Putting it all together

Transcript

Hey everyone, welcome back. So far we’ve learned how to get started in Analysis Workspace. We’ve learned a little bit about how to analyze the data in Analysis Workspace and now we’re going to pull it all together. Some of the things that we’re going to learn today are we’re going to learn about how to collect data for reporting. So this is all about how Adobe Analytics collects data that pushes into your reports. Second, we’re going to take some more of those basic visualizations and learn how to incorporate them into a project. Things like map visualizations or trending. And then the third part of this session is all about democratizing data. So we’re going to go into a little bit more detail on how to share reports and visualizations to other team members. Okay everyone, so let’s get started on how analytics works. This is all about data collection. When you think about analytics, what’s most important to us is really being able to provide a solution that gives you customer intelligence. It all starts with the data collection process. Traditionally analytics has been known as a web digital tool for you to measure web and apps. Well now we’ve expanded that to other channels. Think about your home devices that you use. Also your connected car or your call center. All of these types of channels are collected through Adobe Analytics that allows you to really truly understand everything that’s happening across all of your customers touchpoints.

Then the way that we process the data is also unique. The data collection process is unique to Adobe because we offer the capabilities to have real time triggers of events. We also offer something called context aware sessionization. This is a really unique capability because instead of being capped at that 30 minute session, we allow you to adjust it. So say for example you want to look at a mobile app session. You might want to reduce that to five minutes versus 30 minutes. Also for long form video content. Maybe you have two hours of content so you want to adjust that session accordingly. Whatever it is that applies to your channel that you’re trying to measure. The third part of customer intelligence is all about machine learning. I’m sure you’ve heard a lot about machine learning capabilities. We try to produce this in every single feature that we release. We have a lot of great capabilities like anomaly detection and contribution analysis. We utilize something called Adobe Sensei in our virtual analyst to be able to produce rich visualizations with machine learning like segment IQ or journey IQ. The final process and part of data collection is being able to put a play button to really put the data into action. We offer bi-directional data flows into other Adobe Experience Cloud solutions like Adobe Campaign and Adobe Audience Manager. All of this allows you to bring your data and insights into action into other Adobe tools as well as externally. Now let’s talk about the analytics lifecycle. The first part of it is being able to define your data set. This is where you really want to understand what type of business question do I want to ask. What are my KPIs, my key performance indicators? Once you’ve enabled the type of data questions that you want to ask you can go on to the design phase. This is where you’re able to keep track of those business questions and create a design based on the KPIs that you want to collect. The third part of the process is actually deploying your tracking and your measurement. This could be done through our launch extensions, our APIs, and our SDKs. So once you’ve deployed all of your tracking and measurement that leads you to your analysis phase and this is where you’ll spend the most time. This is where you’re able to actually answer those business questions that you created in your defined stage. This is a great way for you to really understand your insights and move on to the action portion, which is the last step. Action is where you’re actually able to take the data that you’ve collected, the insights that you’ve produced, and push it into play. And then this cycle is constantly ongoing. It’s circular. So we know that customers’ experiences are constantly changing and therefore we know that business questions are constantly being re-asked in different ways. So we want to make sure that we’re continuously looking at our data, asking new business questions to produce the insights that we need. Now let’s look at our analytics value framework. The first part of the framework and the lowest level is data integrity. This is where you’re asking questions about your data and gaining trust in the data insights that you want to deliver. This is where you’re asking your business questions and making sure that you’re collecting the data in a format that’s going to be successful for the business questions that you’re trying to answer. Most customers need to start here or revisit this step. The second part of the value framework is the reporting stage. This is a stage where you’re able to create rich visualizations with your data and send reports to other team members in your management to be able to explain some of the business questions that you’re trying to answer. You don’t want to spend too much time in the reporting phase. You’re able to leverage many different templates and just create a basic template that you can share across to other team members so you’re not spending a ton of time creating graphs and charts. Once you’ve shared out your reporting data and have that on a regular schedule, you can then move on to the next two phases. The third phase is insights. This is where you’re pulling together the