Analyzing the Data

Hello everyone, I’m Prithvi Bhutani. I’m a Principal Product Manager here at Adobe. And today I’m going to be walking you through the analyze section of the Experience Maker Skill Exchange program. My coworker, Danielle, just walked you through the analytics user interface. So we’re going to delve deeper into how to get the insights you’re looking for through analytics. We’re going to be exploring how to build a freeform table, date ranges, apply data, and use data to build your data. And then we’re going to talk about the experience maker and how to build a freeform table, date ranges, applying segments, and finally, we’ll explore some panels in Workspace as well. I’d like to kick off with a quote that accurately summarizes the value of data and insights that we’ll be talking about today. We understand that organizations depend on their data to help make the right decisions to grow and succeed. Adobe Analytics is here to help make that database decision-making easy and actionable. Before we head into Workspace, let’s spend a little time understanding three key metrics that form the foundation of a lot of analysis in Workspace. First is Visitor. A visitor is a unique user to your site for a selected time period. A visitor can have multiple visits. Visitor-based metrics in Adobe Analytics are people, users, devices, etc. Next up is Visit. A visit is a sequence of page views in a setting. A visit consists of multiple page views. Examples of visit-based metrics in your data are visits and sessions. The last metric we’ll be talking about today is Page View. A page view is counted for each server call that is sent. In the analytics world, you’ll also hear this being referred to as hits. So to sum it up, the page view metric counts every server call sent. The visit metric represents a sequence of page views in a session. And the visitor metric represents the number of unique users to your digital touchpoint for a given time period. So let’s take an example. Let’s say there’s a retail website with Adobe Analytics deployed. When I visit that website, I’m recognized as a unique visitor. And so that metric would be incremented. Now, if I were to navigate through a few pages off the website and leave, my visit would be counted as a single visit. And every page I navigated to increments the page view metric. When I visit the website the next day, the visit metric would be incremented to now count my new visit. And depending on how many pages I view, the page view metric would also increment. A visit is always tied to a time period. The most common way a session ends and a new visit is counted is if the user has been inactive for more than 30 minutes, which is what happened in the example I just shared. Now that we have conquered that, let’s move on to building a freeform table. A freeform table is one of the easiest ways to get answers to your questions. A freeform table is a flexible data table that helps you analyze, compare, and break down your data. Let’s start by creating a blank project in Analysis Workspace. Danielle has already covered the landing page, so we’re just going to dive straight into building a project. A blank project typically opens up with an empty freeform table. If you’re not starting with a blank project but would like to add a freeform table, head over to the panels option on the left and drag and drop a freeform table onto your project on the right. Here, since we started with a blank project, we already have a freeform table to build out. The first thing I recommend with building a freeform table is to set the time period for your analysis. So using the calendar, choose that first. Now we’ll start with a metric to build what’s called a time series or a trended data table, which essentially shows you the trend of the metric selected against time. I’m dragging and dropping the metric Unique Visitors from the left component rail over to the right half of my freeform table. The freeform table automatically adds in the day dimension here to complete my trended table. You can see that there are many rows based on the number of days in the time period that I chose. There are controls here to choose how many rows you want displayed at any given time. You can also leverage the pagination to move back and forth to data points. Let’s try to add another dimension. I’m going to drag and drop the page dimension here to replace day. Next, I’m going to break down a dimension item by another dimension by dragging and dropping that over. I can also break down by right-clicking on the page dimension by right-clicking on the dimension item and choosing the dimension either from a list or by searching for it. Now, I want to share two tips that will help you master the art of the freeform table. Tip one, anytime you’re analyzing data with a freeform table, think through three key requirements. The when, which is your time period, the what, which is your dimension, and the how many, which is your metric. And the second tip I have to share is that you want your metrics to form the columns of the freeform table and the dimensions to be the rows. So when building a freeform table, drag and drop metrics to the right half of the freeform table and drag and drop dimensions to the left half. Let’s do a quick exercise to practice our newly learned concept. So let’s try to build a freeform table to answer the question, which page was the most visited in the month of April? Let’s break that down. Our when is the time period, here April. The what or the dimension is pages, and the how many or the metric is visits. So putting all that together, we get this. In my dataset, the