Tips & Tricks

Thanks for the introduction.

Hi everyone, my name is Christos and today we’re going to be walking through a sampling of some of my favorite tips and tricks across some of the core functions of Analysis Workspace for users like yourselves. A little bit about myself, I’m based in Philadelphia. I have experience with Adobe Analytics, starting as an Adobe Analytics customer, where as an analyst, I had first-hand experience working in Adobe Analytics, using the tools to answer critical business questions, to do regular reporting and generate insights, and service the needs of our broader organization. From there I worked in implementation consulting with an Adobe partner, and I had the opportunity to work with customers across various industries. I then made my way over to Adobe within the Success Services organization, where I help customers like yourselves to realize full value from their Adobe technology. A lot of what we’re going to be covering today is sourced from my first-person experiences as a business practitioner, along with the experiences that I’ve captured and gleaned from working with customers across industries. Today’s session is really going to be focused on identifying some features within Adobe Analytics Analysis Workspace that, in my experience, are a little overlooked, and talk about some use cases and tips and tricks on how you as an analyst can leverage these features in your day-to-day reporting. We’re really gearing this conversation to power users who are looking to up-level their abilities in the tool, and with the hope that you leave this session with some ideas of your own on how you can better utilize all the features available to you in Workspace, and share that with the rest of your organization. Our tips and tricks today will be spanning across three core functions that analysts are typically responsible for. Those being build, so how to explore and better understand your visitor journeys and life cycles through segmentation and advanced analysis. Next would be visualize, so how as an analyst you can tell a story and provide actionable and contextual insights that can stand alone in front of end recipients. And then lastly, share, which is really the most critical piece here, is how you as an analyst can democratize the data that you’re so familiar with to the rest of your organization. We’ll be focusing on some features in the product, including sequential segmentation, so diving into some tips for creating robust segments using the Venn operator. We’ll be looking at use cases for after and before, as well as within in the exclude and exclude. We’ll also be taking a look at cohort tables and seeing how you can weave in cohort tables into your regular reporting. Then looking how as an analyst you can create insights rich reports with contextual visualizations that can stand on their own. We’ll be talking about some custom templates, workspace curation, and lastly the Adobe Analytics mobile app. Let’s jump into our build section where we’ll start out with some more advanced segmentation techniques including sequential segmentation. First off, what exactly is sequential segmentation and what value does it provide to you? With this feature, with sequential segmentation, you’re able to uncover details about your visitor’s journey through your experience beyond your standard segmentation capabilities. This is something I recommend you layer on to your already pre-built segments or creating based off of a flow, which is something we’re going to walk through. In addition to the already complex segmentation capabilities in workspace, this capability will allow you to really get a deeper understanding of who your customers are and who your users are. This set of features is really powerful once you begin to harness its full capabilities. As we know, there’s no such thing as a happy path for a customer to follow. Users will either navigate through the site experiences in varying ways, whether they’re on an app or a mobile site. No matter what your definition of success is, there’s always numerous ways that users will navigate and accomplish goals. With sequential segmentation, you’re able to isolate users and dive deeper into sub-segments of users to pinpoint opportunities to optimize and to build a better understanding of who your customers are. Now, if you’re a bit newer to this concept, let’s start out by looking at a fallout report. What you see here is a very basic fallout report where I pulled in an all visitors starting point. Within this path, I have a step one exists, a step two exists, and then a lead exists. This is an example of a lead submission form. A great starting point is to begin here in fallout when building sequential segments. If I right click and quick call out, if you’re not right clicking everywhere in workspace, I highly recommend that you do. It will become your best friend if you’re more and more familiar with right click functionalities. As you can see here, when I right click on that final step in this flow, I can select create a segment from touch point. What this opens up is the sequential segment already preloaded into the segment builder here. Instead of creating this from scratch, I came from a flow visualization and am able to see a sequential segment pre-populated. You’ll notice that instead of your standard segmentation operators of and and or, you see the then operator. This is implying that one qualification criteria occurs followed by another. With that right click on the funnel, the segment builder will pre-populate these events. This is just a good way to get started in sequential segmentation. You’ll see that much like any other segment, you can look at this data on various levels, including your visitor or your visit level. One call out here is that with sequential segments, these won’t necessarily return any data on the hit level. I recommend sticking to visitor or visit. Just a reminder as to why the hit inclusion will