on any of the dashboards or projects that you’re gonna be building in order to minimize the amount of them that you need to build.

Then when it comes to recycling, that’s the second R, we’re gonna be looking at avoiding what we would call as reinventing the wheel. So if you have any visualizations or any panels that you’ve created previously on any workplaces, projects, we’re gonna be recycling those and looking at how can you reuse those as well. And lastly, it’s gonna be reduce. Reduce is pretty much just about reducing the time it’s gonna be taking you, whenever you need to carry out any of these activities.

On top of it, reducing the load time that it’s gonna take for any users or any stakeholders in your company whenever they access Adobe Analytics projects.

So without further ado, we’re gonna start with reuse.

Just to make it a little bit easier for you, I thought about building some kind of story because we’ve all been there and this kind of helps whenever we need to explain new things. So for today, the use case we’re gonna be looking at is we work for our organization. There’s several brands under our organization and one of them has reached out to us. They want to have a snapshot of the traffic and main conversion metrics that they have in the website related to this specific brand. They already have an existing project in Adobe workspace. They just want to have this top level snapshot kind of information view at the very top of it.

It’s something that’s gonna be looking something similar to this.

So if I just swap quickly onto Adobe Analytics, as you can see, I already have this kind of report in place. These are all the different visualizations that they already have existing and going on in their current project. This is what they’re usually working with. All they wanted to do or what we’ve been briefed to do is pretty much adding this snapshot data table, but to make it more user-friendly, we’re not gonna just have this table and we’re gonna have some summary numbers. So for any of you that are familiar with Adobe Analytics, there’s several ways where you can add these summary numbers.

This is one in our case, it’s probably gonna be right clicking. So if I right click on any of these top number, which is the total number for whatever date range we’ve selected, we can click on this visualize.

If we scroll all the way to the bottom, we’re gonna find this summary number. When I add this summary number, at first it’s gonna be quite big. We can always resize it, make it easier for us to handle and for everyone to see this. And once we’ve got it with the size we want, pretty something like this, yeah, something like that should be fine. We can just rename it and call it Visits because that’s the metric we’ve selected. Now, it’s just a matter of repeating this exercise a couple of times, but as you can probably see right now, as soon as I right clicked on the page views one, it swapped the number and now in here you can see page views instead.

When I add this second summary number, you will see it’s exactly the same number. So you’re probably wondering, okay, this is kind of good that we can recycle the table, but how do you make it so these numbers don’t change whenever I add a new one? In addition to it, just let me finish resizing this one, cool. In addition to this, if I click now on the unique visitors, you will see that both of them are updating. So I’m gonna rename this one to unique visitors because it’s the one I already have selected right now, so why not keep it like this? And the easiest way or the best way you can handle this is by clicking on this orange icon, you will see that this says this small hint saying manage data source. If you click on this option to log the selection, what you’re making is now whatever you have selected when you tick that box, it’s gonna be displayed on this box, no matter what else you click on. So if I click now on visits, you will see that this one updated, but the unique visit will stay the same. So this is essentially the way we can reuse these different tables or data sources to display different metrics without having to add a single table for every one of them. So now I’m gonna go ahead, I’m gonna log the selection for the visits one as well, and the last one I’m missing is just the page views one. So if I right click on it one last time, click on visualize and click on summary number, I just need to resize it again. So I’m happy and I can fit all of them in the same row.

And it is a matter of reordering now.

So just doing like this, the last step we would have to do is log the selection again. But as you can see, something like this, you’re allowed to have several visualizations linked to the same data source, which would be this freeform table.

You’re not just restricted to these summary numbers. If you wanted to have a trended line of the visits over time, that’s something you could add as well. We can actually do it right now if you wanted to. We right click on the metric we want to, we go to visualize again, and we just find the line one to have that trended line of visits over time. Just remember to rename everything every time you do something, because that’s usually best practices.

Let’s say, visits over time.

Just like this, if we want to add some more, or if we want to make sure that no matter what the users click on, this number doesn’t change, we just lock it as well.

One last thing that’s probably gonna be quite helpful is once you’re happy with this snapshot of the data, the table is kind of irrelevant anymore. It’s not really relevant for the users, because they already have all the information that they need. And you can always just untick this box, and our big messy table that we were using to do all these data mappings to different summary numbers, it’s gonna be gone from the user’s view. This doesn’t mean that the table is gone from the whole dashboard, it just means that it’s gone from the user’s view. And if you need to do any changes, you can always bring it back by doing the same. You can click on any of these orange lock icons, and you just need to tick this box again. As soon as I tick it, if I scroll down, you will see that this snapshot data table is back on my dashboard.