home page had the highest number of visits for the month of April. Let’s do one more exercise before moving on to the next concept. So can we find the browser with the most visits in the month of March? Here, the time is March, the dimension is browser, and the metric is visits. In my dataset, the browser with the most visits is Google Chrome 80.0. Great. That is the most fundamental of skills to learn while using Analysis Workspace. Now that we’ve mastered that, let me show you a few tips that might help. You must have noticed these drop zone guides pop up as you build out a freeform table. The guides are to help guide your actions to the right spot onto the table. When you try to add a metric to the table, the add guide will help you drop the metric to the right spot. The replace guide will show up when you try to drop a dimension over an existing dimension, like what I did when demoing how to build a freeform table. When you try to break down a dimension by another dimension, the breakdown guide will pop up. Here, I’m trying to break down the purchase thank you page by the campaign vendor. Lastly, anytime you try to filter the table by using segments, the filter by drop zone guide will be available. Here, I’m trying to filter online revenue by a segment new visitors. We’ll be talking more about applying segments later in our session. Our next section is all about date ranges. Date ranges are an incredibly important part of any analysis. Adobe Analytics provides you easy yet flexible ways to choose the time period for your analysis. The calendar in Analysis workspace helps you specify dates and date ranges or select a preset. Any calendar selection applies at the panel level, but if you have a project with multiple panels, you can apply your date range selection to all panels instead of manually changing them out. You’ll notice when you first open a project and the calendar that workspace calendar defaults to the current month and the last month. Since typically that’s the most commonly used time period, that makes it easier to quickly select the date range that you would like for your analysis. Workspace also has this great concept of rolling dates that lets you generate a dynamic report that looks forward or backward for a predefined set period of time based on when you run the report. For example, if you want to report on all revenue for last month based on the created date field and run that report in June, you’d see orders placed in May. If you ran the same report in July, you’d see orders placed in June. Date ranges are the range of dates you conduct your analysis across. Date ranges are provided by Adobe, applied in the panel calendar, or created using the date range builder. You can find pre-created date ranges in the calendar or in the left component rail. You’ll notice various time dimensions in a separate date ranges section on the component rail. You can drag these date ranges into a workspace project. You can also create your own custom date range that will show up in the calendar as well as the date range section of the component rail. Let’s learn more about those. Custom date ranges can be created and saved and used as time components in Analysis Workspace. Let’s create a custom date range by rolling time period. You can create a custom date range by navigating to the component menu or using the plus option in the left component rail. Here we’re going to create a rolling date range for two months ago. So select the date range two months ago and check the rolling date checkbox. Open up the details to ensure the conditions match what you’re looking to create and click on apply. Name it accordingly and save. The custom date range will show up on the left rail once it’s saved. You can drag and drop it onto a freeform table for your analysis like any date range or time component. Let’s talk a little bit about date comparisons. Why do you need date comparisons? Analysis requires context and often that context is provided by a previous time period. For example, the question, how much better or worse are we doing at this time last year is a fundamental to understanding your business. A frequent analysis that you may want to run is comparing a metric against itself during a different time period. Analysis workspace lets you compare year over year, quarter over quarter, month over month, week over week and any custom date range as well. The date comparison feature automatically includes a difference column which shows you the percentage change compared to a time period specified. Head on over to the freeform table. Here I have a visits by the page dimension for the month of May. Right click on the metric and you will see the option to compare time period. The three options that show up within compare time period depend on the date range selected for the panel. Once you click on that, you will notice the time comparison is generated. You’ll notice that workspace creates the date comparison, adding another column with visits and filtering that by a custom date range of the previous month. Also, a new column with the percentage change between the two metrics is auto-created. This feature could quickly help you with comparing any metric over time for your analysis. Here’s an example. If you needed to know what was the percentage change in revenue of your various products by visits between May 2021 and April 2021, you’d start with a freeform table. With the date range set to May, metric as revenue and dimension as, here I’m choosing subcategory. Right click on the revenue column and choose compare time period and this will easily answer the question you have. Adobe