not return any data is that with a hit, you’re limiting your segment data to a specific single image request, which the user will qualify under this criteria. In this case, if we were to select hit, it would require all of these events to occur at the same time, which in this case, we have events that are happening across separate steps or separate pages. If I wanted to see this sequence across sessions, I would select visitor here. If I wanted to see these steps in one session, I would select visit. Really depending on the business question that you’re trying to answer, create these segments on either a visitor or on a visit level. Of course, things like your unique customer or user journey for your site or apps will impact this. Perhaps if you’re selling a good or a service that typically takes place over multiple visits, a visitor container would make more sense. Or maybe if you’re interested in zeroing in on a specific sequence that happens in one session, check out the visit inclusion criteria for this sequential segment. An important call out here is that over on the top right, by default, it will be set as include everyone. This is going to introduce our use cases for this section. With this include everyone, it will qualify all visitors who have touched step one, then step two, then submitted a lead. It will include all of their actions before, during, and as well as after this sequence of events. Now, while that’s interesting, you can really kind of kick it up a notch and further filter this sequence by using the only before or the only after sequence. So let’s take a look at some use cases for that. So with our only after sequence, say for instance, you’re a software vendor and you’re interested in seeing what visitors are doing after they viewed a landing page, then an informational video per se. You can set up a sequential segment to analyze that specific group of visitors. Perhaps you can look at how many of those visitors are coming back and signing up for a demo or you’d pull in those key checkpoints, create that segment similar to what we just walked through and select only after. And suddenly you have a new subset of your visitors who you know are educated on the value prop of your software. And you can take that and be potentially more aggressive with your targeting for that user base. Next, another fun example here using the only before sequence would be analyzing search terms for visitors before an important milestone. And this is a really, to me, it’s a very exciting opportunity that you can use this feature for. So if you are an electronics retailer and you want to know what site searches are most common for users who engage with, say the comparison tool, then order in the same visit, you would set up that segment looking at comparison tool as a checkpoint, then an order. And with that only before capability turned on, you’ve got a brand new perspective of what this high value segment is searching for. So you can use that segment, bring it into a internal search term report and bring that into the forefront of, okay, maybe there are things within that subset of your internal search term report to better understand what your visitors are looking for. The insights you’re deriving from this segment will help better understand the types of activities that often result in downstream success events. Additionally, you can also leverage the within feature. And I’m going to flip back to the UI here to show this to you, because we were talking about the inclusion here on the right, but with the within feature, it’s a really handy tool that helps answer business questions from potentially say your VP of merchandising who wants to know how many visitors are submitting a lead, then downloading a white paper within a week. With this capability, you would click this clock icon here and you can select within a week and setting a within dimension clause between rules allows you to restrict data to sequences where that clause is satisfied within that time period. So this is a really handy way. Obviously beyond our time dimensions here, you can even include things like page views or other custom dimensions that are met before the next sequence of a checkpoint is completed. So another really handy example of using sequential segmentation, but kind of taking it to a step further to answer some of these more specific business questions that could come from stakeholders within your organization. Then lastly, excluding between checkpoints. So this is another really interesting example of leveraging the sequential capabilities of the segment builder where potentially you would want to see how many users are visiting a landing page and not a product page, but going straight to a search. That could be an interesting way to say, well, maybe there’s something about that product page or the navigation to finding products that is confusing customers and is potentially misleading or confusing for end users there. So the exclusion between checkpoints is something that you’d be able to do within the same UI here and it would require you to create a container. And within that container, you would have the ability to exclude and create a new, more specific version of that segment that has a rule in place that excludes a checkpoint from ever happening. I’m going to walk through that really quick. If I were to add a container here and let’s say step two is what we were going to be excluding. I am going to pull out leads and I would move over here and select exclude and you’ll see this red exclusion bar appear here. And what this is saying is that this visitor group or this segment is moving from step one not to step two. Somehow they are getting to a lead submission without step two. Maybe there is a way that users are getting to that step without fully following the intended path of the user journey. These are some of the many capabilities that you have at your fingertips with sequential segmentation and using