And this would be the first one of our R’s reuse. So I’m just gonna go back to the slides, and we’re gonna move on to the next one, which is recycle. So just similar as before, I’ve prepared kind of a brief or use case from the company we’re working for. And in this case, it’s just a matter that brand A showed the workspace project that we created for them on a QBR or a department-wide meeting or organization-wide meeting, something like that. And brand B has caught the wind of what we’ve been doing with brand A. They have a similar project to what brand A used to have.

And in this case, they’re just asking us to do something similar for them. They want the snapshot as well, and they did notice that brand A has final visualization at the bottom of their project. They have the exact same tracking, the exact same website structure, so they wanted to have this funnel as well.

So just same as before, I’m gonna go into Adobe Analytics, and I’m gonna show you how to save some time whenever it comes to copying or recycling things that we’ve done on previous projects on Tenure ones. So as you remember, this was the project we were looking at before when we were working on the snapshot. In this case, it’s gonna scroll all the way down where we have this funnel visualization that brand B is interested in.

And this is one of the things, if you remember from the brief, that brand B wanted. So the easiest way or fastest way for you to replicate this, even if there’s some differences, you can always change them afterwards, but the general structure of the checkout funnel is gonna be the same. We can just right-click on this visualization, and all we need to do is click on this Copy Visualization option. When you do this, what many people don’t know is that you can paste them across different projects. So instead of just copying it on a different panel within this report that’s specific to brand A, what we’re gonna do is we’re gonna go to this brand B report or project that we’ve created in the past, and we can add this visualization that we just copied anywhere really. In this case, I’m just gonna be adding them in the bottom. And as you can see, when I right-click on this, we’ve got a new option that says Insert Copy Visualization. When I select this, right below the visualization where you right-click, we’re gonna get this checkout funnel that we just copied from the previous project, if that makes sense. This is not just applicable to visualizations. This is also applicable to panels. So if I go back to brand A report, or any report that we have a panel we want to replicate, in this case, this is the site-wide snapshot. If I just expand it, just to make sure this is the one that we want, I can just right-click on the panel, right when the cursor changes to the double-cross. And if I copy the panel and go back to brand B, I just need to scroll all the way up. And if I right-click on this panel in here, you always need to right-click on the panel heading. If you click on the Insert Copy panel, you will see that now we have this site-wide snapshot included in brand B’s project.

There may be a case for changing a couple of the metrics or changing the report suite, but as you can see, this is gonna be way faster than redoing the whole process of building the panel, building the table, and building the different summary numbers. You can do it in one go by a couple of right-clicks with your mouse.

So hope this one was helpful as well. I’m just gonna jump back again to the slides, and we’re gonna be looking at the last R of today, which is reduce. In this case, reduce is not gonna be that focused on Adobe Analytics, because as you can probably think, there’s two aspects to it. The first one is we’ve been reducing the time it takes for us to building projects or dashboards for anyone in our organization.

So that’s probably one of the first points I’m gonna be surfacing. And there’s a couple more functionalities that you can actually use to save up some time whenever you’re working with some projects that have a lot of panels.

These are the apply the date range to all panels. As you can see on this screenshot I have in here, there’s this button that you can always click on. It says apply to all panels. Whatever date range you have selected, it’s gonna be applied to every single panel that you have on your project, which is quite handy whenever you need to do QVR reports or things like that. And the second functionality is the apply reports to all panels. So similarly to right clicking, clicking on copy panel or insert copied panel, when you right click on the heading of any of the panels, as you can see on the screenshot in here, there’s an option that says apply reports to all panels. What this does is it’s gonna update every single panel that you have on your project to have the same reports that you have selected on whatever panels you’ve right clicked on.

The second aspect to the reduce R is reducing the time it takes to load any projects. So whenever a user opens a project in Adobe workspace, if there’s too many panels or too many visualizations within those panels, even though Adobe Analytics is usually very fast to load, it can take a little bit of time because at the end of the day, it’s gonna depend on the user’s internet connection.

So hiding the data sources like we did on the first R is one of the first good steps that we can take towards reducing the load time of any of the projects that we’ve created for any of the users in our organization. As you can see, I took a screenshot in there as well, just to remind you where this feature is. It’s right here, just remember to un-tick this box. There’s another option that you can do.