Analytics lets you build, manage, share and apply powerful, focused audience segments to your reports using analytics capabilities, the Adobe Experience Cloud, Adobe Target and other integrated Adobe products. In this section, we’re going to learn about how to leverage these segments in our analysis. Now, segments allow you to identify subsets of visitors based on characteristics or website interactions. Segments are designed as codified audience insights that you can build for your specific needs and then verify, edit, share with other team members or use in other Adobe products and analytics capabilities. Broadly, segments can be categorized as being four types. You can segment visitors based on attributes, so browser type, device, country, number of visits, gender, etc. You can also segment visitors based on interactions, which describes how they interact with your digital touchpoint. So which campaign, keyword search or search engine. You can segment visitors based on their entry and exit touchpoints, which define where they are entering your website from, where they land, the last page they visited before they exit your website, etc. Lastly, segments can be built around visitors based on custom variables, product ID, defined categories, customer ID, etc. You can find segments in workspace in the left component ring. They can be dragged and dropped into any panel that you may be working on in Analysis Workspace. Segments will filter the data in your tables and visualizations. Segments can also be used as dimensions to compare against metrics. And you can also break down dimensions by segments. There are two ways you could use segments to filter data. The first is to apply the segment globally. This applies to all of the data in the panel. Here I have a freeform table and I’m going to pick my segment and drop it onto the segment drop zone at the very top of the panel. As I’m adding these segments, you’ll notice that the data in the table is dynamically changing and is being filtered by the segment applied. I can add more segments here too and stack them and they will essentially be added together. Another way to apply segments is to apply them at a metric or column level. This is helpful when you’re trying to compare a metric filtered by some condition against the same metric without the filter. So let’s say I want to compare my visits against all my pages from mobile phone to visits against all my pages from a non-mobile phone. I’m going to start with my freeform table here with pages that I mention and visits as my metric. I’m going to search for a segment that I have created, visits from mobile devices, and drag and drop it over the metric column. Now I’m going to add another column of visits, search for my another segment, visits from non-mobile devices, and drag that over this new visit column. Once I do that, you’ll be able to easily compare and contrast for every page the visits from a mobile device versus visits from a non-mobile device. Another really great way to use segments is to use them as dimensions. I have a table here with visits and page views as the metric and page as the dimension. I’m going to go ahead and search for my segment and replace the page dimension with a segment. Now you’ll see that I’m able to see the views and page views for this segment. I’m going to search and add a few more so I get a better picture. I just need to search and drop it onto the table header, and it’ll append to the list. This gives me a great picture to understand the visits and page views of these different segments, all in a single view. How would we use segments to find the most popular page visited in the United States from a mobile device in July? So starting with a freeform table with the date range set to July, visits as a metric and page as a dimension. Now we’re going to stack two segments to get the answer we need. Drag and drop the segment visits from mobile devices and the segment USA, all onto the segment drop zone at the very top of the panel. That will help you find the most popular page visited in the United States from a mobile device. Let’s step it up a notch. Let’s find the month-over-month change in visits and page views from visits from search engines between June and July. So we’ll start with a freeform table with visits and page views as a metric and July as a date range. We’ll use the segment visits from search engines as the dimension here. Now we use the compare time period feature twice, once for the visits metric and once for the page view metric. This would give a great month-over-month view, visits and page views for the segment visits from search engines. We’ve talked at length about the most popular panel in Workspace, the freeform table. There are several more panels in Analysis Workspace that can help provide various insights. So we’re going to explore two such panels in today’s session. Quick Insights is the first one. Quick Insights is a panel specifically built to answer business questions quickly and is a great tool for beginners. Clicking on the panels icon on the left will open up the panels menu in the left rail. Drag and drop the Quick Insight table to the left to start using it. When you first start using Analysis Workspace, you might wonder what visualizations would be most helpful. Which dimension and metric might facilitate insight, where to drag and drop items, where to create a segment, etc. To help with this and based on your own company’s usage of data components in Analysis Workspace, Quick Insights leverages an algorithm that will present you with the most popular dimensions, metrics, segments and data ranges that your