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. So 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 web page 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. So 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. So 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’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. 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. Thank you. Good to be back. All right. Good to have you back. Let’s jump into some of the questions that we’ve received. And by the way, keep those questions coming. We really appreciate your participation. Lots of questions coming in. Okay. Let’s start with Nikita. Nikita asks, Danielle, how do we filter out traffic from unwanted sources? That’s a really good question. And definitely something most companies are looking to do. So we actually have a section within the admin area where you can go in and you can select a list of URLs that you want to remove from your data capturing. So that’s probably the easiest way. There’s a little bit more details if you go on Experience League or just do a search for deleting URLs. But there is a way to do that within the admin section so that you can make sure that it’s not skewing your data. Yeah, that’s great. Let me just add there. If you were to do a search for URL filtering on Google, URL filtering and Adobe Analytics, you will find it’ll take you directly to this link. It’s also, keep in mind too, you can filter out bot traffic, which is a common question as well. Definitely. Okay. Tara asks, can you explain more about seeing which users have visited numerous times? Sure, absolutely. So there’s a couple different ways. Earlier, I was talking a little bit about the histogram visualization. And this is a chart where you can go to be able to bucket your users to see how many times somebody has come to a specific area, page, site, section. So if you’re really looking to see people that have visited one to three times, you could segment those users as infrequent visitors. Or if you wanted to create a bucket for eight plus visits, you could segment that to say, these are my loyal visitors, and these are ones that I want to make sure that I’m making them happy and giving them personalized content. So histogram is a great way to do that. The other visualization, which I highly recommend, which we don’t go into detail about today, but is called cohort analysis. And the reason why our cohort analysis is so excellent is because of the fact that you can actually select what you want in your rows of data for retention and churn. So instead of just looking at date ranges in your rows to see how many people have returned visit to my website or app, or how many people have not visited the next week, you could actually look at it by a specific dimension. So if you wanted to see how many people visited a specific section of your site week over week or month over month, you can do that with cohort analysis. You could also look at how many people turned after watching a specific piece of content, or how many people returned and visited after they made a purchase. So you can actually use products or page URLs or any type of dimension that you want. So I highly recommend that you go into our cohort analysis visualization panel and play around with it, because there’s a lot of really cool things you can do there. And we have some great videos again on the YouTube channel that kind of walk you through some of the different features of cohort. Yeah, I 100% agree. Cohort is one of my favorite visualizations. And it actually will surface a lot of insights that you may not be aware of. Thanks for that. That’s a great explanation. OK, Jeff asks, are you able to create summary change from two different points in time, for example, year over year within the project with a different date range? Yes, you can absolutely do that. One of the greatest things about our kind of right click summary change capabilities within a table is that you can customize the date range in that second column. So if you have your set date range based off the project or panel dates, and then you select a completely different date range, it could be that same kind of time length. So say if you’re in this quarter and you want to look at previous quarter, or if you want to customize it, you could say, I want to look at this quarter compared to last quarter this year. So you have the complete flexibility to be able to customize the date range in which you’re comparing each other against to look at percent change. Perfect. That’s awesome. OK, Tara asks, can you explain more about metric possibilities about sites that do not sell but rather share content, or the main purpose of that site is for users to engage with content? What a great question. Yes. Because of the fact that I work with a lot of media and entertainment and telecommunications customers, I oftentimes am looking for examples on how to use our metrics and KPIs to really understand content consumption. So what you want to do is you want to make sure that you’re capturing with your dimensions name of your content. So whether it be an article, a blog, a video, a slideshow, make sure that you’re accurately identifying what those dimension names are. And that way you can use any of the visualizations we talked about today to see the performance. So you can look at page views, you can look at video views, plays, starts, completes. You can even use something like the fallout report to see how much people are engaged with different aspects of your content. So if you want them to start on a specific article and you want to make sure that they get to the next article or watch a video, you can kind of see and visualize how they’re going through that journey. And if they’re not getting to the next piece of content that you want them to view, what’s causing them to drop out. And again, that’s in our fallout report. So you can use the right click functionality to see fall through. So yeah, there’s a lot of ways, a lot of great metrics, understanding visits, views, completion, all of that kind of good stuff to understand content engagement. Yeah, that’s great. Let me add on that. You can also create what’s called a calculated metric. I’ve seen a lot of customers who are trying to address engagement by creating a custom metric customized for their organization or their use case to understand and measure engagement, whether it’s content engagement, video engagement, page engagement based on what articles they engage with and so forth. So great. Yeah. One of the cool things that I see today is when you go onto content sites and when you look at articles, it will actually tell you how long the read time is. Maybe it’s a five minute article that’ll take you five minutes to read. And with those calculated