This is whenever you have projects with lots of panels and these panels have lots of visualizations within them, it becomes a bit heavy on the laptop to load all the resources, especially if you have segments applied on them, if you have different date ranges, if you have lots of breakdowns on them as well, it’s gonna put a little more stress on your machine. So if you collapse all the panels, once you’re done with any changes, whenever you come back to do any changes or someone comes in to look at specific section of the project, they just need to expand that specific panel and that’s gonna make sure that Adobe only loads that specific panel.

That way you can save couple seconds depending on the project that you have. Some cases you’re gonna save up a couple of minutes even. That’s the last trick or the last tip that I came with for you today.

This is me for today. Thank you for coming and hope you have a nice day.

Awesome, great stuff, Danny. Thank you so much for sharing as our first analytics rockstar of the day. Love that concept, reduce, reuse, recycle. It’s like we’re thinking green and I’m a big fan. Next up, we have Thomas Buckley joining us who is a manager of data warehouse and business intelligence over at Miles & Moore. He’s a career marketing analytics professional with strong international experience in both digital and media analytics. At Miles & Moore, Thomas is part of the Lufthansa group and is responsible for the strategic and operational management of analyzing and optimizing the entire digital ecosystem of Europe’s largest frequent flyer and mileage-based awards program. Sounds like some great data. While there, he works cross-functionally to build and maintain an extensive implementation of Adobe Analytics across various digital websites, sales channels, and of course, the Miles & Moore app in order to drive performance-based decision-making. Thomas resides in Berlin and can often be found exploring the city by bicycle. I love it. Welcome, Thomas.

Hello and welcome. Today, I’d like to talk to you about making your traffic and conversion variables smart.

My name is Thomas Edward Buckley. I’m manager of data warehouse and business intelligence at Miles & Moore, part of Lufthansa group. Miles & Moore is Europe’s largest frequent flyer and awards-based mileage program for Lufthansa group airlines, which include Lufthansa, Austrian, Swiss, Brussels, Eurowings, and other European carriers. I live in Germany’s capital city, Berlin, and enjoy exploring the city by bicycle. We’ll look right now at the case for smart traffic and conversion variables. Then I’ll give you some brief definitions in case you’re not aware of what variables are. Talk about how to get smart and how to make your traffic and conversion variables smart. Look at some guidelines for using this approach, then some sample use cases before wrapping up with the summary of the five steps for setup.

When we look at the case for smart traffic and conversion variables, I’d like to start with an example. In this case, etsy.com. Imagine you are the digital analyst for etsy.com and you want to analyze and set up the measurement structure for the login component. You can see users can log in either with their Google, Facebook, or native Etsy credentials. So putting ourselves in their shoes, what are the things you would exactly like to measure? First, you might want to know whether the user are actually logged in or not. They’re logged in state. You might also think about which credentials are they using to log in, Google, Facebook, or Etsy.

In a further step within the login process, we’ll be looking maybe at their login permanency. How they set a checkbox in order to actually remain logged in after their session has ended. And finally, especially in a mobile use case, we might like to look at the login biometrics. Are they using Android fingerprint authentication or perhaps iOS face ID to log in? So this basic measurement of an important component would utilize four variables in order to do a full blown analysis. That’s a lot of variables. And even though Adobe is very generous with the number of variables they provide, they are limited to a certain number depending on what your contract states. You’re either Adobe is either generous, very generous or extremely generous.

Additionally, if you’re using one report suite to measure multiple properties, it’s even more imperative to think about using what I’m calling smart variables. That’s because the number of traffic and conversion variables is limited to an actual individual report suite. In our case at Miles & More, we have three large properties and we use one report suite to measure them. We have milesandmore.com where members can enroll in the Miles & More program and also find out about offers to earn or spend miles. For our frequent flyers, we have a mobile app, the Miles & More app, where users can access their digital service card in order to earn miles at a POS or while they’re in an airport context. And finally, we have the Lufthansa World Shop where members can redeem their award miles for things like noise canceling headsets or luggage or other travel related accessories. So these are three very distinct websites or properties that have different types of measurement structures but also have some overlap. And for those overlap, we need to be smart about how we’re using our variables to make sure we do have enough variables available to measure everything. And finally, smart variables also have the benefit that they give you flexibility. You have the ability to look at your data in different ways, either grouped together, individually, or combined via breakdowns.

So if you’re not familiar with what are traffic and conversion variables, let me give you a brief explanation. Of course, there’s plenty of good documentation on the Adobe Analytics website. But dimensions in a standard table are the words. So in this case, we’re looking at a dimension called page, where we see values like the homepage or a category page. And metrics, on the other hand, are the numbers, the values to a specific dimension.