company uses. In fact, you will see these dimensions, metrics and segments tagged as popular in the drop-down list. Now Quick Insights helps you properly build a data table and an accompanying visualization in Analysis Workspace. It also helps you learn the terminology and vocabulary for basic components and pieces of Analysis Workspace. It can guide you through simple breakdowns of dimensions, add multiple metrics, compare segments easily within a freeform table. It also helps you change or try out various visualization types to find the right tool for your analysis quickly and intuitively. When you first start out, go through the short tutorial that teaches you some of the Quick Insights panel basics. Select your building blocks or components in the Quick Insights panel. Dimensions will be represented by orange, metrics in green, segments with blue and data ranges in purple. You must select at least one dimension and one metric for a table to be built automatically. You have three ways of selecting the building blocks. Drag and drop them from the left rail or click on the drop-down and search the list. You will see some of them marked as popular. This is based on your company’s usage of these dimensions. If you know what you’re looking for, start typing and Quick Insights will fill in the blanks for you. When you have added at least one dimension and one metric, the following will be created for you. First, a freeform table with the dimension. Here I’ve chosen marketing channel and the metric. Here it’s bounce rate. The segment iOS is also applied to the metric because I use that in the panel. Second, an accompanying visualization. In this case, a bar chart. The visualization that’s generated is based on the type of data you added to the table. Any time-based data such as visits per day or month defaults to a line chart. Any non-time-based data such as visits per day or month defaults to a line chart. Any non-time-based data such as visits per device defaults to a bar chart. You can change the type of visualization by clicking on the drop-down arrow next to the visualization type. We talked about how breakdowns can help drill down your analysis to specifics. Quick Insights helps make breakdowns easier. Click on the plus breakdown option below your dimension at the very top to select what to break down your dimension by. You can add up to three levels of breakdowns on dimensions to drill down to the data you really need. You can also add more metrics. You can add up to two more metrics by using the AND operator to add them to the table. You can add up to two more segments by using the AND OR OR operator to add them to the table. The second panel we’re going to explore is the attribution panel. A given customer journey isn’t linear and often unpredictable. Each customer proceeds at their own pace. Often they double back, stall, restart, or engage in other non-linear behavior. These organic actions make it difficult to know the impact of marketing efforts across the customer journey. It also hampers efforts to tie multiple channels of data together. Attribution gives analysts the ability to customize how dimension items get credit for success events. For example, a visitor to your site clicks a paid search link to one of your product pages. They add the product to the cart but do not purchase it. The next day, they see a social media post from one of their friends, click the link, then complete the purchase. Attribution gives analysts the ability to customize how dimension items get credit for a success event. The attribution panel is an easy way to build an analysis comparing various attribution models. It is a feature in Attribution IQ and gives you a dedicated workspace to use and compare attribution models. To start, click the panel icon on the left, drag the attribution panel onto your analysis workspace project. Add a metric that you want to attribute. Add any dimension to attribute against. Examples here include marketing channels or custom dimensions, such as internal promotions. Select the attribution model. The model describes the distribution of conversions to the hits in a group. So you can pick first touch or last touch based on which attribution model you’d like to use. Select a look-back window that you want to compare. The look-back window describes which groupings of hits are considered for each model. For example, visit or visitor. A look-back window is the amount of time a conversion should look back to include touch points. Attribution models that give more credit to first interactions see larger differences when viewing different look-back windows. The Attribution panel returns a rich set of data and visualizations that compare attribution for the selected dimension and metric. This table helps you compare and contrast the results of the various attribution models selected in the panel. That wraps up the Analyze section of today’s session. I hope it helped give you a sense of how easy it is to get answers to the business questions you have with Analysis Workspace. If you have any questions, keep them coming. I’m going to stick around and help answer them. Thank you, Prithvi. Welcome back, Gaurav. Hey, Richa. Glad to be back here. Great. Let’s dive into that live question box then. We’ve got the first question from Archit. He’s got a long one. He says, when we drop a dimension on another dimension entry on a panel, is there a way to expand the number of rows we can view for all dimensions at once? Currently, I have to do for each row separately. For example, if I drop a dimension page and then drop another dimension countries for each entry listed under page.