these capabilities, you’re able to begin building a stronger persona and help inform strategy going forward for your most valuable visitors. Let’s take a peek at the next capability in the build section, cohort analysis. This is one of the more complex but powerful features in Analysis Workspace and we’re going to be diving into how you can fold this into your regular reporting or for ad hoc requests if you’re not already. First off, what exactly are cohort tables and how can I read these charts? Cohort tables are a form of behavioral analytics that breaks people into related groups for analysis. So these groups or cohorts are sharing common characteristics or a defined time span. They’re particularly helpful when you want to see patterns across a life cycle of the user. So instead of slicing and dicing your customers blindly without the notion of time, cohort tables allow you to build a deeper understanding of how groups of users engage with your experience, allowing you to identify inflection points that are really influential to the customer’s journey. Then using this info, you can respond accordingly. So if you’ve poked around on the tool before, you’re probably familiar with the most common feature in cohort tables, that being the retention type of cohort table. We’re also able to do a churn analysis. What we’re going to talk about today is the advanced setting here of a custom dimension cohort. So many customers want to analyze their cohorts by something other than just time. And that’s where this feature of custom dimension cohorts give you the flexibility to build cohorts based off of dimensions of your liking. For instance, you can use cohort custom dimension retention for analyzing app version adoption. So using that custom dimension cohort, you can compare app versions side by side to see which customers on which app version are worthy for targeting and re-engagement to upgrade their version. Campaign stickiness is another use case that is a great usage of the custom dimension functionality. Here you can see I have our dimension selected as last touch channel. Another call out I wanted to show within a cohort table that is a really powerful feature is the ability to right click and to create a segment from that cell. And what that does, it pulls in all the logic from the cohort table so that you don’t have to create this crazy segment here. And if you want to action upon this or leverage this for any future analysis, you can save this and use it in the future. So in addition to custom dimensions, within cohort analysis you’re also able to look at latency tables. And this is a great way to do pre-post analysis. For instance, if you are launching a new product page or a new product in general, you’re able to leverage this feature to see what impact this feature has on your post launch behavior. And if you want to pull in a metric like revenue or orders, ultimately this is going to give you a good view into how that change impacts your customer’s journey. So in summary, our build section here, sequential segmentation is going to allow you to expand upon your existing segmentation and go deeper into the analysis for potential targeting opportunities and a more robust understanding of who your customers are. Cohort tables are going to allow you to build a deeper understanding of how groups of users engage with your experience, allowing you to identify those inflection points that are influential to your customer’s journey. So let’s take a look at some of the quick comparison functionalities built into Workspace. Very commonly, analysts get asked questions around how the data that is being reported is doing relative to historical performance. It’s an all too common question that gets asked in performance meetings or even in a report that gets sent out to users within the organization. Just saying this is kind of bringing me back to instances where that exact question gets asked. This is great. These numbers are fantastic, but what does it actually mean? How does this compare to where we were potentially last week or last year? And I’m going to walk through just an example of how you could do that on the fly and also within a regular project that you’re creating. So I’ll start out with a very basic metric here of orders, and I’ve got that broken out by mobile device type. Our other here is desktop. And what I’m going to do here is in this free form table, I’ll right click on the metric here. And what I’m going to do is add a time period column. And once I right click on that, I have the option to look at this data. I’ve currently got the month of August 21, and it has the ability to look at a prior month to this date range, this month last year to this date range, as well as a custom date range to this date range. That’s a lot of date ranges, but the example for a custom date range, sometimes you may have organizations that are operating under a fiscal month. You can create your own fiscal date range for that option. But in this example, what we’re interested in, say we’re in this performance meeting and somebody asks the question, okay, I see that we’ve got 1.56 million orders on desktop. How does that compare to this month last year? I can select that. And suddenly I’ve got this side by side comparison looking at August of 2020 as compared to the August of 21. Now that’s a great side by side comparison. Maybe you’d layer on a visualization here, but let’s take that a step further. I’m going to go back. I’m going to right click. And instead of adding a time period column, I’m going to compare time periods. And I’m going to do the same exact comparison. What this is going to do now is generate a new column for us to actually interpret the data that is being presented. So instead of just showing that data side by side, right clicking and comparing this date range will present a newer view of this data. It may take a little while to retrieve as we’re pulling data from previous years. As you can see