Traffic variables are called props, and traffic variables are one of two types of dimensions. They’re used to report on popularity of various dimensions on your site. And conversion variables are called EVARS, and they’re used to determine which dimensions of your site contribute to success events.

On the other hand, the metrics, the numbers, there’s things called the standard metrics, like we’re seeing here, visits, but there’s also success events that you can custom integrate. They’re used to measure the number of times the visitor reaches a goal.

And in a later step in this process, we’ll also need to know a little bit about classifications. Classifications are a translator functionality within Adobe Analytics that allow you to categorize data in order to display it in different ways in reports. Classifications can translate, for this example, product IDs, which are a combination of letters and numbers, into friendly names like mountain bike or bicycle helmet. It’s a baked-in functionality that you can use and set up.

So now let’s try and get smart. Let’s actually make our conversion and traffic variables smart. So going back to our Etsy example, we were looking at four different variables that we needed to measure log in. If we get smart, we can reduce this down to one variable called login type. And what we will do is we’ll simply concatenate these different attributes that we were looking at. We’ll start by using, as an example value, the login state. Are they anonymous or are they logged in? Separated by a pipe with the login credential, are they using Google, Facebook, or Etsy credentials? Separated again by a pipe to look at the permanency. Are they remaining logged in? And then a final pipe before looking at the biometrics, are they using things like Android fingerprint authentication or face ID from iOS? So in that case, we’ll only need one smart variable to measure the login. How will we go about doing this? The first step is, of course, in the administrative interface in Adobe Analytics to create a dimension called login type.

We’ll then need to document how we’d like to fill that login type. So what is the value definition? It’s a concatenation that’s pipe separated using login state, credential, permanency, and biometrics as the four distinct attributes of login type. So an example value that then will be populated into that dimension is something like logged in pipe, Etsy credential, pipe stay logged in, pipe iOS face ID.

In the admin console, then under classifications, you’ll create a simple classification structure that says the main dimension is login type. And these four attributes are the classified values of login type in order to break out those four attributes. And finally, then you’ll create a classification rule, either using a regular expression, which counts the number of pipes and orders then, for example, the attribute after the first pipe, Etsy credential into the classification called login credential. Or you can use some simple contained statements and say if a value contains Etsy credential, then that should be filled into the login credential. You’ll of course find more documentation about this on the Adobe website. Some guidelines for using smart variables.

First, it’s good for known or fixed numbers of attributes. As in our example, with login, we had four fixed attributes, and they’re all known, the values that were going to be populated into this dimension was well known, and we didn’t have to think about that.

It’s not good for lists of attributes where the values might not be known and might not be fixed. There’s a baked in functionality in Adobe analytics called list variables, which you’ll find in the admin interface. And finally, it’s important to keep aware that there is a byte count on traffic and conversion variables. So you’ll need to be aware that props have a maximum of 100 bytes, and EVAR is a maximum of 255 bytes. So you can only have a limited number of attributes. Looking at some sample use cases, we just talked about login. This is a wonderful tool to use for tracking codes when measuring your advertising campaigns, as well as for on-site advertising. In a miles and more context, our advertisements have a headline and a subline and always the context of a partner, like a hotel or a car rental company. So we track them all together in one large dimension and then break them out, the headline, the subline, and the partner. For product attributes, instead of just tracking an ID, you can measure things like the product name, the product color, or the product size. And when you’re looking at form field analysis, something we do extensively on milesandmore.com is where users have the ability to search for a flight, a car or a hotel, and redeem their miles on our website at the best price. So here I have an example on the right where you can see we can collect the values collected together and see what are the most popular combinations. In the lower left, we can see what was the individual use of the specific adult count or the number of adults that we’re looking for a booking. And in the final version, we have an example of a breakdown where you can see how Berlin, Germany, was the most popular hotel destination for single travelers.

So you have the option again of looking at the data grouped, individual, or broken down. So let’s look at the five steps for setup in summary.

First, review your implementation strategy. Is there room for improvement? Are you approaching the maximum use of your variables? Or are you doing a new implementation and you have some time to plan out your strategy? Think about how you can implement smart dimensions and smart variables. Then, of course, document your plan. We use Confluence at Miles & More in order to document our implementation, but you can use the tool of your choice.

Next, you’ll need to go into the admin interface and set up smart traffic and conversion variables. So build those EVARs, build those S-PROPS, and get that implemented on your website by talking with your development team or using Adobe Launch in order to implement things on your own.