metrics, based on knowing how long the article length is to read, you can create a calculated metric to say, I only want to look at people that spent at least five minutes on this specific page and create a segment out of that or create a calculated metric. So there’s definitely ways to be able to utilize our calculated metrics to be able to look at something custom like that as far as time spent. That’s awesome. Thank you, Danielle. OK, another question is fallout visualization only for video or media industry users or even finance users can use. So absolutely. Go ahead. I’m sorry. I was going to ask. I think, yeah, fallout visualization for different industries. Can you talk about that? Absolutely. I think one of the examples that I like to get for finance industry is imagine that you have somebody that’s going to fill out a form to sign up for a mortgage or they want to fill out a loan application for a credit card or any type of different finance example, credit cards, whatever it may be. As they’re going through that form and filling it out, there’s different aspects of that form. There’s different touch points or different steps in that form. So as you tag those different steps, those steps can be available to you within fallout. So you can drag and drop each touch point of that process of them filling out that form to see how far do they get into that signup process. Maybe it’ll be surfacing insights to tell you, okay, my signup process is a little too long. A lot of people are falling out at this specific step. So you can go back and rearrange or work on optimizing the fill out process, basically. So yes, you can definitely use it for different use cases. Yeah. I love the fallout visualization. It allows you to compare and contrast two different segments. So for the example of filling out a mortgage, you could have a segment based on the, maybe it’s the type of mortgage that they’re applying for and understanding the steps and where the conversion by each step at each stage in that funnel to understand where they’re falling out to, where do they go, where they navigate to. It really uncovers a lot of insight to optimize that funnel or that conversion flow. Okay, great. So Ashley, you had asked, could you elaborate more on fallout visualization specifically for steps, instructions, pages? Hopefully that was helpful. I would also direct you Ashley to probably, if you did a search on Google, if you just said Adobe analytics fallout visualization, it actually will give you step-by-step with an experience league. Or if you go to YouTube and do go to the YouTube or the Adobe analytics YouTube channel and look up fallout visualization, it’ll actually give you a full tutorial on there as well. Okay. Let’s go to this next question. What types of non-web data are you able to collect in Adobe analytics? Ooh, that’s a good question. You know, traditionally we have been focused on web digital analytics and we’ve progressively adopted new channels to be able to measure. So think about your mobile app or your tablets. Think about your e-readers. We are also able to measure devices like your home devices, Amazon echoes. We can measure connected cars. We can measure OTT over the top connected devices, you name it. And we can probably measure it. That’s one of the great things about Adobe analytics is just our ability to be able to capture so many different touch points and channels that visitors are leveraging today, because it’s not just somebody coming to you through your website. It’s actually somebody coming to you through your website, through your app, through your home device. So we can definitely capture a lot of different channels. So good question. Excellent question. And thank you for that response. Okay. Let’s go to this next question. What type of artificial intelligence and machine learning capabilities are available in Adobe analytics? Oh, we have a lot. So we have Adobe Sensei, our machine learning capabilities, and we build it into a lot of the features that we develop today because we understand how important machine learning is for picking up insights that are new and insightful and maybe not as easy to dig out. So we have one of my favorites, which is anomaly detection. So when you look at your line chart or your trending visualizations, this is actually using our machine learning capabilities to detect any statistically relevant… Okay. I can’t talk today. It’s statistically relevant anomalies in your data. Any peaks, any high points, any surges that are above the norm to be able to see, oh, wow, I have this anomaly. Look at how well my page views are doing it or look at how well my time spent is doing for this page. What’s contributing to that? And that leads us to another machine learning capability, which is contribution analysis. And I think we talked a little bit about this earlier, Clay. Contribution analysis is a great way to see what’s contributing to that anomaly in your data. So looking across all of your different data points and being able to surface to you the contributing factors there. Yeah, that’s great, Danielle. And let me add just a quick tidbit on that in terms of a tip or trick. The anomaly detection is critical for you to pay attention to. I work with a retail company, very focused on e-commerce and during Black Friday or some of the other big holiday events, anomaly detection helps them understand statistically significant trends in their conversion rate or their cart addition rate so they can see if it’s dropping or going up. They can actually look at that at a pretty finite granularity to understand if they need to make optimizations in the product mix or the offers on the home page and so forth. Great tip. OK, last question, Danielle. What is your favorite or what is the best visualization to start with if you’re brand new in Adobe Analytics? Oh, that’s a great question. I mean, a visualization, I would say summary numbers and summary change is the most basic, easy, fundamental visual because it’s keenly focused and surfacing on your KPIs. So when you start out with your first project, you want to understand, OK, let me think of two to five metrics that I’m really focused on to be able to answer my business questions. By surfacing up those summary numbers and summary change, it allows you to really see at first glance when you open a project exactly how you’re performing. You don’t have to go down to the freeform table and kind of sift through and search for your highlights or your data points. You can easily just create a summary number to surface your KPIs so you can see it quickly. That’s great. And that brings us to the end of our Q&A. Thank you so much for being here, Danielle, and answering all of our questions. We really appreciate your time. It was really great being here. Thank you so much.