Workspace Features that you Should be Using

Transcript

Hello everyone and thank you for joining today’s session on Workspace Features You Should Be Using. I’m Purnima Thakur and I’ll be taking you through this session today. Before we move ahead with the session, it would be really nice for me to introduce myself. So here goes. I am an analytics lead at Lenovo. I live in Mumbai, which is based in India. I’m an Adobe analytics champion for this year and I have been using Adobe Workspace since 2015. I love giving back to the community through digital newsletters and mentorship sessions on LinkedIn. Other than digital analytics, my other passions include public speaking and tennis. If you’re a Novak Djokovic fan, we are going to be best friends. If you want to connect with me after the session, here’s the QR code that you can actually scan and send me a request on LinkedIn. I look forward to hearing from all of you. Let’s go through the agenda for today’s session. We’ll be starting with conditional formatting in freeform tables. Those that help you spot high performing or underperforming data instantly with visual cues. Then we’ll be moving on to dropdown panels in Workspace that make your trashboards smarter, cleaner, and a lot more interactive. Then we’ll be covering non-default attribution models for metrics because attribution models go way beyond last touch. And last but not the least, we’ll be covering functions and calculated metrics where I’ll tell you how to uncover insights with the power of maths.

So let’s start with feature number one, conditional formatting in freeform tables. Now conditional formatting is a feature that we often use in Excel. But what if I told you that instead of exporting the data from Workspace every time and using this feature in Excel, you can actually use that within Workspace itself. Because as we all know, they provide important visual cues to spot outliers instantly. Here’s an example that I’ve crafted for you. Imagine that your stakeholder wants to know the high traffic driving pages with high bounces in order to spot the problem area. Now if there is no conditional formatting in this table, all you will see are a bunch of numbers and you’ll have to go through each of them to understand the data. However, with conditional formatting, you can instantly spot that lead form step one page in this example is a high traffic driving page that also has high bounces and maybe is an area that should be looked into.

Now conditional formatting settings is not just that simple in Workspace. Adobe also offers you three important settings that changes the way how you look at data. First being percent limits. This option highlights the top X percent of values in a column based on the selected metric. It means that if you set the upper limit to the percentage of your choice, it color codes it accordingly. Next is the default auto-generated setting in conditional formatting where Adobe actually applies the colors based on the data that is provided. And last but not the least, we have custom where you get the entire flexibility to put whichever limit you want. Now let’s take a look at the conditional formatting example that we were talking about. So as you can see without conditional formatting, this is how your table looks. Let’s look at the three settings that we just discussed. So if I go within the setting icon here, what I can do is instead of the bar graph visualization, that is selected by default, I just have to click on the conditional formatting. And here are the three options that we just spoke about. In this example, I have chosen the percent limit. So I have put the upper limit as 20, mid as 10, and lower limit as zero. You also have the option to change the color palette if you please.

Now if we look at exits metric, this is using the auto-generated, which is the default conditional formatting given by Adobe. And last but not the least, in bounces, I’ve actually used custom where I have defined the limit myself. So as you can see, this is how the settings can be changed within Workspace.

Next, we move on to the second feature, dropdowns in panels. Dropdowns in Adobe Analytics Workspace are nothing but interactive filters that let users choose from a list of values. What that list needs to contain is totally up to you, because they make the dashboards dynamic, user-friendly, and very reusable. Have you been in a situation where you have to duplicate the same panel for a mobile view and then a desktop view of a different countries? Well, dropdowns in Workspace actually eliminate this problem entirely. Now let’s look at the two types of dropdowns that are available for us to use. The first are static dropdowns, where you actually go within a dimension item and choose the list that you want. For example, I can go in marketing channel and choose direct, referral, and social media, and by pressing the shift key, take and create a filter there. The next is dynamic breakdowns. Here, you actually drag the entire dimension item, so I can drag the entire marketing channel and create a filter of all the channels within that. But how are they different? They’re different because static dropdowns do not auto-update, and it’s good to use them when you have a fixed list to consider. But dynamic dropdowns, this is particularly helpful when the data keeps on changing. For example, if you have to create a weekly filter.