Fourth, in the admin interface, build simple classification rules and use the classification rule builder.

And then at the end, you can begin collecting data and analyzing both the combinations or the individual attributes. Of course, there’s more. If you’ve enjoyed this presentation and this tip, then vote for Thomas Edward Buckley as your analytics rockstar.

Thanks for watching and have a great day. All right. Thank you so much, Thomas. I love any tip that gets classification rule builder involved. So if you haven’t used the rule builder before, go check it out. Some great stuff in there. Next up, for our final analytics rockstar, we have Darren Robertson. Darren is a data specialist, and his title is as a lead analyst over at Save the Children in the UK. He works primarily with the charity and education sectors, as well as being sought after for consulting. Darren has been acknowledged as a top 100 data leader in the UK two times in a row and is focused on helping businesses make a difference through the use of data. He understands how to merge data from both digital analytics and other business data to make truly impactful data influence decisions. Darren currently leads Adobe Analytics launch and People Core Services over at Save the Children UK. So take it away, Darren.

Hi, I’m Darren, and today I’m going to talk to you about how to better build calculated metrics and take that scare factor away from it. So just a little bit about me. My name is Darren Robertson. I’m the lead digital analyst here at Save the Children. And I’m also the product owner of Adobe Launch, Analytics and People Core Services here at Save the Children. I’ve been recognised as a top 100 UK data leader by DataIQ, two years running. And it’s my job to push Save the Children forward with data maturity and digital analytics strategy. Currently, we’re focusing on using our Adobe Stack to leverage CRM data to use on our website. And that’s going to drive amazing impactful user journeys. And we’re running an ambitious self-service analysis system that incorporates data from our CRM system and predominantly Adobe digital data across our organisation. I come to Save the Children and to yourselves with a wealth of analytics, strategic thinking, and have helped businesses in many sectors over the years to develop and derive better insight through the data that they have access to. I’m always happy to connect with fellow practitioners. And you can either use the QR code to look me up on LinkedIn or just grab hold of me using my LinkedIn URL there. So I’m going to talk to you about calculated metrics. It’s a passion of mine. It’s one of the best things that I found in Adobe Analytics. And the calculated metrics are really super powerful. They’re absolutely really versatile in their use. They can be really simple or they can be really complex. And they can sometimes look a little bit scary, just as like the metric that you can see just below in this presentation. I mean, that would scare anybody, I think, if you saw that for the first time. But these things aren’t actually that complicated to do. So I’m just going to tell you quickly about the advantages of these calculated metrics. So, you know, it’s about unified metrics. You can create unified metrics across reports, any part of that analytics platform, the report builder, the anomaly detection part of the platform, and freeform analysis workspaces. It’s really good because you can avoid implementation changes. You can create those metrics derived at report runtime without actually having to make major changes to the way that you’ve implemented your analytics. And you can view these historically as well, which is like fantastic, because essentially they’re just based on segments, right? So the other great thing about them is it’s super shareable. The ability to share those metrics across the report suites, across different business functions is really, really crucial. And being able to use that report suite logic of filtering on these metrics that you’re building is really, really important. And of course, you’ve got those statistical functions, and that really means that this is a really super powerful tool, but can often seem quite difficult to use. So, being me, I couldn’t settle with just one top tip. So you actually got two top tips from me today. So tip one is got to build it bit by bit. And this is really important. You need to build those components that make up the metric that you’re looking to build. I always start by laying out all the metrics and dimensions I need to build that calculated metric so that I can get a better picture of what it is that I’m trying to build. I always use a freeform table to lay it out. Using this freeform table means that you can, for example, creating the mean of a metric that you’ve already pulled across means that you can have that, you can use the right click functionality on the metric that you want to work from to duplicate that in the new metric that you want. And this is really, really important because as you start to build those across in a big freeform table, you’ll have access to all the different data points that you’re going to use to create that complex, potentially complex calculated metric.

