Adobe Analytics: Putting it All Together

All right, that was great. So now let’s look through how to pull it all together. We’ve looked at how to build a table, create a project in the dashboard, how to use segments and tables. So we’re going to take a look at a few different things. First we’re going to look at how do we collect reporting data. So with analytics, you really need to understand where the data collection comes from before it goes to the report suite or the report. The second thing we’re going to look at is how to create basic visualizations based off the data that you collect today. And the third thing that we want to do, most important, is the data democratization. So being able to share the data is most important so that you can share the results of your hard work, be able to tell a story with your data, be able to show management in the executive leadership team that you’ve driven success through your data optimization and analysis. So let’s go ahead and first take a look at how analytics actually works. What do we do behind the scenes to make the data available to you? The first thing that you want to do within your organization is define your business needs. What are your use cases? What are you trying to achieve with your data collection? So once you’ve defined your data sets, then you want to be able to design your tracking strategy. Being able to create your data governance as to how you want to collect the data, how you want to categorize that data, which team gets what data, really truly defining your strategy so you’re set up for success at the beginning, that you have a clean data collection, you’ve defined your strategy and your business goals. The third component here is you want to deploy your tagging. You want to deploy your data collection so you start collecting that data. That’s probably the most important part is getting your data goals defined and then deploying your tagging so you’re able to start collecting the data and understand are there any issues with the data. You can clean the data. You can really start to analyze the data for your KPIs. So that leads us to the fourth step, and that’s all about analyzing the data collection. It’s probably the most important step because you can collect data all day, every day, but unless you’re actually analyzing it to answer a business question or define your goals and action against, there’s really no purpose. So being able to analyze the data and understanding the opportunities for growth or where you can kind of optimize your products and services is really super important. And finally, the fifth step, as I mentioned, is actioning against that data. So being able to take that data analysis, find and tell a story with that data, and being able to act against it. Say you see a drive in page views and traffic to your site and it’s contributing from a specific product. You can go out in action and start advertising that product because you see it’s really popular. You want to be able to use that data and that analysis to action against and use it for recommendations or personalization or even targeting. So those are kind of the five steps or cycle in being able to leverage analytics data. So now let’s look at the value framework. The first step here is data integrity. You need to have really strong confidence in your data, and that’s where the whole data definition and data governance step comes into play. You want to be able to effectively gain insights and really truly be able to capture your KPIs, key performance indicators from the start. But in order to do that, you need to have strong data integrity. We’ll build on that to be able to leverage reporting. Reports and dashboards is where you’re able to layer onto your data collection to be able to analyze that data. Reporting is super important. Some people just like to export the data and look at it in Excel, but reporting really just makes it easier to visualize and understand the data and to be able to tell stories with the data, which is really important to management. And then the third step here is driving the insights from that data. You want to be able to comb out those golden nuggets to be able to understand what’s really a success story that you can share with your manager. Or you may want to use it to be able to understand where there’s issues or where there might need to be further optimization in a certain specific area. So use your analysis, use your insights to be able to really drive your strategy and increase and improve performance on your site and your app. And the final step here is modeling. So you hear about it all the time. You have AI and machine learning that allows you to be able to kind of find new, really exciting discoveries with machine learning that you wouldn’t typically find just combing through the data manually yourself. So start to use those machine learning insights like anomaly detection and our attribution models to be able to surface new successes and KPI achievements. This is how you’ll really be able to leverage the data more effectively to be able to analyze using machine learning capabilities through things like Adobe Sensei. And as you move through these four steps, obviously you’re going to improve the value of your data and it’s going to continue to improve as you optimize on those four steps. So now let’s talk a little bit about data collection. Data collection is how we’re able to actually capture the data and bring it into our reporting system. So it all starts with a visitor. We talked about visitors at the beginning. You want to be able to track that visitor and all the different traffic interactions and events that happen on your page and app. So when you look at the visitor, they come to your homepage or they come to another section of your website. You want to be able to measure that visitor from that point forward. So as you visit the webpage, Adobe Analytics will actually have a snippet of code on that page where we’ll be able to measure that user. And that specific pixel can be deployed through our Adobe Launch tag management system. So once we load that pixel on the page, we can understand that visitor’s interactions and everything they do within that visit. How many page views they have, how long they spend on your website. All of this information is recorded through that pixel and sent back to Adobe servers for us to be able to collect all of those server calls and be able to report on it with an analysis workspace. Reporting is typically available within minutes to hours within the same day. We have really great collection mechanisms to be able to report and understand data consumption on your sites and apps so that you can view it in analysis workspace in your projects and dashboards, either on the same day or the next day.