Now let’s take a look at static and dynamic dropdown in Workspace. For example, if I have to go within a dimension, let’s say mobile device type, and I have to only select desktop and mobile phone. So I’m just going to select these two, press the shift key, bring it here, and leave it. So what happens is that a filter with the selected dimension item is automatically created here. Now I can choose the view to be for either desktop or mobile, but please make sure to deselect the one when you select the other. Otherwise, your data is going to show a combination of both the filters that you’ve selected. Now let’s move on to the dynamic dropdown. Now this is particularly helpful if you have, let’s say, a week filter. So I can just drag the entire dimension here by pressing on the shift key. Now currently, well, let’s say I’m in the month of July, and this entire week hasn’t come yet. So if I select the data, let’s say I’m selecting the last week, I’m not seeing any data because I haven’t entered that date. So what happens is that as and when the data comes in, these will be automatically updated, and that is where dynamic dropdown is helpful.

Moving on to the third feature, non-default attribution models for common metrics. Now let’s take a look at a little complicated but very important feature. But before that, let’s understand what is attribution. Attribution is nothing but it refers to how values from success events are distributed and how they are represented in the dimensions based on the attribution model that you have chosen. You all must be aware of last touch attribution, which is very commonly used, where the last touch point within a journey gets the credit for the success event. But Adobe actually offers a plethora of attribution models that are at your disposal.

What you can see right now in front of you on the screen is a list of attribution models that you can use, and this is not even the exhaustive list. Let’s take a look at the participation model here for the sake of demo. Participation model means that every touch point involved in the journey gets full credit. But when do you want to use it? You want to use it when you want to understand the contribution of each and every touch point within your journey towards the success event.

Now let’s look at non-default attribution model example. Now let’s say that I’m looking at pages and the traffic orders and revenue next to it. Naturally, as you can see, there is absolutely no value that’s being recorded for orders and revenue for these pages because these are often attributed to checkout pages where the order has actually occurred. But what if I wanted to know all the pages in the journey that have contributed to that particular order and therefore the revenue? So that is where attribution model and participation model in this case can be helpful. So all I have to do is go to the settings icon, click on use non-default attribution model. From this dropdown, I’ll select participation. You have the choice to switch containers, but I’ll leave it to visitors and I’ll also leave it to the default look back window of 30 days. Let me click on apply. Now, the minute I do that, I can easily see that orders are now being attributed to pages that have actually contributed to orders. So it’s not just for the checkout pages where the orders eventually happened. This is how we use attribution models to understand data.

Now let’s move on to the last and the final feature for today, functions and calculated metrics. Functions and calculated metrics is one of the most underused feature in Workspace, but also the most important because it actually uses the power of maths to uncover deeper insights that we can probably not even imagine. You have a lot of functions and calculated metrics at your disposal, right from the basic math functions to regression, logical, trigonometric, statistical, you name it and we have it. But how do we use functions to our advantage while doing analysis? So let’s take a very simple example where I show you how functions can be used in your day-to-day analysis.

Let’s think of an example where I want to understand and see the bounce rate for pages where visits are greater than thousand. In that case, I’ve utilized in this example two important functions, the if function and the greater than function. So the greater than function that you see within the blue box is actually nested within the if function which you see within the red box. The greater than function here is giving an example saying that visits should be greater than thousand and I’ve nested that within the if which tells me that if this is true, return the bounce rate and if it is not, then just show the number zero.

Now let’s understand using functions and calculated metrics through a demo. So as you could see in the demo, I showed you how I created a calculated metric where I wanted to see the bounce rate of pages that have visits greater than thousand. So if you see this table, all I’ve done is just drag that calculated metric here and if I simply sort this table by that, you can see that any page that has the traffic less than thousand, so if you see this row, right, anything below that is going to return the bounce rate of zero percent because that’s what a condition said that if your page traffic is greater than thousand, then return its bounce rate or else show zero. So this is how you can use functions and calculated metrics. While this example is simple, let me show you where you can find absolutely any example for calculated metrics that you’d want to look for.

Obviously, it’s not possible to show you all the important functions and calculated metrics and how they can be used and for that very reason in the chat, you will now see a link to the calculated metric playbook that my fellow Adobe champion Mandy George has written. This is the holy grail of using calculated metrics and I urge you all to take a look at it. So let’s discuss the key takeaways from today’s session. Using conditional formatting is important if you want to instantly spot trends without exporting the data. If you don’t want to duplicate panels to show different views of the same dimension, go for dropdown filters.