data you collected and making sure that it’s answering the questions for your business, making sure that you’re collecting your KPIs correctly and noticing any trends in your data or any highlights that you want to make to your managers. The highest tier of the value framework is modeling phase. This is where the data scientists and statisticians come in and they’re able to utilize statistical modeling and propensity modeling to be able to understand their data in a more rich, in-depth way. As you move up across these four steps, it will provide greater value in your data. Now let’s move on to the data collection part of the process. This is how we actually collect data within Analysis Workspace. Once you arrive to a website or an app, this is the first part of the journey. This is where we’re able to actually apply the analytics code and tracking pixel to understand what actions are happening on your website or app. This data is then sent to our Adobe data layer in Adobe Launch. It invokes an image request that then sends it back to the webpage in order for us to collect all of the data and actions that are happening on that page. It’s basically just a transparent one by one pixel, so nobody on the end user side notices anything that’s happened at all. Finally, once we collect that data within our data servers, we’re able to process it and produce it into reports and report suites in Analysis Workspace. Okay, you’ve probably heard me mention report suites a lot by now, so let’s talk a little bit more detail about report suites. We like to give an analogy of a closet. Say you’re someone that wants to be super organized in your closet and you want to organize your clothes by shorts, pants, dresses, skirts, etc., but you want to keep it separated. You may have a report suite for your skirts. You may have one for your pants. All of this is inside of your closet, which can be labeled as your global report suite. Think of it that way. You have your report suites that are sectioned out according to how you need it. Maybe it’s mobile data versus web data or call center data. Those are your individual report suites, but when you want to see those data combined and all together, you have your global report suite, and that’s where you can get to everything within data collection. Something new that we also provide is our virtual report suites. So you might have heard of secondary server calls, which incur a cost that enable you to kind of break out your data. Well now virtual report suites are not at a cost, and you can actually segment your data that way as well. Now let’s look at how to build some basic visualizations within Analysis Workspace. We’ve collected all that rich data, so we want to see it in action now. It may be a little bit tricky understanding what type of visuals do I want to choose for my reports. So here are some tips for you to consider when you’re thinking about what type of visualization do I want to use. First you want to identify the most important data. This is where those KPIs in the define stage come in handy. Second, you want to choose the best visualization that’s there to tell a story of your data, and we’ll talk a little bit about that in the next slide. Third, align your visual with a story. Every report should tell some type of story and answer some type of business question. Remove any unnecessary noise, and we’ll look a little bit about that when we go into Analysis Workspace again and create a project. You want to be able to highlight your main takeaway so that anybody who’s reviewing the report understands what business question it’s answering. And finally, you want to make it easy to consume the report. Don’t add in too many visuals in there where it’s kind of confusing and hard to read through. So now let’s look at what type of visualization to choose. The first type is your comparison chart. This is the one that’s probably used most often. This is where you can get your vertical and horizontal bar charts. You can also use things like our table or our heat mapping conditional formatting to more easily understand differences between two different data points. The second type of visual is trends. Trending is very important. The line chart is probably the most often used visual because it allows you to see how a piece of content is trending over time or how a metric is trending. Just remember that the line chart uses time as the dimension, but you can use any metric to see how it’s trending over time. You can even have a dual access visual here. Next, the other type of visual that we have are parts to a whole. The most common chart that you’ll see here is actually a pie chart or a donut chart. These are great visuals when you want to understand parts to a whole. Then we have relationship charting. This is where you can have more advanced visualizations like scatter charts or even bubble charts. We have diagrams, but we also have something called segment IQ where you can have visualizations populated for you with our Adobe Sensei tool. That allows you to look at two different sets of segments or data sets. The final type of chart here is distributions. This is our histogram chart. This is where you can measure something like how many visitors are coming one to three times a week, how many are coming three to five times, how many are coming five times or more. Being able to bucket and group these types of visitors through our histogram chart is a really valuable tool. Like I mentioned at the beginning of the Learn session, we have a lot of different types of visualizations. We have over 20 that are available for you to be able to use in your charts. Remember that that visualization icon is there