So one of the most fundamental things about analysis workspace and data collection within Adobe is report suites. So report suites is the most fundamental level of Adobe Analytics. It’s really how you categorize your data collection. Say you want to have a report suite for different mobile devices, your smartphone versus your desktop and your tablet, or you want to have a report suite by product to be able to differentiate successes across your different products or brands. So there’s different ways that you can leverage report suites as you’re tagging and deploying your Adobe Analytics tags. The other type of report suite is your global report suite. A global report suite can be a combination of multiple report suites into one. It’s a great way to see an overall outlook of your full business, to be able to see every action that happens across all of your brands and all of your devices. So I definitely encourage you to use the global report suite. It’s really important for your data collection and analysis. And then there’s a virtual report suite. So the virtual report suite is the ability to be able to segment a set of data within your report suite so that you’re not using secondary server calls. So this is a great opportunity for you to reduce costs and reduce the number of secondary server calls for you to segment your report suites into different kind of custom data sets that may be more important to one group versus another. So this is all available today. And especially if you’re first starting out with analysis workspace and data collection, you’ll want to be able to break up your support suites into a development report suite and a production report suite so that you can differentiate and understand when you’re testing and QA-ing before launch, you can use your development report suite. And then when you go live in production, you have your production report suite. So I encourage you to do that as well. Okay, so now that we’ve talked a little bit about how to collect data, how we build that data into a project analysis workspace, now we can look at how we can visualize that data. We want to be able to tell a story with that data. So here are some of the six tips that you can leverage and you can think about when you’re building out your projects in your dashboards and really creating better visualizations. The first one here is being able to identify the most important data. You can drag in any metric dimension that you want, but you want to make sure it’s important to your business and it’s a KPI that a lot of people are interested in. So make sure that you’re looking at your most important data and defining your KPIs at the start so that you can measure against those. Second, you want to choose the best visualizations possible. And we’ll go into a little bit of detail about the different segments of visualizations, but each metric data type or question that you’re answering requires a different type of visualization. So make sure you’re really thinking through which visualization would tell the best story based off of the business question I’m trying to answer and the KPI that I’m using. Third, you want to align your visual with your story. So if you want to kind of highlight your performance over this month and how you’ve really improved your product purchases from the start to the end, then you would use something like a trend or a line chart. So really visualizing it to tell a story that, hey, look at the significant growth that we’ve seen this month using this chart. Fourth thing here is remove any unnecessary noise. Now with Analysis Workspace, it’s awesome because it’s so flexible and you can drag and drop all sorts of different types of visualizations, but make it simplified, make it easy to read and understand. You don’t want it to be too busy and noisy where somebody who just logs in for the first time to look at the dashboard is like, whoa, there’s too much information. You want to make sure it’s clean and easily readable. The fifth one here is highlight any main takeaway. So if you’re going to share a dashboard or a project with someone, or if you’re going to present it, make sure that you’re really analyzing the data to understand the key takeaway, what the KPI is that you really want to promote and tell a story about. So look at the dashboard, let them know what the key takeaway was. It’ll make it a lot easier for them as they’re going through the project to understand the data because you’ve summarized the key takeaway for them. And lastly, make it easy to consume. As I said before, it really is important, especially because if you have different audiences, different teams, if you have an executive looking at the report, make sure it’s easily consumable and not too distracting with a bunch of different charts and graphs. Okay, so there’s so many different types of visualizations to choose. One of the most basic ones is comparisons. So obviously you have your bar charts. We have horizontal and vertical bar charts. You can do stacked bar charts. We also have tables and heat maps. I love to use conditional formatting in our tables because it makes it so much easier to see when there’s a high performer and a bad performer. With that conditional formatting, you can really see across a bunch of different dimensions, which are doing really well versus not as well. So that’s a great way to do comparisons within Analysis Workspace. The next one here is trending. So trending is super important when you want to understand progress over time. A lot of KPIs are associated with trending performance to really understand peak performance or lows or dips. So using the line chart here is a really great way to be able to understand trending. And one of the more advanced features that you can use with the line chart is anomaly detection, which is our machine learning capability that