Go beyond default attribution models and unlock the power that Adobe has to offer by experimenting with different models and uncovering deeper insights. And last but not the least, uncover the real power of calculated metrics by using functions within those. This is it from today’s session and now I’ll pass it on to the host for the Q&A. Thank you. Well, thank you for sharing all those great tips, Purnima. Our final Experience Maker Spotlight is Damon Hall. Welcome, Damon. Hello, everyone. Thank you for joining me. I am Damon Hall and we are going to go over channel attribution and why it is the backbone of your marketing tech stack.

So we’ll go through some introductions, why bad attribution is bad analytics, and why you should prioritize good channel attribution. We’re also going to go over the TRACE acronym and how it can help you to understand the remember the different important steps for setting up your channel attribution.

We’ll focus specifically on the clarity and experience part of that acronym, and then we’ll go over key takeaways.

So a little bit about myself. I have been an Austinite since 92. My wife and I have four rowdy kiddos that keep us busy all the time, and we love to go to the beach and see different cultures and cuisines. I speak Russian and fun fact about myself on the professional side. I’ve been an Adobe Analytics champion 24 to 25 and love using Adobe Analytics to provide insights to marketing stakeholders.

I also love using Adobe Target to act on those insights.

So digging in, why does bad attribution equate to bad analytics? I’m sure we can all relate when DemandGen comes to us and they ask, hey, how is our last campaign doing? When you flip open Adobe Analytics and you see no data or bad data, you have to ask, has the campaign even started yet? In this case, we see that instead of being ad visitors, we see a large body of unspecified visitors. And as we go through all the different metrics and trends, we are seeing that this unspecified audience is pervasive throughout the entire report. The downside here is that your stakeholders now don’t know what to actually attribute any of the conversions towards. And there’s a lack of trust now in the data.

It doesn’t have to be that way. But this is also why focusing on putting into place a good channel attribution plan is important. To help illustrate that importance, I like to think of our sites as a mall. So if you think of your site as an outlet mall, all the different parts of your outlet mall represents parts of your sites, your search, your cart, your home page. These are the different shops. And with visitors coming to those different parts of your sites, you’re able to see an increase or decrease in traffic to any of those places or increase and decrease to the year conversion points. But what you’re not going to be able to tell is why those increases are happening or why you see a drop all of a sudden. And you can’t provide actionable information to your marketing stakeholders. With a good marketing attribution plan in place, you now see that you can see any display ads come in, SEO visitors come in, paid search, and you could provide your stakeholders with insights as to, hey, there was a drop in traffic or a drop in conversion rate. And you could even provide them the understanding of which of their visitors, your shoppers, if you will, are your hire converters. On top of that, if you put in a good attribution plan into place, you can use those audiences for personalization and A-B testing as well.

So the trace acronym, the trace acronym helps me remember those different pieces that you need to put into place as part of your attribution plan. The first is taxonomy. Taxonomy is super crucial.

Regulate enforcing that taxonomy, anchoring, allowing that taxonomy and that attribution to persist, and then clarify and experience. And this is clarifying Web analytics and providing that clear view to your stakeholders and personalizing the experience of the visitors. Today, we’re not going to go into as much detail on the T-R-A part of this acronym, but it is important to go over why they are important and that they are a precluding step before you get to analysis and experience.

Taxonomy is important to put into place and to think about how those terms will impact your analysis. So it is good to have a collaboration with a related team that is setting up that taxonomy.

Regulate is important because once you have that taxonomy in place, you need a team to enforce that taxonomy with tools, regulation, government, governance, QA. And this ensures that that taxonomy persists and doesn’t have any errors within them, and that you don’t find issues with things like broken queries or double question marks in your queries and things like that. And then anchor. Anchor is important because it ensures that that attribution is soundly captured within first party cookies and that that persists through your conversion points like form conversions or your cart conversions all the way through down to the end of your funnel. And this allows you to have a full funnel view with your attribution in place.

With that understanding, let’s go ahead and step to the clarify part of the acronym. And that is, again, the clarity around analytics associated with your attribution. What we’ll cover today is how we can see conversion rate differences in your audiences, how you can see a trended analysis of metrics associated with your audiences, and despite your best efforts, bad things might happen. So how can we troubleshoot when those bad things happen and kind of get to the bottom of fixing bad attribution? So here is a dashboard that is a bit different than that Bing ad dashboard that we saw at the beginning. We could see the trends of your different audiences here clearly depicted. No mysterious audiences here or unspecified visitors here. We see your total visits, the related conversions, the related conversion rates, and we can see here really neat insights that you could share with your stakeholders. For example, while SEO has the most traffic and even the most conversion by virtue of there being more traffic, they aren’t the highest at the conversion rate. And so we see here that the SEM audience has a much higher conversion rate, and so stakeholders may want to focus on them because there’s more bang for their buck.