for you to be able to use at any time. You can right-click as well to be able to visualize it. Just make sure that you’re selecting the right cell, row, or column that you want to visualize. I’ll show you in a little bit about how to lock in that visual. Here’s two different tips to getting to visualizations aside from that left navigation menu. The first one is anytime you’re on a cell and you want to visualize it, you can simply right-click on it and then select the visualization of your choice. The other way to do a visualization is actually if you look at a row and a table and you hover over that cell, you can see the visualization icon come up. When you click on it, it will detect what type of visual would be best for that piece of data so that you can select it to have a visualization populated in your report. Now let’s start to build out some visualizations in a project. The first most common type of visual is the summary number. The summary number is where you can surface your KPI. The first thing you’re going to want to do is either drag over the summary number visual from the left navigation or you can simply click on a row, a total line, and right-click to visualize it. What that will do is it will select the row that you selected and show the total summary in the summary number. This is a great way to kind of highlight in large font a summary number of your choosing. Maybe you want to see the total number of visitors in the time frame or you want to see the total number of products purchased in that time frame. You would then be able to select it from the column and be able to produce this visualization as you can see in the screenshot here. Now this is really important. This is something that is sometimes not followed. If you’re creating a project or report, you’re creating a visualization and you’re like, why does the data keep changing in my visual? It’s so important to just do this easy step. Once you create a visual, you’ll see this little wheel at the top. That’s your visualization settings. When you click on that, that’s where you’ll be able to lock your selection. That enables you to lock the data in the cell that you selected for the visual so that it never changes. Even when you select other cells, that data stays consistent. Another great tip here, remember when we talked about removing all the noise? This allows you to remove the legend. The legend will say what the metric is or what the cell actually means that’s selected. If you remove that and just keep the title of the summary number, then that reduces some of the noise and makes it better. Additionally, if you have a number that’s really long, you may want to abbreviate it as well. Another popular visualization that we have is summary change. This is being able to look at differences between two numbers. What you’re going to want to do here is make sure that you have two cells selected. The first cell that you select will be the numerator and the second cell will be the denominator. Once you select these two cells and do the summary change visualization, it’ll then appear at the top as you can see here. Between these two cells, there’s a 0.3% difference. Make sure that you go up into the settings and lock the selection. Now let’s look at this quick demo. In this demo, I’m going to show you how to create summary numbers and summary change. I’ve named it here. I’m going to go to the metrics and dimensions components and drag it over to my table. Now that I have all the metrics and dimensions that I’m selecting, I can go back to the visualization section. I’m going to go down to summary number, drag it up, and make sure that the cell that I want is selected. I can then go and adjust it if I want to show it on a different cell. I’ll name the summary number so that I know what it’s for. The metric usually is what we name it. And then I’ll go in and lock the selection to make sure that the data doesn’t change. So now when I click around, it still stays on that set data. Here I pulled over the summary change visualization. I’ve selected two different cells. As you adjust and change and select different cells, the percent change will change. So make sure that you have the two cells that you wanted selected and you lock the selection. The next visualization we want to create within the report is our trending line chart. So I use this chart a lot to be able to understand how things are trending over time. You can use this trending report to be able to look at different intervals of date ranges. So say your report suite is scheduled for a year. Your project has the year to date data. And I want to break it out by week. I can break it out by week. I can break it out by month. I can break it out by quarter. There’s different date ranges that you can select. And remember that only time is able to be used as a dimension in this trending analysis. So in this demo here, you can see I have a table and I’m looking at online revenue for my customers. I have it for this month only. I’m going to go to the visualizations and I’m going to pull over the line chart and it’s going to populate the total for that column. So I can see all the online revenue for that time period. I can also adjust it if I select different cells. So if I just want to look at one specific week and every day within that week. And don’t forget that you can actually change the granularity. So I’m going here from day to hour. Now you can see it’s a lot more detailed. This would be a great visualization if you want to look at trending for a specific day, say Black Friday or another big sale day in which you want to track online purchases. Don’t forget to lock your selection. That makes sure