finds significant events that it will call out for you to look into. We also have the vertical bar charts and the stacked area chart for you to use for trending. Next, you might want to look at parts to a whole. One of the most common charts that I use for this specific action is the pie chart or the donut chart. These are the easiest way to see parts of a whole, especially if you have a small set of dimensions that you’re looking at. You might not want to have like 15 plus categories or dimensions that you look at in a pie chart. It’s better for when there’s just a handful of dimensions that you want to compare against the whole. Next you have relationship charts. So we have some really great advanced charts here like scatter plots, Venn diagrams, which you can also see in our segment compare panel. And you have bubble charts. These are all really great relationship charts that are available with an analysis workspace. And then finally you have distribution charts. So I really love this one too. This is the histogram and this is where you’re able to bucket actions or interactions. Say you want to see the number of visitors that have visited within the past one to three times versus three to five times versus five to 10 times. You might want to target those users that are high visit users in the five to 10 bucket to be able to retain and keep those users and give them new relevant content to look at. Okay, so with visualizations, there’s a few different ways that you can actually build a visualization with an analysis workspace. Like I mentioned at the beginning, when you go to the left navigation menu, you’ll be able to see an icon for visualizations. And when you click on that, it’ll give you a full assortment, a full menu to be able to look at and choose which visualization you want. And then it’s as easy as dragging it and dropping it into the project to be able to look at. So here you’ll see an example. It’s just the basic freeform table. We have a little heat map coloring in there, conditional formatting, and then you can build off of that and create this unique dashboard. So here’s some summary numbers, you know, a bar chart, a donut. So being able to take the data from the table and creating a rich visualization dashboard out of it is what our goal is. A couple of tips that you can leverage with an analysis workspace to also get visualizations added. You can use right click anytime. So if you’re on a cell or a row or a column and you want to visualize it, you simply right click and scroll down to the visualization selection, and then you can pick which chart or graph you want to visualize. And then magically above the table, it’ll insert that chart or graph based off of the cells that you’ve selected. So if you don’t want to go to the left navigation menu, you can right click. You can also go to the row and at the row on the right, there’s another little visualization icon. If you click on that, you have to hover over it, but when you click on that, it’ll give you a prompt and give you a guess as to what type of visualization might be effective for the data that you have selected. So that’s another way that you can quickly and effectively create a visualization without having to go to the left navigation. Okay, so now let’s start to build out some really basic visualizations to add to our dashboard. So we have a table. What you’ll want to do, probably the most fundamental chart within your analysis workspace project is a summary number. A summary number just allows you to bring forth your KPI so you can see at first glance, as soon as you log into your project, how am I doing? So if you want to bring in your summary numbers, you simply go to the table and select the cell that you want to create that summary number for. So if you want to do it for the total for everything within that column, you don’t have to select everything. It’s just going to automatically take that summary number from the top of the column and illustrate the total in your summary number. Otherwise, if you want to select a specific cell within your project to show that summary number, you can do that as well. So it’ll specifically take the data point from that cell that you have selected when you create the summary number visualization. There’s a couple of other instances here, just different variances when you start to play around in analysis workspace and you’re creating your project. As you click on different cells, columns, rows, this just gives you some instructions as to which it will select based off of what you have your mouse selected on when you’re building out your visualization. So most likely you’re going to want to take the total and then with that, you don’t have to select anything. You just have to make sure the column for the dimension that you want to measure is selected. There’s different settings. One of the most important things and one of the things I had to learn pretty quickly when I first started building out projects is you want to lock your selection. If you don’t lock your selection, then anytime you click on a different cell, it’s going to adjust and change that summary number. So if you want to keep it in place and keep it held, you would select the lock selection and that way it’ll not change the summary number as you’re clicking around to different cells within your project. So that’s super important. A couple of other tips that I just wanted to highlight here is if you have a really long summary number, say it’s in the millions or billions, you can actually abbreviate it so it’s not as big and it just kind of, it doesn’t take up as much room. So remember earlier we were talking about remove all the noise, abbreviating it just makes it a lot cleaner to be able to look at. You can also remove the legend so that it’s not showing either and that makes it so the display is a lot nicer.