here, we’re looking at our mobile device type orders year over year for the month of August. This nifty percent change column here is auto-generated when you create that right click comparison. And what this is simply doing is creating a calculated metric really of your orders this year as compared to your orders last year. And you have this pre-populated conditional formatting so you can easily answer the question, okay, that’s great. How does that compare to last year? You can easily say we’re up 2.4% as compared to last year. Mobile seeing a really significant lift year over year. This is where the next step of the context around this data and telling the story around the data that’s being presented is done in a much easier fashion using Analysis Workspace. So moving on, let’s take a look at another really great way to look at your data in a contextual format, ensuring again that your data is able to live on its own. And I keep saying that, but it’s very important that when these reports get in the hands of the end consumers, that they can look at it and they are able to intuitively look at the data and have an insight being generated. This is a little bit of a step further in this time comparison example that we’re walking through, but with this calculated metric that I’ve created of cumulative orders, this is a very common approach to understanding your performance to date. And what this is is simply pulling in a function. So as you can see down here, when you’re creating your calculated metrics, you can bring in all of these functions. In this case, I brought in cumulative and cumulative is going to create a cumulative sum of all of my orders for the time period that I am limiting this to. So I pull in cumulative orders. This is great. I’m going to right click here and add a time period column. So I’ll want to look at this month last year to this date range. And suddenly I have this very nice looking trend line to see, okay, how are we comparing to our previous year? Where is potentially, is there a gap from our performance year over year? This is something that could live in some sort of a regular, whether it’s a weekly report or something that is actively being checked to see how your performance in this case orders, but it can be any other metric that makes sense for this use case comparing again your data today and this month to previous time periods. Another feature that I strongly suggest you incorporate in your daily reporting needs is the rich text editor. A great way to provide that context and to ensure that these reports can stand alone on their own is by adding a text box to your reports. So I’m going to just walk through really quickly. This is a very powerful way to ensure that your end recipient is reading the data correctly and interpreting the data and the KPIs correctly. So oftentimes there are many ways to interpret numbers and when a product owner or a high level executive may see this report, they may be overwhelmed potentially by the data that they’re seeing. They may ask questions around how is conversion rate calculated or give me more details around what you mean by cumulative orders in this example. These types of questions are all too common and ultimately the goal here in creating rich visualizations and rich accompanying text is to ensure that there isn’t a barrier to entry into understanding the data that’s being presented. So with this ability you can do things like adding disclaimers like cumulative is the sum of orders to date. Another very common way to leverage this capability is potentially if we are setting up a new page and we want to incorporate even a screenshot, we can now pull in an image URL to show this was the hero image that drove this click through. This is just another way to be leveraging the rich text editor in analysis workspace, in projects. This is great for not only these kind of regular reports that are going out to individuals, also a really fantastic way to tell a story around why data is being shown the way it is or ultimately this is your opportunity as an analyst to provide a recommendation. I think that’s kind of that last mile of all these beautiful reports that you’re creating, but being able to package up a report with your recommendation as the expert, the person who is most intimate with this data, this is your opportunity to feed that into your templates and to tell that story. So that’s kind of a quick overview of our rich text editor capabilities. Again a little bit more of a 201 level of analysis workspace and getting more bang for your buck out of these reports that are going out to your stakeholders. Another great tip I love is creating custom templates. So as an analyst I’m regularly finding myself doing deep dive analyses in target on activities in optimization, experience targeting, and I really love using templates for analytics for target. So especially for organizations that are pushing out tests regularly or if you’re a lean analytics team and you’re looking to ramp up your ability to do analysis, we know that using workspace for your optimization reporting is a game changer. But having to create these robust A4T panels regularly can become quite cumbersome. So my tip for this section is that if you’re finding yourself adding the same filters, those breakdowns, visualizations, and text boxes, as well as images for each experience, for each template that you’re creating, the recommendation would be to come over into your template or into your project and save it as a template as an admin. So you can do that over here and see save as a template and the project will be saved under the current project name followed by the word template in parentheses. And admins can change this name by editing the template in the admin UI. In addition, to create a project from an existing template, they’re under the custom templates tab. This same idea can be applied to standard analyses that you’re doing regularly. So it’s not just