that you keep the data consistent on the cell that you’ve selected. Another really rich visualization is the map tool. You know, maps are often used in online reporting and they’re super important because maybe you want to track your activities in one state versus another state or within North America and the rest of the world. So these map visualizations are a great way for you to be able to look at your data and also use it to segment. So maybe you want to create a segment of all users within United States and then you want to apply another segment on top of that, like all users that are using their mobile app. So the map visualization is a really great way to segment your data and look at how things are trending and tracking across the world. The final visualization that I want to talk about today is a fallout visualization. This is a really rich, powerful visualization because it allows you to pull in any touch points within a journey to be able to understand how users are interacting with your site and where they’re falling out or falling through to. So I’ll give an example because a lot of times I work with customers in the media realm and they want to track video content. So for their touch points in the fallout visualization, they’ll use a video start, all of the quartile events that happen with them in the video and then the video complete. So they can see exactly when users are falling out or disengaging with that piece of content. You could also use it for something like a signup process. Maybe you have five steps in the process and you want to see where visitors falling out so I can re-engage with them and get them to finish the process. So now we’re complete with the project. We have a lot of rich visualizations. We have our KPIs and the summary numbers. We’re even able to show summary changes so we can see how things have changed over time. We have a rich trending line data so we can understand revenue over time. Where were our peaks and anomalies? Where do we see some dips in the data? We also have our map visualization to show how we’re performing across different regions. And finally, that great fallout visualization to understand what touch point people are disengaging with your content or your page. All right, now that we’ve created our project, let’s look at how we can democratize that data within the organization. I’m going to leave you with this quote. An analyst’s job is not to pull the data. The job is to translate the data into stories that drive actions and results for our business. It’s really the most important to be able to answer the business questions that you need to. So when you’re pulling reporting and you’re pulling together those visualizations, here’s some important steps to remember to produce your data into insights. First of all, you want to think about what is the request? What are they trying to answer? Make sure it’s not too broad and you really define exactly what they’re looking for. You want to understand the audience, know who you’re trying to reach with your data and insights. You want to be able to speak the language. Don’t leave the reader guessing as far as what you’re trying to tell them with the data and insights that you’re providing them. Fourth, know the value of your insight. You really need to understand what your data is aimed at producing. What type of insight do you want your readers to understand after they look at your visualizations and reports? And finally, question your assumptions. You’re going to get some tough questions. You’re going to get questions around, well, why did this happen or why did that happen? So really be able to understand your insight and what’s happening with the data and be prepared for those tough questions. Now you’ll be ready to share your project. Once you know the answers to your questions and you’re ready to share the data and insights, you can follow these easy steps to share your projects. We have several different ways that you can make it available to your team. One of the first things that you can do is you can go to the share section at the top of your project and share the project directly through Analysis Workspace. You can also copy a link to your project and send it through IM or email. You can send the file through email. You can also curate the project data. So let’s talk a little bit about how to curate your projects. When you curate your project, it’s probably because you don’t want to be able to share everything within your data set. Maybe you’re wanting to share to a team that’s only interested in mobile data and revenue. So what you can do here is you can curate a project and share certain components of that project to the other user or team. So you want to make sure that you save your original project first, and then you can create a new project off of that one and select the components that you want to be made available to the other user. Once you share that curated component, they’ll be able to see it within Analysis Workspace as if it’s any other project that you’re trying to access. You can also do actions like downloading and sending the report. So if you want to download the data, we actually have up to 500,000 rows now available. So you can download it to CSV or Excel. You can also share the project through PDF format. So maybe you want to share those rich visualizations so you can send that over via PDF to your boss or to your management team. Okay, now that we’ve gone through how to create and share a project, I just want to leave you with one final thought. Remember that you can continue to learn. We have a YouTube channel with tons of videos and content about how to leverage Analysis Workspace. We’re