Another KPI that you’ll want to look at and one thing that you’ll want to highlight within your dashboard is summary change. The great thing about summary change is it allows you to take any two cells or any two totals and be able to see the percent difference between those two. So what I did here is I have a couple of different columns. I’m looking at visitors again but I’m looking at visitors for this week compared to a column for visitors from last week. So I want to take those two cell totals and do a summary change to see the percent of visitors that came this week versus last week. So I can see it went down by 0.3%. This is an excellent way to quickly create a calculated metric that you can highlight and see as soon as you open your dashboard of your project every time. And don’t forget to lock it.

So now we’re going to look at this video and it’s going to show you how to name the project that you’re creating. We’re going to go and we’re going to look at the specific visits metric in the table. We’re also going to search for a dimension to pull into our freeform table. Now after this we’re going to bring in the summary number. So here you can see that it’s brought in the total from the cell column. You can also click around to other cells to adjust this to change your summary number. You’re going to want to name it here and you’re also going to want to make sure that you lock it after you’ve selected the cell that you want to appear in your dashboard. So here I’ve locked the selection and now you can see when I click around it doesn’t change anything within the summary number. Now I’ll take in the summary change calculation. At first it doesn’t show anything because I don’t have any cells collected. When I select more than one it starts to adjust the calculation based off of the two cells that I highlight. So once I have the calculation that I want I can lock the selection and be all set.

The other visualization that I wanted to talk about today is another common illustration that you have within dashboards. A lot of companies want to see their trending performance over time for different KPIs or metrics. So the visualization line chart is a common one that we use and you can use any type of date range that you want. So if you have your date range selected in your project and it’s selected to this month probably you’re going to want to look at it by day. You might want to look at it by week. You can even get down to the most granular level and look at it by minute. So when you go into analysis workspace and you create this line visualization you can look at it at any date range that you want. So you want to look at it for this current year and then break it down by quarter in your trend chart. We have a lot of different options for you to be able to analyze your data. You can go down to the most granular or high level that you need according to your business question that you want to ask. So this is another great visualization. Definitely encourage you to use this especially because you can take advantage of some of our machine learning capabilities with anomaly detection within the line chart. So here’s an example here. We’re going to look through how to create a line chart in analysis workspace. So I’m going to go to the visualization icon and I’m going to select and drag and drop line. Here you’ll see all of the online revenue for that specific month. And then I can click around to various cells to see it by the week within that month. So again there’s a lot of different flexibility as far as which date range that you want to select. You can get down to the most granular level. So for instance here I’m going to look at it by hour. So there’s a lot of different options and ways that you can look at the line trending data and analysis workspace. It just depends on how granular you want to get within your project. OK so let’s look at this next visualization. This is the map visualization. This is a pretty common visualization. It just allows you to kind of do some heat mapping and look at delivery by metric across different areas. You can go all the way up to the country or the continent level. You can also go down to the city level or the local DMA level. So this allows you to look at your location specific traffic by any metric that you want. You can also look at it by calculated metric if you’ve created a calculated metric. But there’s a lot of great options again to customize your map view and you can zoom in or zoom out as much as you need to. It’s on the fly so you can adjust it as you’re looking at the dashboard. And finally one of the most popular visualizations is the flow visualization. And when you really want to understand your customers journey or their experience this is a helpful visualization to understand what’s the most popular way that they navigate throughout my site. You may want to start at the entry level which is your home page. What page are they most likely to navigate to next? What happens after that? Which pages are most popular? How can I adjust the navigation so that I get them on the path that’s most going to keep them engaged the longest and increase their time spent? So this is a great visualization to understand how they’re flowing through your traffic or your web page or your app and be able to highlight successes. Maybe you’re doing a campaign to drive traffic to a specific area of your site so you can highlight that in the flow chart. Okay, so now that we have a project full of all of these great visualizations we’ve added a freeform table, we’ve added a line chart, summary number, summary change, maps. What do we do next? This is where we go into the data democratization step. This quote says an analyst job is not just to pull data, it’s to translate that data into stories that enables you to really drive actions and results for your business. It’s all about