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 and 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. 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 an 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 and 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. You might have heard of secondary server calls, which incur a cost that enable you to 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’s 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. So 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. So 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. So 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. So even when you select other cells, that data stays consistent. Another great tip here, remember when we talked about removing all the noise? Well this allows you to remove the legend. So the legend will say what the metric is or what cell, 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. So 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. So once you select these two cells and do the summary change visualization, it’ll then appear at the top as you can see here. So 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 in 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. Thank you, Daniel. And welcome back, Karif. This is our final Q&A. Thanks, glad to be back here. Super. Let’s dive in. We’ve got a question from Niharika. She says, we have our forms hosted on AMForms. Is there a way we can capture the forms data entered by users in analytics? Yeah, sure. Great question. And tells the story about how Adobe Analytics actually integrates with other products. And in this case, Adobe Experience Manager. So yeah, there are a couple of options here. In the AMForms, actually, when we talk about the data captured through the forms, you can do this through actions and then mapping it to data elements. Now when you go to the details in terms of how do you do that stepwise in the AMForms, when you go to creating actions, in the actions, you should be able to events and actions, you should be able to add Adobe Analytics as a source of extension in terms of data elements. Right? And that’s where you can configure what are the field values that you want to send to Adobe Analytics. And then in Adobe Analytics, once you have captured data through data elements and actions in AMForms, you should be able to look into based on form metadata, that is form titles and so forth, you should be able to do all the reporting based on form fields. And then further again, connecting it to other variables, not just the variables that is being tracked by forms. So yes, you can absolutely do that. Great. Melissa wants to know on adding a combination of dimensions, that’s data and channel, would that lead to any duplication of user metrics? Yeah, that’s a good question. When it comes to the best practices around using Adobe Analytics, and because it is free form analytics, of course there are ways you can also go wrong in terms of how you’re reporting data and how you’re looking at it. So one, this needs a bit of a knowledge in terms of how your implementation is being done, what is the data that you are capturing, what is the hypothesis that you are testing when you are looking at a particular number of report. To simply answer this question, it really depends what metric are you using. So by need, you might also need to look at overlapping reports, for example, a metric like page views. So when you look at the absolute unique visitors report, no, that will not show any kind of duplicate if you bring in multiple dimensions, because if you have to understand the data that you see in terms of breakdowns, bringing in multiple dimensions, they go through correlations and sub-relations between data. So the way it is being tracked in your customer journey, for example, there will be an option wherein people are on the same page and you are collecting multiple information about that particular register, and then there is a further element of data that is logically connected to that information, and that is how your implementation would go. So as long as it makes sense for you to break one dimension by other dimension, it should be correct data when you are looking at the right metric. It is expected behavior if you are seeing an overlap or double counting of the customer. For example, if you are looking at a visitor level information, so you created a segment which is at a visitor level, you are looking at a lifetime of a visitor and then you are trying to look at a particular dimension. In that case, what you are doing is you are looking at all the widgets that have actually aggregated to that particular visitor, and in that case, you should be looking at a number which might look inflated to you, but it is just that you are doing your analysis at a segment that is at a visitor level. So the simple answer is that, as long as you are looking at the right correlation of data and breaking it down and it makes logical sense and you are looking at the right metric, there won’t be any logical overlap or duplication of data. Great. Kabir wants to know what are occurrences and what do they help measure? He says all new tables seem to automatically measure occurrences, but he is not sure what that is. Sure, that is a good question. Especially if you are a new user to Adobe Analytics, some of these metrics, out of the box things that we have built in might be confusing for you. Great question. Occurrences is basically just talking about the number of times an event has fired, right? And then it relates to the number of other metrics that you look, which is widgets, visitors, and sessions, and so on and so forth. Now, occurrences is very simplistically put. What it really means is that if you put a tag to track a data on a particular page, so if you want to track page name information in a variable, when you pull an occurrences metric, it basically just tells you that number of times this tag has fired, as in that event has happened, right? And it could be a same page can be viewed multiple times in a particular given session. So when you compare that with widgets, for example, the widgets data is telling you that a page has been viewed at least once in a visit, and that qualifies for that metric. But when you talk about occurrences, that really tells you every single time that page has even reloaded for that matter, right? So typically, your occurrences metric will be quite big in size when you compare with any other metric because further down, you’ve got to be filtering, right, in terms of just looking at not what are the events fired, but you can look at a visit level or a visitor level. So occurrences is the base metric that talks about number of times a particular event has occurred or been fired in Adobe Analytics. Varun would like to know what are the types of AI ML capabilities that are available in the product? So good question keeps me excited, because you know, that is where we are building a lot of capabilities. Yeah, so Adobe Analytics ideology has been around productizing a lot of AI ML capabilities, right? And Adobe Sensei, as you know, the great product that that actually enables our all the AI ML, not just in Adobe Analytics, but across products, even in our creative solutions, right, our document solutions. Now, all the intelligence built by Adobe Sensei translate into tons of features that you see in Adobe’s analysis workspace, for example, attribution. So our attribution is really built on AI ML attribution, we have something called as algorithmic attribution, which is completely ML based attribution. So you just look into unbiased data, churns all the data out and builds the most optimal attribution model for that matter. We have segment comparison, wherein you can just drag and drop two segments, click a button, and then the AI automatically runs a comparison across various metrics and dimensions for those segments, right? We have anomaly detection, for example, wherein you should be able to look at an anomaly in your data. For example, on a