continuously adding new videos about new features or ways to use the Analysis Workspace. But I’m sure you have a lot more questions than just what’s available on YouTube. So I’m here now to be able to answer some of your toughest questions. So let’s go for it. Welcome back, Danielle. Yeah, so keep those questions coming in. We really appreciate your participation. So thanks, Danielle. That was really a great overview and bringing it all together for us. But a question here. So what are some important pitfalls to avoid for a new user starting out? I would say try not to go too broad. Think about what type of business question, what type of story you want to tell. Because remember, from the tips about visualizations, you want to be able to tell a story with your report, be able to produce insights that are going to be valuable, and that are going to help to answer your business questions. So like I said earlier on, make sure that you’re considering what top business question do you want to answer? Which handful of key performance indicators or metrics do you want to be able to capture to be able to answer that business question? Then from there, you can start to build out a really basic report focusing on your freeform table. How do I want to segment it? How do I want to apply certain dimensions that help with that business question? What date filters do I want to use? Just keep it simple at first. Don’t try to get too fancy. Don’t try to add too many visualizations or too much noise into your report. Because remember, it’s not just for you. You want to be able to democratize your data, share out your reports, and be able to produce insights that are going to be valuable to your organization and that are going to help answer those business questions. My top advice and things to avoid is keep it simple. Don’t add too much to it. Make sure that the report is simple, easy to understand. Anybody can go in and look at the report and know exactly what you’re trying to do, what type of business question you’re trying to answer. Yeah, that’s great advice. Yeah, keep it simple. And on a similar note, a question here on the visualization. What would be the best visualization to start with in a project if you’re new? Yeah, so like I said, starting with the freeform table, probably the visualization I use the most when starting and creating a new project is the summary number and the summary change. By surfacing up the summary number, which is one of your KPIs, that allows somebody to go into the report and instantly see in a large font what that KPI is, how is it performing. And then right alongside that, you can show the percent change or the summary change from a previous time period, from a different product, whatever you want to kind of compare it to. So having your summary numbers and KPIs is very important. I also like to encourage using calculated metrics or percentages, because that way you can see performance in a much easier to digest format. So that’s where your summary change number comes in. What’s the percent change from the previous time period, whether it be you wanting to understand how many page views have I had, how many visitors have I had, how many purchases of my products have I had, and then comparing it to that previous time period. So I’d say the most important metrics or visualizations to start out with with the report is a summary number and summary change if you want to add that in as well. Brilliant. And what if I wanted to see trends? What visualization would I use for that? Oh, we have so many great visualizations for trends. The one that I encourage you to use the most is probably the line chart. The line chart easily shows you trending over time with any metric that you want to look at. You can even apply segments to it. You can look at two trend lines within one chart. You can also utilize our machine learning capability within the line chart, which is anomalies. Anomalies uses Adobe Sensei to be able to understand spikes and peaks in your data. So something that’s significant to your data points that may be a call for attention. Maybe it was a product release date and you had a surge in product purchases or you had a specific promotion that enabled that surge in the purchases or you have a dip because you have a high number of errors on your website and people are exiting out or they’re not able to navigate to it. Anomaly detection is a great way within line trending to be able to visualize and understand performance over time, as well as certain significant events. I love using trending visualizations because it’s so easy to understand and be able to see trends in your data, whether it be visitation or purchases or subscription signups, whatever metric you want to choose. Yeah, exactly. Anomaly detection is such a powerful tool and just can save so much time. Beyond anomaly detection, are there any other AI machine learning capabilities available in the product? Yeah, so we try to incorporate our machine learning capabilities into as much feature releases as possible. With anomaly detection, it’s an excellent feature of our tool. But to take it one step further and a little bit more advanced, you could actually use something called contribution analysis. What that allows you to do is as you see that anomaly, when you hover over it and select the analyze call to action, it will do a contribution analysis for you. So it will produce data within a table and visualizations to understand exactly what contributed to that anomaly. Maybe it was a specific product or a specific day or location or device type. It’s just a step forward in terms of your analysis to be able to understand what contributed