data as the foundation and then action against that data. Connect and collect and action. So let’s look at some of the common tips that we have for data and insights. First one, when you’re collecting data and you’re sharing it, you want to understand the requester. Ask them for a single data point and ensure it’s what they really need. They may ask for a bunch of different metrics or KPIs and they’re not necessarily KPIs. So make sure you narrow it down to exactly what they need and be able to answer a specific question with the measurement that they’re looking for. The second tip is understand your audience. Make sure that you’re telling a good story, that you’re sharing enough information that they’re able to look through the dashboard and understand the high value points or the KPIs that were being shared. Making sure that it’s easy for a broader audience to really understand the dashboards. And then you’ll want to speak the language. Don’t make the reader guess or try to understand what you’re trying to achieve with your dashboard. Make sure that it’s clear and outlined. One of the great features in Analysis Workspace is you’re actually able to add text or notes. So if you want to call the specific action alongside your chart, you can do that. Just make sure it’s clear and not too noisy for the end audience. Know the value of your insight. Don’t make the reader panic. Make sure you give accurate representation of the size of the problem. Make sure you’re clearly outlining if they see a sudden really large drop, there may be something contributing to that that is a known issue. So just make sure that you’re kind of illustrating certain points that you want to call out that may worry a reader if they were to just glance at it. And finally, another tip is to question your assumptions. Your job is to question findings and prepare for those hard questions that may come from the audience as they’re looking through your dashboard. So being able to answer and really understand your analysis before you’re presenting or sharing it out. We have a specific share menu with an Analysis Workspace that makes it super easy for you to share across your organization or to different teams. You can share the project. You can also share or curate components of your project so you don’t have to share over the full thing. So if you have multiple brands within your project, you can curate it and select which components you want to share over so you don’t share all brands together. So you can do all of this within the share menu, including just simply sharing a link to the project or sending it through email. A lot of great ways to be able to democratize the data within your organization. Here’s a little bit more details on curating a project. Making sure that you save it before you curate it because you want to make sure you have your original copy saved with all of the components there. You can save a separate copy with the specific components that you want to share out or curate to other users. A couple other great options if you don’t necessarily want to share over the full project is you can download it to CSV or you can even download it to a PDF. When you download it to a PDF, it shows you the full dashboard, all the visualizations and the data tables. Okay, so that leads us through to the end. We have a YouTube channel where you can gain a lot more rich, relevant information about how to build out your analysis workspace projects. A ton of great videos here. I hope that you’re able to learn something new today and be able to go out and create your first project and dashboard and really effectively look at your data and understand your KPIs when you’re going in. I think we’re going to go ahead and go over to our question and answer section of the day’s training. Thank you so much for joining me today and let’s go ahead and take some questions. Thank you once again, Danielle. With that final chapter, I hope you see how all of these elements come together to create a powerful tool for you to learn more and engage on a deeper level with your audience. As with the previous two sessions, to answer some of your questions, I want to bring in Principal Solutions Consultant Greg May from our Sydney office. Greg, it’s all yours. Thank you, Tambi, and hi everybody. So quite a few questions have come in, so let’s just crack on with it. So the first question that I saw was from Ana Luisa. She’s asked, what is the difference between the segments containers when it comes to hits, visits, and unique counts? So I do see also that some answers have been put in the comments, but for everyone’s benefit, I’ll take you through this. So a hit is basically a single event that’s happening on the page. So it could be that they’ve loaded a page, that they’ve downloaded a PDF, that they’ve pressed a button, something like that. That’s an individual hit. A visit is a series of hits right across a session that a user’s when they’re visiting your page, right? And then you might ask, what is a session? Well, a session is a series of hits until they pause and they pause for at least 30 minutes. So that would be a user who’s come on, who’s browsed your property, looked around and then gone away and left it for 30 minutes. Now you can control that setting as well for different use cases if 30 minutes isn’t going to meet your needs. Then of course, a unique visitor is the same person coming back over and over to your site. So that would be a series of sessions and within those sessions, a series of hits. So that’s really the difference there between hits, visits and unique visitors. And you can apply them to any of your segments and to your reporting to make sure that you get the fidelity that you’re after. So onto the next question. This one’s from Rachel. So how to do a clustered stack bar visualization. So unfortunately I can’t just show you in the