for your target optimization analyses. So you’ve got these pre-populated templates that are available to you, which I highly recommend checking out if you haven’t already. And with these pre-populated templates, you can adapt them and change them to your needs and save those under new names and as custom templates as well. So the use cases for this section are to create an A4T template. So A4T project templates and optimization project templates so that your analysis can be done quickly, so that you can focus on doing that deep dive analysis and sharing those insights to the rest of your organization. In addition, another great example would be to leverage your custom templates for landing pages. So a lot of organizations are launching new landing pages regularly. With those launches come the need to have actionable reports quickly and up and running. Some organizations are launching new landing pages weekly. So if this is something that you’re creating from scratch, you should create a templatized version of a landing page project and include some of those most common breakdowns. And include the most common segments, very similar to what we were talking about in the A4T example. But again here, this is really the opportunity for you to lessen the burden on creating some of these things from scratch and developing a foundation for a standardized approach to templatize reports going forward. This will save you time. This will allow you to do more interesting analysis and free up your abilities to answer questions and dive deeper. So to summarize our visualize section, ultimately the goal of a business practitioner at PowerUser in Adobe Analytics is to ensure that end recipients of your reports, of your projects, are understanding the data in an efficient and effective manner, as well as understanding the story that you are trying to tell. Not always the case where you’re able to be there to tell the story. You can tell the story in many ways. You can tell a story using the compare time period. You can tell the story with leveraging the cumulative function and using that rich text editor to explain the data and providing context and other screenshots, whatever it may be, to better encapsulate the message and recommendation coming out of that report. And then our custom templates. So this is a really great way for analysts to be efficient in the tool and spend less time on creating reports from scratch and ultimately doing that exploratory analysis and creating your A4T reports and templates for landing pages to ensure that you have more time to do the analysis that matters most. All right. Moving on to our share capabilities. So a goal of many organizations should be to democratize access to data and ultimately shifting users to be able to self-serve. Fortunately within analytics, there are many options for sharing. You can make a project available to users in your organizations with varying levels of editing control. And this is great if you want to ensure that all users have access to a project in their respective UI or alternatively you can send a shareable link, a PDF, or even schedule a report. An important layer to this option is project curation. Now the use case that we’re going to walk through here is to create a curated experience in Adobe Analytics for early adopters of Adobe Analytics. Now with curation, you’re able to limit the components, those being the dimensions, metrics, segments, date ranges, before sharing a project. So oftentimes users, especially your newer users, can get overwhelmed by the long list of EVARS, props, and events. So much so that before this feature existed, I would personally print out a list of EVARS and events that novice users should and should not touch. Fortunately, curation of projects has solved for this. So your early users of analytics, you can curate predefined sets of components and ultimately lowering that level of effort needed to get into the UI and answer questions. So in order to do this, you would hop over into the UI here and I’ve got just a standard project set up here, an acquisition funnel. Go over to your share panel here, hit curate project data, and what that does, it’ll pull in all of your pre-populated components that are already in your project. And you can also edit these here so that if there are additional components that you’d like to incorporate, that’s another way to ensure that the end user of this report is going to be able to understand what they’re seeing and not be overwhelmed with the data available to them. Next is our analytics dashboard mobile app. Now I don’t know about you, but as an analyst, my leadership team lived and breathed site performance. The sequence of events would go something like this. Our VP of digital operations gets an email alert at 8 PM that an error event is spiking going through the roof. I get a phone call asking me to figure out and understand trying to isolate the device type causing that issue. I then crack open my laptop and do a quick slice and dice, then email a screenshot of the findings. These types of requests would happen all the time. And we know that this workflow isn’t necessarily the most efficient for analysts and for those in leadership positions. Now entering the scene is the analytics dashboard’s mobile app. So with the mobile app, we’re able to create these predefined scorecards to help answer these types of questions to alleviate the stress for analysts and also get answers for our business leaders. So if we take a look at, here I have an example of a site operation scorecard that if our VP of site ops were to have that same question, they can go into their mobile app, they can pull up form errors and see which device type is driving these form errors. And then also what are the form errors? The ability to do these types of analyses on the fly really changes the game in terms of the workflow from leadership towards the analyst. So really the benefits of the mobile app go beyond