to my anomaly. So that’s a great machine learning capability. We also have segment IQ, which allows you to look at different segments. Remember earlier on, we talked about segments and how it’s based off of characteristics and visitation behavior. So you can apply two different segments, three different segments, et cetera, to be able to compare against. And we actually look at overlaps and unique differences between those two segments or three segments, whatever you choose. It’s a great way to understand overlaps in terms of what new visitors versus existing visitors are consuming, what products they’re looking at. How can I use that information to further action against it and personalize those segments better? Or to create a segment out of two segments based off of their similar characteristics. So that’s another great machine learning capability that encourage customers to use a segment IQ. Yeah, brilliant. OK. Now, a slightly different topic here, more on the data collection front. So question from Mohan. As with the Adobe Visitor ID page clicks, what are other important data points that Adobe collects at the time of a customer visit? Well, it’s really it’s absolutely up to how you want to be able to measure against your pages in your apps. You have the flexibility to be able to decide what types of measurement we have. Obviously, we have the hit visit and visitor level of metrics that we’re able to understand. But you want to go beyond that to understand what’s happening, what are the events that are happening within that page hit or that visit or that that visitor that they’re accessing. So anywhere from the number of people that view an article that scroll down on a page that click on a button or open a form to start the signup process, what phase of the signup process do they get to being able to basically just tag and analyze every touch point or interaction that your customer has with your page or your app. Adobe Analytics is able to measure. So as you’re trying to start to tag with Adobe Analytics, definitely go through your site or your app and really evaluate and understand what would be important to me and my organization to be able to track and measure to understand success of my brand and my service. So there’s so many different types of metrics and dimensions that you can measure. Lots of examples, but we do definitely go beyond the page visit visitor level to be able to understand every type of interaction that customers have.

Yeah, absolutely. And on a similar note, if we’re looking beyond the website, what types of maybe non web data are you able to collect to then analyze and report on? Yeah, that’s a great question because we’ve done a lot of work. There’s a lot of emerging channels that are coming into the market, and we’ve seen that customers more than ever are starting to leverage different types of devices to access a page in an app. You’re not just using your laptop or your desktop anymore to go to sites or apps. So we now are able to measure not only mobile and tablet devices, but connected TVs, gaming consoles, e-readers, connected cars, which is pretty cool home devices. That’s a really neat one because that enables you to measure different skills that are happening on home devices. Adobe Analytics is able to measure and capture that. So if you’ve developed a skill for Alexa, say, to purchase a certain product on your site or your app and Alexa’s measuring that, you can measure that through Adobe Analytics. So that way you’re able to measure the full customer journey of every type of digital interaction that’s happening with your customer because they may not purchase it through your mobile app, but they may go onto their laptop later and purchase it, or they may go to their home device and ask for them to purchase it and deliver it. There’s some really cool capabilities that we’re able to measure now on these emerging channels. So definitely, if you’re interested in learning more, reach out to your Adobe point of contact and they can help you to understand how you can leverage different emerging channels and measurement with Adobe Analytics. Yeah, that’s really exciting to hear so much about those emerging channels. As an extension to this, would you be able to maybe provide a bit more information about journey analytics that we saw earlier? Yes, absolutely. So journey analytics is a big focus of Adobe Analytics because we want you to be able to understand a customer and their journey. What’s really important, like I just mentioned, is that there’s so many different devices that a customer is accessing to be able to reach a certain milestone or goal for your organization. So you want to be able to capture that journey through journey analytics. We have something called customer journey analytics, which is new, which allows you to actually capture both your online digital activities with Adobe Analytics, but you can also combine it with your offline activities or other kind of solutions. Bring your data in from your CRM channels to understand certain customer related traits combined with your Adobe Analytics data. You can bring in offline data like call center data or even your point of sale transactions. There’s no limit in terms of the types of data you can bring in from both online and offline sources. So you can combine it with Adobe Analytics and be able to visualize the customer journey and customer journey analytics through things like flow and pathing and cohort analysis. So it’s a really exciting new advancement in the way that we look at Adobe Analytics because it goes beyond just digital measurement and takes you into offline channels as well to combine that data source.