given date, the number of page views coming from a particular channel is really high beyond your boundaries or thresholds, and you click on that, and the AI comes into play, right? And it really goes back and finds a contribution analysis in terms of factors, you know, parallel to what you call as a root cause analysis. And this is done automatically. So our region has been that number of analysis features that you would expect in terms of, you know, either looking at this data and building those analysis, how much of that we can either automate through AI or keep it unbiased, things like attribution, right? So how do we keep it unbiased in terms of, you know, self-learning models and things like that? So it’s very embedded. The AI approach and ML approach in Adobe Analytics is very embedded instead of an open workbench kind of approach that you have a UI, you go to write code, do attribution, build ML models and things like that. We are into productizing, making it easy for you. Thanks Gaurav, great insights there. So Yog wants to know if it’s possible to integrate Google search console in analytics and understand the entire flow of visitors from particular organic keywords. Also, how accurate will this data be since they are from different platforms? Yeah, that’s a great question again. So the first half of this is yes, you could of course look at, you know, a particular keyword level analysis. You would get data in terms of tracking keyword and onwards journey in terms of once you have clicked. Yes, quite honestly, there would be certain gaps in terms of, say for example, if you’re trying to optimize a keyword level journey looking into Adobe Analytics versus looking into of course Google Ads or even analytics, there would be some gaps. But my experience tells me that when you look into the keyword optimization kind of use cases, then a lot of this is actually a directional insight. And then the wealth of information that you can add on top of keyword level data from Adobe Analytics is where a customer uses Adobe Analytics to further optimize either keyword or further going into the channel level. But the sweet and sore answer is that there will be difference into say a Google Ads versus what you look at keyword because a lot of that data we may not be able to track and could be just in Google, but still there is a way where you can use Adobe Analytics to optimize your keyword strategies, whether for natural search or for a paid search for that matter. Great. We have another question from an interesting question from WC. WC asks, are we able to track an entire user journey from a non-closed environment, non-closed logged in to closed environment, logged in and are there additional tagging requirements for this? Yeah, very, very good question. Yes, absolutely. You know, somebody asked me a question, I get really excited because that’s where our positioning is. Adobe Analytics is very unique. It’s not a reporting tool for digital data, just that, right? It’s basically a customer journey analytics tool. So what it really means is that of course you can track journey between pre-login, post-login, pre-login for the post-login implementation we would have, you know, because it is related to data privacy and security. There is something called as declare ID that we can implement. There’s all the considerations around hashing and encryption and all that. You could do that. You can stitch your journey. There are a few additional implementation steps and you could stitch your journey pre-login, post-login. And not only that, you can actually go beyond devices. So what if somebody comes to your web, is not logged in and then she logs in, becomes a logged in customer. You complete that journey and the next time that particular customer actually just goes into your mobile device, right? Your app and then logs in there. The moment that happens, we actually stress the journey from web to your mobile device. So it’s not just pre-login, post-login. It’s also, you know, going to multiple devices and it’s just in that journey. And even stretching that, if you’ve heard of customer journey analytics, which is the extension of Adobe Analytics, is really enabling cross channels. So how do you track the journey of people from your customers from pre-login to post-login to cross device to online versus offline, right? So all of that is a possibility. It’s an open sky for you. Great. Rajat here wants to know, they’re having a challenge currently. He says, we are trying to pass daily visits, orders and other metrics into Tableau dashboard. Is it possible to do this without any manual intervention, any video or resource that can help him or guide him on this? Yeah, I mean, this is a bit of a specific question. So I don’t know if post-session, if there is a contact that you want to contact us and there’s a way we can help you. But it really depends upon how that workflow is today. You can definitely automate it. So I have seen customers automated this through bringing the data into report builder and then just automating to a macro and then possibly to a workflow, right? That can actually schedule sending data from my Excel or report builder. Another way is that you could also possibly automate it through sending the data on the data feed to your, say, for example, listening source in your Tableau and just keep, say, for example, storage as your data source in your Tableau where Adobe can on a scheduled basis drop the data, right? So all the metrics that you’re looking at will keep on dropping data there. There will be some implementation effort in terms of if you’re really aiming for a seamless, automated workflow for getting this data. Or the last option is also that if you could read data through API and then read the data from Adobe Analytics and just get that data into Tableau, right? That’s also a possibility. So there are a couple of ways here. Yes, it can be automated. But if there are any specifics, you will have contacts and you might want to reach out to us. Maybe another thing I can add here, Rajat, try reaching out to support, customer support with a CCT or customer success manager. If this is something that they can help you with, they can share documentation around that. And of course, if it needs customer implementation support, then your CSM can help drive that conversation for you. Another question from Rajat is on how can we see visits played by desktop, mobile, iOS app, and Android app? Is creating segments the only option? Yeah, great question again, Rajat. You’re on fire. Yeah. So see, there are a couple of ways, right? So some organizations from a governance perspective might have different reports for collecting, say app data or desktop data and all of that is a possibility. But as long as you have a roll up and then you have all the data coming in together, one is that you can of course do it through segmentation. But also if you look at the device level segregation of data in terms of understanding where you the traffic is coming from, for example, there is actually a device report in Adobe Analytics, right? So there is a dimension called every device and that’s where you could, if you just drop that dimension in terms of, say a date wise data or just look at from any other dimension for that matter. So there is a default tracking or device type, and this is based on the user agent and all of that information that you get. So you could either use that or you can have segmentation using multiple ways. I recommend the segmentation ways because the segmentation really gives you flexibility in terms of having like a very specific conditions in terms of how do you identify a particular device or a particular widget for that matter. So as I said, default segmentation, these are a couple of ways that you can actually look at it. Thank you, Gaurav. That’s all the time we have left for Q&A today, but thanks for answering all our questions. It’s been a great discussion. Thank you so much for having here and all the great questions. I wish good luck to all the learners and thank you, Richa.