Good. Great question. I love this question for the fact that this shows that somebody is using Adobe Analytics diligently. Yes, you can do that, Archit. These are little tips and tricks for using Adobe Analytics. Of course, it’s a vast solution. The way you do this is that when you are trying to drop another dimension over the earlier dimension, all you need to do is to select all the rows together. You could do that by Ctrl-Shift or Ctrl-Down Arrow. Once you have selected all of those, then you have two options. Either you drop the next dimension on top of all the selected rows together at once and all the rows will be actually broken down into the next dimension. Or you could also, after selecting all the rows, do a right click and say break down by whatever dimension that you need. Also, I’m taking the opportunity to talk about the little tips and tricks section that you see once you log into Adobe Analytics. On the right bottom, whenever you log into Adobe Analytics, you will see a blue box that talks about tips and tricks. So when you go there, you might actually find answers to some of these questions, which are practitioners tips in terms of little nuances on using Adobe Analytics.

Thank you, Gaurav. We have a few questions from Melissa. Let me break them down. So the first one she asks is, can we even monitor social channel engagement and perform analysis on it? Sure. Great question, Melissa. Thanks. Yeah. So there are a couple of parts of when we talk about social channel engagement and performance analysis. So what Adobe Analytics does is that, you know, if you have to understand it, Adobe Analytics is a post click analysis, analytics solution. What it really means is that it tracks your journey. So, for example, if you’re talking about either running an ad or a content in your social media pages, say Facebook. Once somebody has clicked into that particular content, the journey onwards in terms of after clicking, when you reach to your website, your app, wherever, that entire journey is tracked because, you know, we place a tracking code in that journey. So as a referral, you will be able to look at where people are coming from and then optimize the performance, knowing, you know, people who came from Facebook clicked into this particular ad at a campaign level, at a creative level, and then go details in terms of what happened. So did they bounce? Did they engage with the content further? Did they go to the next pages? Did they convert? That’s one part of it. Now, when you talk about in social media engagement, channel engagement, for example, once you put a content in your social media page or Facebook page, how people engage, how many people liked and commented and things like that. Now, this is the kind of data that Adobe Analytics doesn’t track. But at the same time, we also have an integration with tools that do it. For example, tools like Sprinkler, there are Hootsuite, there are tools that track those channel engagements, and there is a native integration between Adobe Analytics and these tools. What it really means is that you should be able to track these metrics from these particular tools, again, back to the entire conversion journey. So, yes, there’s a lot that you can do in Adobe Analytics around social channel engagement and performance. Thanks, Gaurav. The other one she wants to know is how do we link the website to be monitored by the tool? Sure. So the earlier part of this, if you’re talking about, I’m assuming that the question is also about tracking social channel engagement to when people land on the website. That I kind of explained through a tracking code. In your career tips, you will have a tracking code or on content. And that tracking code enables the tracking of entire journey in terms of a funnel. Similarly, so when you talk about individual web properties, how Adobe Analytics works is that for web or for app, you would have in your implementation process, you would have a JavaScript library that runs in your site or SDK that runs in your mobile app. And that keeps on constantly tracking based on whatever data layer that you want to track around your customer’s behavior. So the tracking methodology in terms of how do you implement in your site, website or an app that connects with social media, as I said, in terms of referral links or referral URLs, because that also has a tracking code that connects with your onward journey on the site.