user interface, but if you do bring a stack bar, you drag it the way that Danielle showed onto your pane, you can then start to add multiple metrics. And then those metrics will automatically stack next to each other in the visualization. The way that you do that, if you’re on windows, you could do control click across the different columns of your metrics. And of course on a Mac, it would be command click. So that’s the way that you can do that. And of course that principle of multi-select or control or command clicking across multiple different metrics works across the board, across any different type of visualization that you have. Now another question that’s come through from Priyanka. Does Adobe provide a demo version to practice these techniques for us? So unfortunately, no, there is no just public demo version that you can log into at adobe.com and play with. However, there are a couple of ways that you can do this. If you are an existing client, you will always have a development report suite as well as your production report suite. Now the development report suite won’t have a lot of data in it, but you can certainly go in there and play around with it. And you can do that to your heart’s content, but that’s where your pre-production testing and things like that are going on. However, there is absolutely no harm either in going in and making your own reports in your live report suite. You can’t actually do any harm. So you can create yourself a new project, build dashboards to your heart’s content, just keep them private to yourself. There’s really no need to be so concerned about that. So if you are a client, try your development report suite, try your live report suite. If you’re not though, there is no public site at this point. So for the next question from Shamista, which is, is it possible to add different parameters on the X-axis and the Y-axis for comparison? So I’m guessing here that we’re talking about a scatter plot graph. So the scatter plot graph actually supports up to three metrics. So you can compare three of them in the single report, but if you do need different parameters on the X and Y beyond that, I would suggest simply making side-by-side reports, and then you can visually compare those as well. So next question, and so I can see a few new questions coming in as well. Just refresh my book question list. So a question from Sachin, how to schedule a report to any email with data in Excel? And he comments, currently it’s PDF, I believe. So that is true. You can definitely schedule reports. Up in the send menu, you’ll find the scheduling options, but PDF is only one of those options. You can actually schedule not in Excel directly, but in CSV or comma separated values, which of course you can open directly in Excel. So you can definitely do that. And then the data will arrive in your inbox to the nominated email address. And then you can simply open that in Excel. One other, just while I’m on the topic of Excel, there is also our report builder plugin for Excel, where you can actually start to interrogate your analytics data directly in Microsoft Excel as well. So if you’re using analytics and you haven’t seen that, I’d definitely recommend going to the help and looking for that. So next one is from Ray. So is there any way to get rid of the duplication of unique visitors over time, but that they are only counted once and not multiple times? So in this case, I guess a unique visitor is represented by a first party cookie, what we call an experience cloud ID, that as long as they don’t clear their cookies or refresh their browser or change to a new browser, I should say, they will appear as the same visitor over and over. But they will be the same visitor throughout. So in terms of duplication, not 100% sure on that comment there. But Ray, if you do want to elaborate on that in the comment, you’re more than welcome to as well. Moving on to Saima, can we combine bar and line graphs in the workspace? So you can’t combine them in a single graph because a line chart is based on time and a bar chart is based on the dimensions that you’re working on. So those two sort of aren’t compatible in a single chart, but absolutely yes, you can combine them in your workspace. So you might have a line chart on the left and a bar chart on the right as an example. So definitely that’s possible. And then that seems to be it for the questions that have come in. Now, there’s a few other questions that are commonly asked and I think worth talking about as well. One is that can we exclude internal traffic from the results? So when you are browsing your sites yourselves and you don’t want those hits to appear as traffic because it’s not genuine real traffic coming from your customers, well, the answer to that is an absolute yes. You can nominate IP addresses that are your internal work addresses and even external ones from partners or agencies and so on so that you can exclude them from the results. Absolutely. Also, you would have heard today about anomaly detection. So anomaly detection is actually a very powerful tool as you’ve seen. But a lot of questions do get asked about how are those anomalies detected? Why and when do we say that particular data point was an anomaly? So anomaly detection basically uses a look back window. So it looks at your ebbs and flows of how your data comes in. You might have more during the day and less at night, more during weekdays, less on weekends, things like that. And what it will actually do is use that to start predicting what to expect in the future. It also has some quite clever algorithms for things like public holidays. So it knows when there’s a public holiday happening and to expect maybe some differences there. However, the look back window will depend on whether you’re breaking this down by hourly, by daily, weekly, and so on. But it’s basically just