Here we can see a trended analysis here for display ads. If your stakeholders come to you and they’re wondering why there’s such a huge increase in conversions, you can come to them and say, oh, yeah, that’s display ads. We launched that campaign last week, and we could clearly see it’s display as that’s causing that increase. And for troubleshooting again, despite your best efforts, mistakes will still happen when they do. You can create a URL query dimension that allows you to see and check against your other dimensions. So in this case, we have a nested table view where I have dropped the URL query at the top. And in the query, you can see the, in this case, UTM parameter definition. And as I dropped in medium, source, campaign, content, and term, you see a one to one match on the same visitor volume. So in this case, you can see that the query is sound and that the related dimensions are properly capturing. However, if it was broken, you might see that one of these zeros out, or you see less of a volume than you would expect. And this will allow you to double check and look at that part of your query to understand, oh, hey, there’s two question marks there, or there’s a space or a bad character that’s causing a break in the attribution past a certain point.

And so I have found that to be a very useful tool is to have a URL query dimension that captures those things.

So experimentation, the last letter in the acronym, experimentation and personalization is something that you can do with a good attribution plan in place. You can have your engineering team set up profile scripts within target that will allow you to target based on these channel, channel attributions, and also allow you to do AB testing and personalizing on your sites. But precluding to all of that, you are going to want to help the related teams validate those audiences and ensure that there’s a good match rate. To do that, what I like to do is set up an audience that is first in Adobe Analytics, you set up a segment that is display ads on a certain page for a certain date range, and set up an AA validation test and target that has the same audience targeted, same date range, same page. Then you can then overlap those two different segments on top of each other. And if you see something like what’s on the screen now, you can know that the match rate is relatively sound. You’re not going to get a perfect match rate. That almost never happens. But in this case, you do see a really good tight overlap. And this means that the match rate is good. On the other hand, if you see something that looks like the following here on the right hand side, the match rate is not good. And this will then allow you to come back to the related teams and let them know that their profile scripts are maybe not working the way we would expect and that maybe we need to come back and reevaluate those profile scripts to make sure that something is not set right. So some key takeaways. Having a holistic implementation for your attribution plan in place empowers your web analytics teams to provide actionable data to your marketing stakeholders.

They also allow stakeholders who are focused on specific audiences to look at what efforts are working and which efforts are not working, and then they can focus on next steps. And then lastly, for your teams that are doing A-B testing and optimization, you can help those teams better understand if their audiences have a good match rate and target or if they do not.

So this concludes my section, and I look forward to answering your questions in the Q&A. Well, thanks, Purnima. Thanks, Damon. That was a rapid fire session full of useful info. This is our last Q&A, and we’d love to hear from you. If you’ve got questions, get them in now.

So the first question is for Purnima.

Full path analysis report versus flow, fallout and journey canvas all requires manual efforts before coming with meaningful conclusions. What’s your take on this? Well, if you talk about full path report, it automatically shows sequential touch points Yes, it can definitely get cluttered, so you still need to filter segment or group nodes manually in order to make it meaningful. Now, talking about flow reports and workspace is great for directional movement and understanding. Similarly, fallout is great for drop off rates at fixed steps, and journey canvas in CJA is amazing for orchestrating customer experiences. But each of these visualizations needs a lot of manual setup of steps and events, which I agree with. But the bottom line of this entire thing is that all of these tools are powerful visualizations, but insight will only come when we actually practice and use a lot of advanced use cases that come up day to day when we are working with our company data. So I think what I really like to do is that practice more. I like to look at advanced use cases from industry experts, whether it is on LinkedIn or through these sessions, because that gives a lot of perspective. I think when you go to Adobe, there are lots of blogs that are written by a lot of people who work on these things every single day. So when you read their playbooks, when you look at these examples, when you look at Adobe official videos, I think that gives a lot of context. So my take on this or how I generally go by this is to read a lot of articles around it and follow industry veterans on LinkedIn, because they definitely come up with a lot of good knowledge around these topics as to how to use these visualizations and actually make and create meaningful insights.