Great. And the third one is, are there any other operators in the Quick Insight app other than AND? Yeah, so this is really a detailed one in terms of the UI of Quick Insight. But yes, there is not just AND, I think there is definitely OR operator. And I’m assuming that the Quick Insight app is talking about sort of a segmentation that you could bring on the Quick Insights tab. So there are a couple of operators there. I can’t remember all that is there in that dropdown list. But if you do a quick search on Quick Insights in terms of documentation, I think it’s more than AND that you can see there.

Great. Yashwant wants to know, is Adobe Analytics focused on web page analysis or can it also be used for any other database analysis? Yeah, that’s a brilliant question. I love answering that. Yeah, so Adobe Analytics is definitely not just restricted to web page. We are a holistic, as we say, digital intelligence solution. What it really means is that one, it is agnostic in terms of what digital properties that you’re talking about. So you could track data from website, from apps, from smart devices, from IoT, all of that is a possibility. And all of the data can come together through a unique common ID or a unique ID in each of these data sources, and that connects to a common ID service. So you can track beyond web, right? And there’s also some flexibility in terms of getting offline data in Adobe Analytics. For example, loyalty information. So you might be looking at Adobe Analytics to look at digital behavior on a website. But at the same time, you might also connect attributes. So this is a visitor, a customer that logs in into your website. Is that customer a gold loyalty member? Right? And that kind of flexibility is there. So some data can also come in from offline sources in Adobe Analytics in order to enrich and complement what you could do around web page analysis. I hope that helps. Eshwar wants to know, how can we track how many visitors clicked on PDF download guide present on the site? Sure. Yeah. So this is again, you know, functionality that a lot of customers would need. And this is done in Adobe Analytics through what we call as download tracking or link tracking. And what it really does is that it goes beyond tracking events on a page. What it means is that in the configuration to launch, if you are using launch or app measurement code, you would have to enable download tracking. And what it really does is that it basically calls a function in the query string, leverages the query string through a function. And it basically tracks not just what are the links that are clicked, but also what are the documents. So you could pass this is custom in your implementation or the data layer in launch. You could actually define what are the elements that you want to track. So you should be able to track how many people clicked on that link, which document did they download based on what are the URL of that document. And you could also capture other parameters around that document, such as name of the document, type of the document. So it’s the basic link tracking. If you have to do a bit of search in terms of how to enable this, this really needs a very quick configuration enablement on your Adobe Analytics product application once you log in. On the admin side. Vikas has an interesting question. He says, if you’re importing the attribution data into Adobe from a third party MMP vendor, then can we still use Adobe to generate the attribution analysis? Great question. So, yeah. So see, first thing, Adobe Analytics, of course, has a progressively mature attribution analysis methodology. So it’s very full-placed when it comes to mobile app attribution, we do integrate with multiple pairs. So when we are talking about the attribution, actually, if you’re talking about tools such as apps, flyer or branch or adjust, and all of these have native integrations with Adobe Analytics, what it really means is that you could look into the attribution data that being shared by these MMP partners. And this is stitched at the user level with Adobe data. So then you can further augment in terms of understanding Adobe’s analytics attribution that is more at a channel content level. And then you can go into mobile attribution insights that’s being shared by these partners. And you could leverage these integrations to understand that end to end. Adobe, we have productized the attribution feature in Adobe Analytics. So it’s not really an open workbench where you can go and write your code to build a custom attribution model. But as much as we could in terms of a UI that could be customizable, you can change all your parameters. You can enable that attribution model that you are looking at in terms of reporting is also taking consideration of data feed that is coming from apps flyer. Another interesting question