remember it’s a factor of 15. So if you’re looking at an hourly report, we will go back 15 days. If it’s a daily report, we will go back 15 weeks. So that magic 15 number is what we use there. So basically, it will look at the last sort of multiplier 15 of whatever unit you’re using for your time range. Use that as the window for understanding your typical sort of ebb and flow of whatever dimension that you’re looking at. And then it will predict which of the data points are actually anomalies and aren’t. So, all right, so sorry, Ray has actually elaborated on these duplicates. So you once tried running unique visitors to advertiser.com.au, came up with something like 26 million. And of course, our population was not that big. So look, that may be difficult, Ray, just to answer that on the spot here. We’d probably have to look at the actual setup and things like that. For now, if it’s a consistent problem, I would recommend going to client care and maybe showing them some reports and things like that. Or if you do have any consultants that you work with on a day to day, I would recommend talking to them as well. I don’t think I’ll actually be able to sort of diagnose that on the spot during our short little Q&A here. So some other typical questions that come in as well, clarifying the difference between a metric and a calculated metric. So a metric itself is, as Danielle would have showed you, is the units that you’re measuring. For instance, if you’re measuring your pages, it’s the number of page views that you’re getting. That’s your metric. Now, it’s not all the time that you can actually record metrics directly off your page. What if it’s a rate that you’re trying to measure? For instance, the rate of page views per to conversion or something like that. So that isn’t something that you just pick up off your page. So that’s where calculated metrics come in. And a calculated metric would literally, it’s like a calculator, right? You’d take two metrics, maybe conversions and page views. And then you could do a calculation to say, all right, let’s divide one by the other to produce a rate and a percentage. And then you could say, okay, here is my conversions per page view or my conversions per checkout process or whatever that happens to be. And then you can calculate those to your heart’s content. So a very common question that we see in terms of the difference between a raw metric that you’re taking off the page versus a calculated metric that you can actually build after the fact. And then a lot of people as well do ask, well, what type of visualization should I use with all of these metrics? And again, Danielle showed you the bar charts and the line graphs and those types of things. And really that’s going to be quite dependent on the nature of the analysis that you’re trying to do. So as Danielle mentioned, if you’re doing a series over time, then a line graph is your obvious choice because the line graph is the only graph in the workspace that will actually show you that sort of trend over time. Apart from that, it really is up to you which type of visualization that you use. You might use a pie chart if you’ve got sort of some big hitters and then the rest are kind of trivial and that pie chart really shows them out. You might use bar charts, stack charts, scatter plots, all those different types of things. I really would just play around with these things and see which one actually suits you and what the story that you’re trying to tell for the visualizations you’re trying to make. And of course, you can try multiple ones, put them side by side, see which one you like. There’s no real sort of hard and fast answer to that. And that’s actually a good point as well is that a lot of the, again, a lot of the questions I get are, how can I learn how to build up these visualizations? How can I actually upskill myself, gain more knowledge? And that’s where our experience league comes into play. So experience league is Adobe’s both our online documentation, our community forums, our support central and it’s a way that you can start to create your private learning curriculum and analytics is very well represented in that. So the experience league, I’ll actually ask Elisa, she might just pop the URL for that into the chat. It’s experience league.com, sorry, it’s experience league.adobe.com or you can just Google Adobe experience league and then you can find that. And I’d highly recommend having a look at that, signing up. It’ll ask you a couple of simple questions about your goals in terms of the role that you’re in and the type of enablement that you’re trying to do. And you’ll find some really simple, straightforward, five to 10 minutes short and sharp videos. Very, very powerful learning experience there. So I’m just going to check if there’s any more questions. That seems to be all that have come through. So again, just in terms of things to be, as you’re learning to get into analytics and so on, really there are no right and wrong answers. It’s going to come down to visualizations, reports the way that you need to still tell the story, but always keep in mind the stakeholders that you’re communicating to. Are they business stakeholders? Are they technical stakeholders? Will they want it in a nice, beautiful, glossy PDF? Will they want it in a more straightforward CSV so that they can load in and compare with other data sets? So really it’s quite an open-ended system. You can’t do any damage when you’re in there because you can simply drag experiments if you don’t like the results, just close that pane and try it again. Build as many test projects as you want, really get familiar with it. So I don’t think there’s, I think really the best thing to do is just get in there and play with it. Well, anyway, look, that’s all we’ve got time for. So Tanvi, I’ll hand it back to you to wrap up. Thank you very much.