And the other part of that is you really need to remember to step back and look at the container positions, look at the different items that you’ve got and how you’re going to put those into that container. Because I think that’s really super important. If you just try and build it inside of a container, it’s going to be really difficult because you’re going to be overwhelmed with all these different containers within containers. And it’s a much easier way to start building calculated metrics just by coming back to building it out first in this freeform table. So, you know, at Save the Children, we wanted to build this really amazing report. It needed a whole bunch of calculated metrics to make it function. This was actually around looking at a corridor of expectation of values that we were expecting to see from our unique visitors. We tend to have quite spiky traffic. We wanted to be able to smooth that out. We wanted to be able to understand, you know, how many days were above our expected output, how many days were below, what were our estimated unique visitors smoothed out over each day. And in order to do that required quite a bit of work. So, you know, we had to build this massive table. This is the table that I was talking about beforehand. So you build this freeform table. You look to the far left, what we’ve got is we’ve got unique visitors and you can see at each of these points, particularly these ones with the blue shading on, these are all the calculated metrics that we’ve built in order to be able to perform this piece of analysis and to build out this report. We’ve built it stage by stage, taking each bit, bit by bit, and building upon that. And I think that’s really important. It’s about building upon the piece of work that you’ve done before. As you can see, you know, those containers can become really overwhelming. This is just one container from one part of the metrics that we needed. That’s actually the far right of this presentation screen. And if you try to start building that calculator metric straight from in there, it could become quite overwhelming, especially if you’re quite new to using the Adobe Analytics Suite. And at the end of the day, this is what we wanted to build. And it actually was just really easy by being able to do it in this way, building on the piece that you’ve done before, just building it bit by bit. And so the second top tip is actually about keeping tidy, keeping organized with calculated metrics and being able to understand and share with colleagues what it is that you’re doing. And it’s called the calculated metrics workspace. So we all know in our day jobs that documentation is key. Making sure that you document the way that you build metrics or anything where you’re building anything to do with data is really super crucial, right? Let’s face it, you’re not going to remember why you did what you did 18 months ago. I know I certainly won’t. So in order to be able to get around that, we build and create a calculated metrics workspace. So it’s just a normal workspace. Obviously, you have those sections that you have within a workspace, and we dedicate each section to being a new calculated metric. That calculated, that’s the overarching calculated metric that you’re looking to build, not necessarily all the components within it. So each one of those allows us to be able to actually put in what that calculated metric is, have a visual of what it’s representing, and actually be able to detail all the steps that we have taken to actually build that calculated metric. That means no matter what you do in 18 months time, 24 months time, you’ll know exactly how you built that. So also a really great way that your peers can be able to come in and be able to evaluate and peer review any new metrics that you’re building. So having that is a really great way to be able to annotate, keep and document this complex piece of work that you’re spending your great time and energy building. The last bit is tag that calculated metric. I can’t say this enough. When you are building complex calculated metrics that contain lots of different components that are often calculated metrics in their own right, you become overwhelmed with the total amount of calculated metrics that are available to you. So what we tend to do is we take that end calculated metric, the one that we want to share and use in reports with other people, and we actually tag it as calculated metric. This means that it makes it really easy to share and find those top pieces that you’re actually looking to find. And so just as a quick recap on those top tips is, if you think it, you can probably build it. And I think that’s a really important thing with the Adobe Stack. Build it bit by bit using that free form table. Step back and have a look at what it is that you’re doing. Remember that documentation of your process is absolutely key. Set up that calculated metrics workspace. It will absolutely save you tons of time and be super useful to you in the future. And use that tagging functionality to make them easier to find. And of course, we all know this is a vote for these tips. So please vote for this one for me. Thank you very much. All right. Thank you so much, Darren. We really appreciate that last tip. The one takeaway that I really liked was that calculated metrics workspace. We talked about it earlier. There’s great opportunities for governance and documentation and something we should all be thinking about. So you’ve now heard from all one, two, three of our rock stars here today, and it’s our turn to hear from you. So it’s just about time to vote. So think about Danny and Thomas and Darren and the tips that they shared, and think about what was your favorite rock star tip. Was it Danny with the three R’s? Reuse freeform tables as data sources, recycle visualizations and panels and reduce those load times? Or perhaps it was Thomas who made the case for smarter props and EVARs, helping you concatenate data into a single variable to save those variables and then split it out later thanks to classification rule builder. And then finally from Darren, we just heard those two tips for using calculated metrics by building them piece by piece, bit by bit, as well as documenting them into a workspace project. So with all of that, which one is your favorite? So voting is open now. OK, thank you so much for making your voice heard. We have a winner. It was a close race. It always seems to be a close race. So I’m glad that today was no different from the normal. And so the winner of the EMEA Skill Exchange Analytics Rockstar session goes to, I should have brought out my drumsticks yet again, goes to Danny over at Markle UK. Congratulations, Danny. We really appreciate all the great content that you shared. Love that. Reduce, reuse, recycle. I think there’s some songs about that. So definitely check those out. And thank you for being green with your workspace projects.