site health and doing these types of one-off analyses. But regular monitoring of business performance for executives and business users quickly and easily on the go is really where you can leverage the analysis workspace scorecard abilities to ensure that your analytics data is answering questions at the right time. We’ve seen a lot of success with customers using the scorecards to enhance their existing reporting capabilities, including the scenario we just walked through of developing a mobile app scorecard on operations performance to cover things like error trends, breakdowns for your error types and app versions and device types. So that would be our first use case example here for the mobile app. Other great use cases that we’ve seen customers adopt the mobile scorecard for include a special event scorecard. So when there is a major event that is going to impact your site data, think Black Friday or the Super Bowl, Olympics, your team has a pulse on the performance no matter where they are. And then lastly, we have kind of more of a standard sales and funnel reporting, which is probably the most common starting place for our customers who are using the Adobe Analytics mobile app. So identifying your most important metrics and allowing all of your users ease of access to that data and allowing them to see the data, to interpret it in an intuitive way and respond quickly to changing demands with whether that’s relevant merchandising and sales tactics or test ideas. Of course, these are great starting points, but I highly encourage you to expand upon these for your organization. Create these mobile app scorecards for your key stakeholders so that they can intuitively glean insights no matter where they are. So much like any standard project, upfront you’ll want to know who your audience is as in who the consumer of this mobile app scorecard is, what that scorecard is going to accomplish, what is its purpose, what are those KPIs that are going to be curated and are going to be presented to end users, and then ultimately what are those breakdowns and key filters that you’ll want to have pre-populated in the scorecard. All things you want to think about when building out your scorecard. So ultimately, as you can see, we have a lot of really exciting use cases for the Adobe Analytics mobile app. And if you’re not currently using this feature, I highly recommend that you begin exploring it. And it’s actually interesting. We’ve seen a lot of users who wouldn’t have traditionally logged into Analytics now taking a new interest in Analytics data because the mobile app has lowered the barrier of entry for them to have visibility into data and metrics that in the past were just a little bit more difficult to have access to. So let’s summarize our share section here. Ultimately, with the ability to share, you’re driving data democratization and allowing yourself to free up time to do more deep dive analysis. And with curation, you’re ultimately giving your end users an experience that is more tailored to them and requires them to think a little less when building out new analyses. Creating a low level of effort for those end users and allowing them to self-serve gives more time to analysts to do that deep dive analysis. Then the Adobe Analytics Dashboard’s mobile app is a fantastic way for you to create a high level dashboard of your overall experience performance, whether that’s in the form of a business operations dashboard, your performance yesterday versus the previous week, all really great ways for you to monitor your business critical metrics at any time. I know we covered a lot of content today, and I’m excited to dive deeper and answer some of your questions. All right. Thank you so much, Christos. That was some great content, tons of focus on all sorts of really actionable tips, tricks. I love mentioning the Dashboard’s mobile app, which just a few weeks ago won the Quanti at the DAA1 conference for best new technology. And it looks like we’ve had a ton of questions come through on the chat pod. So thank you for asking and continue to ask those questions as we answer them. Unfortunately, Christos wasn’t able to join us this morning for a live Q&A. However, I have a great, great guest with me. I’m thrilled to have Andy Powers here to help answer our questions. Andy, it is great to have you here, man. I hope you’re excited for some fun. Why don’t you go ahead and introduce yourself, please? Sure. And I’ll try to fill Christos’ shoes the best I can. My name is Andy Powers. I’ve been with Omniture and then Adobe for 14 plus years. And my specialty has always been focused on analytics, data interpretation, storytelling, and all of that. So glad to be here, Eric. Love it. I love it. All right. So you love data storytelling. So tell me your favorite type of chart. A moving average line chart. Oh, OK. OK. All right. If you said donut chart, we’d have to kick you off the show. That would be appropriate. Great. All right. So before we jump in, one more reminder. Head on over to the chat bar on the right side of your screens to ask questions. But we already have a ton. So let’s jump in. What do you think, Andy? You ready to go? I’m ready. Cool. All right. The very first question comes from Elena, who says she just wants to double check. Here’s the question for you. In segments, when we select within one visit, does this mean on the next visit we expected X to happen, or does it represent something different? So maybe you can give us a feel for how exactly that within one visit works there, Andy. Sure. So it’s talking, since we’re talking sequentially, we’re saying the farthest out data point that will match here is anything up to and including the last hit of the next visit. So saying within one is saying from my checkpoint here, anywhere up through the end of the next visit, I can match that next checkpoint. Great. Great. Cool. Yeah. I think there’s tons of great use cases for that within concept. Within a