Brilliant. Yeah, really exciting stuff. So the question here from Alexandra, does Adobe suggest any benchmarks for the various metrics or are we only able to benchmark against the overall data for the site? So that’s a good question. Benchmarks usually typically come from your own data sources. We don’t I mean, we do provide some insights as far as overall trends in the industries, but as far as business benchmarks for your specific business and your goals, I would I would start by looking at your own data sources and applying that, maybe looking at benchmarking your performance this quarter compared to last quarter or this month compared to last month. Those are more realistic benchmarks because it’s comparing it to your own organization’s data versus just an overall kind of overarching benchmark, which can include companies not similar to your own. So I’d encourage you to set up goals and alerts. You can set up alerts within Adobe Analytics to set a certain threshold or benchmark that you want to reach. So that way, when you hit that goal or benchmark, you’ll be automatically alerted to it. So you can surface that insight up to your upper management to show, hey, we hit this great milestone, this great goal that we’ve been trying to achieve. We were able to monitor and set this goal within Adobe Analytics. And it’s just an exciting milestone to be able to celebrate or be alerted upon if it’s something negative that you want to be able to quickly troubleshoot. So I’d say for benchmarks, I would I would start with your own data, but we do offer some kind of overarching industry specific benchmarks to.

Great. OK, and a question from Donald here. Can I analyze raw web server logs? Oh, that’s a good question. Raw web server logs.

I’m more so I’m more so utilize Adobe Analytics. I believe you can do this type of analysis and data workbench. Tom, do you know specifically I don’t necessarily look at raw web data logs, but that’s a great question that we could absolutely follow up. But Tom, do you have any experience with that with customers? I imagine this is less relevant for analysis workspace itself. And maybe we have to look at some type of data export or extraction. So, yeah, Donald, this is something that we can either follow up directly or there’s also plenty of documentation on this topic as well. Adobe Experience League has plenty of resources on that. Yes, absolutely. OK, a question from Peter here. This is a topic that’s close to my heart. What is your advice to grow and become more mature in Adobe Analytics? So what maturity levels do you identify and how can these users really start to develop their maturity in the product? I would say one of the biggest things is and this is what I kind of did when I first started at Adobe and I was new to their analytics interface. I came from Comscore before, so I was used to a totally different reporting tool. So the best thing I’d say is getting experience in the tool itself, just playing around, trying different things, but absolutely utilize experience league and all the great videos that are there to be able to truly understand all the different capabilities. There’s so many different things that you can do within Adobe Analytics workspace. So being able to leverage all the videos that kind of show you the different features and different use cases that you can do, as well as being able to get a better experience and be able to kind of just try things out for yourself, work with your consultant or your Adobe contact, schedule demos. So making sure to attend these types of experience makers events, I say is another must do, because that way you can learn about new things that have come up in the product, new things that you’re able to leverage and utilize. Maybe next time instead of the learn track, you’ll go to the grow track where you can learn more advanced kind of analysis workspace features.

And just the one thing I’ll add to that question, beyond all the great content that’s available, like the tutorials, YouTube, Adobe Experience League, we do offer more formal certifications or learning certificates. So you can actually get certified in Adobe Analytics. And in terms of maturity, we also have some tools that we work with our customers on that we call CX maturity assessment. So we can actually assess your organization’s level of maturity or your team. And then we can start to review that, monitor that over time and help identify how we can drive that level of maturity within your teams. So again, you can work with your Adobe representatives on that type of initiative.

And just final question here before we close, because I know we’re short on time. Question from Caroline. Can you see more specific visitor information so that you can analyze what type of visitor is viewing which content, i.e. external versus internal? Internal versus external in what way? Well, I mean, go ahead. Sorry.

No, no, I’m not entirely sure. You know, potentially within the organization or outside. But yeah, we don’t have Caroline on the line. But yeah, is there anything that you could talk to in that particular question? Yeah, I mean, there’s definitely ways that you can segment your visitors based off of characteristics or behaviors when they’re viewing certain streaming media. Another thing that we have is an integration with Audience Manager, where you can bring in audience data. It’s what we call Audience Analytics so that you can layer on certain maybe demographic traits or household traits on top of your analytics measurement. So that’s a great way to be able to apply even more visitor details to your analytics.

That’s great. Thanks, Danielle. You’re welcome.

Plenty of questions there. And I hope we got through everything that you and the audience wanted to ask us today. So that brings us to the end of the Q&A. Danielle, thanks a lot for being here and answering all our questions. We really appreciate it. Thank you, Tom. Thank you, Tom. Thanks, everyone.