Yeah, that’s a great point. We have a huge group of people using analytics every day, and we might as well tap into all those people that we have out there. And that’s the great thing about what we have together. Everyone out there is we do want to make sure that we’re helping each other. I know for myself, I leverage a lot of people in the industry when I have questions or when I need to get something answered. So that’s a great point.

The next one is for Damon.

What happens if you see a high level of attribution to typed bookmarked in paid if you don’t have paid efforts turned on for a period? Yes. So if you don’t have paid turned on for a time period and you start to see type bookmarks show up at a higher rate, that almost always is associated with either bots and you have something going on with your bot attribution that isn’t quite right or filtering or your other means for attribution, whether you have organic or nonpaid audiences being captured and something has broken regarding those data elements.

The other side is if you do have paid attribution that’s perpetually on, but let’s say you’ve turned off a campaign or you don’t have anything flowing through, then attribution sometimes breaks whenever you have spaces within the query or to question marks. Those are things that I’ve seen. That’s why back in one of my recommendations, I mentioned that using a query based intervention to help troubleshoot some of that will allow you to see some of those breaks. Okay, great. So the next one goes back to Purnima. I have a product page and want to find out the orders that were placed from that particular page. If I use participation, wouldn’t that attribute an order even if the actual order was placed on another product? For example, product A page, but the user buys product B. With participation, will I have an order attributed to both A and B products? All right, that’s a very good question. Well, as I was saying in the session, different models that we use have different purposes to serve. So with participation, any touch point as explained in the path that meets the criteria gets credit for the conversion. Whether it’s products, pages, channels, anything that you’re looking at. So in your example, when the user visits product A page, then later visits product B page and places an order, yes, the order would be counted for both the cases. But this is where I would like you to consider why the model is being used. Because participation is often used for influence measurement rather than direct conversion credit. So, for example, most of the time when I, I mean, I use participation model when I want to look at the pages, actually, or the journey of the user through which an order was placed. So it really boils down to why exactly you’re using a particular model and what the use case is. And I always think of participation like, you know, someone in a relay team. So maybe the effort by one person is more, but everybody gets a medal eventually because you’re part of the team. So I think the participation model, yes, it does give credit to everybody, but that’s the use of it because you want to know all the pages or the products or whatever dimension you’re looking at, all of them, you want to know who were involved in this particular success event. So that’s how participation works.

Yeah, participation is sometimes really confusing and it does take a long time for, for me personally, it took a long time for me to understand and understand the different models to use it in. So it’s whoever wrote that, I understand it can be confusing, but just stick with us. Promise it gets easier.

The next one looks like this is also for Purnima. Can different metrics in the same free form table have different attribution models applied? Well, yes, absolutely. So in Workspace, when I mean, you can apply different attribution models to any metric that you’ve put. So let’s say that you’re analyzing orders and revenue, for example. So you can keep orders, let’s say as last touch attribution while you can set revenue to first touch. Now, why would you do that? So this lets you see in the same table which channels close the most orders versus which channels first bought in the high value customers. So the conclusion is that this flexibility allows you to see the journey as a whole and answer not just one, but several questions together. So just to answer your question directly, yes, different metrics in the same table can get different attribution models.

Okay, now that’s really good to know.

It really depends on the situation, right? It’s just once you get into it and then have to choose between one or the other, it also depends on what industry you’re in as well. What are the ultimate goals for your company, for what you’re trying to answer? It can even change within different departments looking for different answers. So absolutely agree with you on that. Personally, working in different industries over the last nine, nine and a half years, I can absolutely attest to that.

Exactly.

Okay, Damon, you have the next question. What is the difference between channel-based segmentation that is hit-based, visit-based and unique visitor-based? Yes, so you can choose how your dimensions are captured. I’ve seen cases where people will want to only capture a hit-based channel attribution because of the possibility of changing your attribution in the middle of even a single session. You could also have a cloned dimension that captures the same information, but for the whole session. That way, you know what the entry attribution is.

And then for unique visitor-based, it’s to have a longer understanding of what is the impact of that attribution across a longer period of time.