visit, you want to have a purchase or a conversion or even a micro conversion along the way. You want someone to add a product to cart, for example. So some really great use cases for within one visit there. All right. Great question. Thank you. And then we have a question which kind of expands on that a little bit and is kind of related. She asks, does then stand for immediately after or at any point after? It’s kind of, it reminds me of like the fallout diagram where you have the eventual or next hit. So maybe you can give us a feel for that one, Andy. Sure. Yeah, glad to. So then on its own is just saying at any point after. But like you said, in fallout, you can have a default to choose immediately after and you could build the same sort of thing in a segment by saying then within one hit, that would be the immediate next step. So depends on the question you want to ask. If I want to know that the next thing I tracked for someone was viewing this page, taking this particular step in the journey, then you might want to modify it with within or again, look at it and fallout like you suggested. But otherwise we’re just talking within your container, your visit, your visitor container anywhere after the initial hit. Yeah. Yeah. Like I’ve always kind of found fallout to be a great way, not only for analysis, but to like actually learn unique ways to build new powerful segments. I like to do the same thing with cohort tables as well, which Christo has talked about. Like try creating segments and see what happens. And then what’s great is it pops up the segment definition for you and you can dig through and say, OK, well, how how is Adobe creating these segments and how can I learn and expand my my toolbox of segment advanced functionality? Right. Yeah, absolutely. The visualizations are really doing that hard work for you, but you can right click, dig into the segment and customize it if you need. Oh, yeah. You just said my two favorite words. Right click right there, Andy. Awesome. All right. So next question comes from Rohit. And this is a good question that we get often from from everyone, from interns to business users to executives and everyone in between. So the question that Rohit asked is when you refer to search terms and this is sort of a specific question based on what Christos is showing. But I think we can expand on a little bit. Are you referring to external search terms or internal? External, Rohit says, are typically masked or throttled by search engines. So maybe you can maybe you can shed some light there for us, Andy. Sure. Yeah. And then feel free to follow up. External search terms are generally not available to any analytics platform just with the way technology is progressed. That’s typically a suppressed field from, say, a search on Google. If someone lands on your site through an organic click, you’re not going to have that data point available. You might also be talking about search terms in the context of studying what people are typing in a search module within your site. So in that case, I’ve often heard them referred to as internal search term versus external search term. But it is worth noting, too, that paid external search terms, paid information in general via tracking code and other methods is something typically that you can analyze more deeply. Yeah. Yeah. And that paid search information can be accessed relatively painlessly these days using the advertising analytics for paid search integration. So it’s really I’m not sure if you’ve played with it or had the chance to set it up, Andy, but it’s pretty painless. You kind of like log into your search engines through the Adobe Analytics admin console. And with a few minutes later, the API connections are set up. And I think you basically get a nightly feed of paid search data for impressions and clicks and the terms, the ad group, campaign, all of that fun stuff that you would generate from your paid search campaigns and advertisements. On the other side, you mentioned internal search terms. And that’s one I love talking about. I think we could have a three hour webinar just on analyzing internal search terms. There’s no other time where in your digital experience, web, mobile app, what have you, that your visitors are telling you exactly what they’re looking for. And Andy, I think you had some pretty cool ways to analyze that data beyond just simply what are the terms and how many search results are being applied. But I think you had some pretty cool segment ideas for that internal search term analysis. Internal search terms are a great fit for sequential segmentation. So you can use the operators like only after, only before in a sequential segment. We didn’t touch on them a ton in Christos’ presentation, but those will let you say, for example, I want to know about the behaviors of this audience before they searched for a particular term or group of terms and use that to understand kind of what was their experience, what did they achieve or not achieve that led them up to that search. And you could do the same thing using only after features to look at a use case where you’re saying, tell me about the perspective of users who conduct a search or search for a particular term or set of terms and say through their next three visits, what is the behavior, what is their customer journey, what conversions did they accomplish during that time? It’s really cool opportunities, like you said. Yeah, yeah. I love that use case for that functionality of only before or only after and tying that in with internal search. What a great way to identify, are you providing a good experience to your customers or is it impossible to find what customers are looking for? And so they’re resorting back to internal search. That’s great. And one last comment, it’s the difference between saying, tell me all about the visitor, all about the visit and their behaviors versus tell me about this. This is the part I want to know about. That’s really