from Raja. He wants to know what are the long one, but tips and tricks on migrating Adobe on Google, migrating Google Analytics, sorry, to Adobe Analytics. Well, yeah, I love that question. Very common question we get from customers and some places there are these two technologies coexisting. There are some places where there is a migration plan. Yeah. So there are a few things that that needs to be considered. Right. One is, of course, a capability mapping. So first thing you need to do is that, well, your choice of analytics software and you have made up your mind to do Adobe Analytics. The second part is migration in terms of retaining your historical intelligence, retaining in terms of how the customer, your users are using Adobe Analytics. So for migration that needs to be also considered. In most of the scenarios, we don’t recommend saying you should be actually stitching historical data with the data that you’re going to collect forward. And the reason are the reasons are two, right? You could do that at a custom level. One, I think the challenge is that when you do that, digital stitching for your another analytical system to a new analytical system, you may not be able to match a lot of it. And that probably the ROI from that exercise is less. So typically customers would have a cutoff date and they will stop using Google Analytics. They can, of course, get the data back from Google Analytics for all the historical data. And then from a cutoff date, you should be able to extrapolate and collect data and use Adobe Analytics. Another reason for this recommendation is also that, you know, typically for digital behavior data, we are not unless a trending report. A lot of behavior analytics is pretty usable once you start using a new analytics tool because you’re looking at recency, right, in terms of digital behavior analysis. So have a cutoff date, plan what are the capabilities that you want to map across these two tools and make sure that it is holistic in terms of answering all the desired questions. So, you know, digital stitching, unless you have a very strong case about it, we don’t recommend to go for that instead just switching over to a new solution. Great. Um, Melissa again here, she wants to know how many rows can be exported into a CSV report, CBS report? Right. How many rows? I think if I’m correct, I think that limit is around 50,000 rows today, but it really is not limiting you in terms of what you can get out of Adobe Analytics, right? So CSV is just one more of it and that limit could be just based on, you know, even Excel sheets limit, for example, right? But there are multiple ways to get the data out. So you can get data out through a data warehouse in Adobe Analytics that doesn’t have, you know, this limitation. That range will be much higher, or you could actually get all the data that you wish in form of a data feed, push out to a cloud storage. So to simplify, there isn’t literally a limit in terms of how much data can you get out. If you’re just talking about CSV, I think that limit is around 50,000 rows for the fact that CSV might not be able to handle more of that data too. We have just about a minute left. Ramya wants to know if we have the option to pause or stop scheduled reports in Workspace. Yes, we have. So the whole report management and Workspace project sharing management is basically the scheduling part is based on, of course, start and end time. So if you can go to your Workspace, you know, report management or the project management, as we call it, therefore each of those reports, you should be able to run the sharing. You should be able to control what is the start time and what is the end time for sharing. So, yes, it can be customized. And also for the scheduled reports outside of Workspace, you have all the flexibility in terms of how do you want to define deliverability, right, in terms of what is the frequency, when you want to start, when you want to really stop that. We’ll take the last one. Can we integrate analytics with third-party tools or like chatbots? Yeah, good question. So, yeah, as I said, right, you could, there are two ways of it. One, I think we have a lot of native integrations with tools. As I said, Adobe Analytics is an open integration, open ecosystem. So what it really means is that if you go to exchange.adobe.com, you will see that there are a lot of third-party tools that we automatically integrate with. There are integrations that only exist. If that integration doesn’t exist, you have flexibility to get data in out through APIs as well across these third-party tools. So, yeah, I mean Adobe Analytics is a great tool for third-party ecosystem integrations. Thank you, Gaurav. That’s all the time we have for chapter two, but we’ll have you back at chapter three. Thank you, Richa, and thanks everyone for the great questions.