So I guess for my question, how long would you suggest that last? Like, do you want it to last forever? I know there’s certain things that you want to, but really, what makes the most sense? Yeah, that definitely. So it really depends also what your organization is, if you’re B2C versus B2B. So this is a preference of how your organization runs itself. I have seen many cases where you just want to look at entry-based attribution, and that’s it, and you want that to persist just for that session. You don’t want it to go past the session because you want to have an understanding of what’s brought somebody back at a later time. I’ve also seen cases where you can capture multiple sessions. So you’ll have a dimension that captures last time what was your attribution, including this time. So you have a string that captures kind of a history of what your attribution was. So you get an understanding of how that changes over time. But to answer your question, it’s a preferential thing. So for B2C, and if you’re trying to see what conversion looks like in one session, then as an entry-based, and you’re only wanting to capture what your attribution is for that session and how you started it. But for B2B, I can totally see a case where you may want that to change every hit, right? Because you can leave the site come back as a different attribution. Yeah, great. Okay, the next one is for Poornima. How do static and dynamic dropdowns handle deleted dimension values? Okay, that’s a good question. So to answer directly, when we are dealing with static dropdowns, it keeps all the values that you originally selected, even if those values no longer exist, because it is static in nature. But when we are talking about dynamic dropdowns, right, so it automatically updates to only show the current values as long as you refresh your dashboard. So let’s say that if a value is deleted, or it no longer occurs, it basically disappears from the list once you refresh the report. So you can use static when you need historical references, but dynamic is most of the time, you know, my way to go when you want to see the latest values or updates in the data. Okay, great.

And Damon, back to you. Are these attribution models only looking at marketing channel attribution, or can it measure attribution of the different points in the journey on the website to a given metric? So, no, they don’t have to be marketing channel attribution.

Only so if I’m understanding the question correctly, you, you can use this attribution and let it perpetuate through the entire funnel. In fact, if you’re doing a good attribution plan, if you have a good attribution plan in place, you will have a good understanding of pre web web and post web performance. And ensuring that your attribution persists through your conversion points will allow you to see, you know, your backend funnel data.

So a good example of this is if you’re just looking at web and attribution associated with web, you would have a good understanding of your traffic to let’s say form completion conversion rate, or if you’re B2C your cart conversion rate. But if you add, if this attribution persists, you’ll get a good understanding of what your traffic impact is to let’s say MQL or pipe or closed one. So it definitely impacts multiple metrics.

Right. That’s a great answer.

Okay, one more for Purnima here. Is there a way to freeze conditional formatting thresholds so that they don’t change when data is added later? Yes, you can do that. Because by default, conditional formatting in Workspace recalculates thresholds as your data changes, obviously. So as in when you put new metrics, let’s say next to each other, you will get the auto generated thresholds automatically. But if you want to freeze them, by which I mean, you don’t want it to change, you can have custom thresholds set up manually like we saw in the session. So for example, if you want to highlight cells of green, when let’s say visits are greater than 1000. So setting that number manually will ensure that it won’t shift to say, I don’t know 1500 later, just because your data set grew. So yeah, using custom threshold is the way to go to ensure that your, you know, values don’t change, or the threshold doesn’t change.

Okay, that’s great. So we have time for one more question. And Damon, this one is for you. What are some quick helpful displays using channel attribution that I could quickly create and show my leadership? Yes, so in addition to the examples, where you can see a trended view of conversion rates, or even just, you know, metrics like visits, form completions, those are in and of themselves very valuable. You could also use channel attribution to look at flowcharts as well. And flowcharts will help you understand journey analysis through the lens of those specific audiences.

And I think we’ve discussed flowcharts a few times today. But don’t forget that you can also right click on those flowcharts and look at the segmentation. So you could create segments for each of those different flow journeys and see what you’re looking at in regards to those numbers. And that way, you could do a comparison against your audiences in those tables to the flowchart segments that are being used. And that way, you understand the difference between those numbers. And that’s very helpful when you’re looking at your different audiences. You could see that perhaps organic has a higher entry path through this route versus paid, which doesn’t have as much of a volume through that route. Okay. Well, unfortunately, that’s all the time we have for, but we did have some really great questions. And I want to thank you both for joining us today. Of course. I think this was a great experience. I mean, you know, working alongside with Damon and all these, you know, and everybody who came together to make this event possible. I think it was amazing. Once in a lifetime, you know, experience. And I look forward to doing many more. Thank you.

Yes, it was a lot of fun. Thank you for having us. Hopefully, we can come back again soon.