important for analysis. Yeah. You know what? That actually is a perfect segue to our next question. So our next question comes from Kevin who asked, and I’m going to read it verbatim and then just kind of like reframe it just to make sure that you and I are on the same page here. So Kevin asks, if a user has the sequence of page A, then page B and a visit, and this is my condition in the sequential segment using a visitor container, does this appear in this segment using the visitor container? And so the way that I’m kind of thinking about this is Kevin’s really wondering is how does the visitor versus visit container affect the data that appears within a sequential segment definition? So how, like what changes within your free form tables and your data visualizations within workspace when you’re combining a sequential segment to those different types of segment containers? So visitor container is going to be all your data within that calendar window. Visit is going to be bounded by this 30-minute timeout on either side. So if you’re looking at behaviors of people within a given burst of activity or a given visit, that container is just going to match for things that they did in that period of activity. If you do it at a visitor container level, it’s going to let you answer questions like tell me whether it was in one visit or 20 visits over time, what are the steps that my customers went through? And in case two, the question was talking about what it’s like if say I have a visit container saying I did this and then this, and it’s bounded in a visitor container, just like any non-sequential segment, the top-level container is going to determine the data that’s being analyzed, so you get that whole visitor data set. Awesome. Awesome. That’s great. Yeah. The subtle differences between those containers are incredibly powerful, but really important to be able to comprehend. And again, it’s a great opportunity. Whenever I create a complex segment, I love to actually just check it before I even apply it to my analysis. Pull that segment into a fallout or pull it into a flow or pull it into just simply a page table so that I can confirm like, did I actually build this? Is this doing what I thought it was doing? And it makes a big difference. I feel like it’s a requirement, especially when you’re answering more complex use cases. Yep. Yeah. The favorite things that analysts love talking about are QA-ing and documenting. So you know, if I’ve got two, finally I have two post-its to put on my desk, those are the two reminders that I always have for myself. All right. Next question comes from Marco and Marco says hi to all. Hi Marco. Thanks for the question. He asks, is it possible to set a dynamic date range based on the date within a dashboard? For example, the prior month before a date set in a report? So Andy, I’ll kick it over to you for a quick answer there and then maybe I can help expand on that. So I think the idea is I have a report and I want to see prior month to whatever is the calendar selection I have in a given panel and workspace. No, you can’t do it there. Anything that’s like a prior month, last month, two months ago, it’s always going to be based on now at the time of the report being loaded. However, you can do it in report builder because report builder offers you the capability to change the sort of now current timeframe reference point. So that’s an option that lets you get at the same goal. Yeah, I think that’s a really good call out there. Andy is, is you’ve got some nice flexibility in terms of defining your dates using all of those super powered Excel functions and definitely plan on utilizing report builder for those types of use cases to Marco’s specific question within workspace. That is something that our product team is looking into is how can we enable customers to be a little more flexible with how they’re defining those dynamic date ranges, both within you know, the calendar and date range in the top right, as well as within the date ranges and timeframes that you’re applying to a specific metric within like a freeform table or something like that. So it’s absolutely something that we recognize there’s an opportunity for improvement for, but for now I think report builder is a great, a great temporary solution for you. All right. Let’s see. The next question, let’s see if we can find a good one here. But we’ll go with, we’ll go with this first one here. So Bilge asks, how do you filter the metrics? For example, I want to see the number of page URLs with over a thousand page views. What’s the best way to do this based on your experience, Andy? Sure. And I know we’re close to time, so I’ll try to be quick. High level, there’s no easy kind of turnkey way to do that. You can make a segment, but it’s always going to be based on a visit, visitor data. So you have to explore some other options. That’s probably the quickest answer I can give. Yeah. Yeah. We’ve, I’ve seen customers toy around with, you know, if functionality and then dragging in greater than and playing around with that, and then the else would basically be zero and then you could sort in that way. It’s a little clunky and not perfect. Segments really doesn’t quite give you, as you mentioned, Andy, segments focus on the user behavior rather than the aggregate analysis. So I think, you know, a combination of perhaps sorting and then if greater than can be helpful for you. Yes, that’s the closest match that I’ve usually seen. Yeah. And then, then of course, you know, we’ve always got our friend report builder to help us out there as well. So you could toy around in Excel. Great. Well, thank you, Andy. Thank you so much for joining me. It was great chatting with you. And also thank you again to Christos Butzakis for for his great session. I really loved the focus on the dashboards mobile app, the sequential segments, all of that great stuff is like true power user capabilities. So don’t forget to take advantage of them.