Tips & Tricks

Transcript

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. So today’s session is really going to be focused on identifying some features within Adobe Analytics and 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. So 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 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 and 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. So let’s jump into our build section where we’ll start out with some more advanced segmentation techniques, including sequential segmentation. So 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. And this set of features is really powerful once you begin to harness its full capabilities. So as we know, there’s no such thing as a happy path for a customer to follow.

And users will either navigate through the site experiences in varying ways, whether they’re on an app or a mobile site. So 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. And within this path, I have a step one exists, a step two exists, and then a lead exists. So this is an example of a lead submission form. And a great starting point is to begin here in fallout when building sequential segments.

So 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. But 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. So 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. And this is implying that one qualification criteria occurs followed by another. So with that right click on the funnel, the segment builder will pre-populate these events and this is just a good way to get started in sequential segmentation. So 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. So I recommend sticking to visitor or visit. And 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. So 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. So if I wanted to see this sequence across sessions, I would select visitor here. Or if I wanted to see these steps in one session, I would select visit. So really, depending on the business question that you’re trying to answer, create these segments on either a visitor or on a visit level.

And 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. And 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 the 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 let’s say you’re 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 an 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. And 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 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 is 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 are going to be excluding.

And 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. Next, let’s take a peek at the next capability in the build section, cohort analysis. So 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. So first off, what exactly are cohort tables and how can I read these charts? So 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 lifecycle of a 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. You’re also able to do a churn analysis. But 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. 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 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. You know, 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 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. But 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. And 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 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, OK, 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. And 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, well, 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. And 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. And 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 reports 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. 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. And 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, 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 p.m. 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 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 operations 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, up front 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 about building out new analyses. And 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 Dashboards 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 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 Christos and thank you for joining me live here during the Q&A. It’s great to have you buddy. Thank you, Eric. Good to be here. So we’ve had a number of really great questions come in through the Q&A chat pod and why don’t we just kind of crush through them and as a reminder, if you want to join us, you can join us at least through the chat pod and through the Q&A over on the right side of the screen right now. So if you have additional follow-up questions or other questions that are completely unrelated to the questions that Christos and I are talking through, don’t hesitate to ask and we’ll add them to the list. All right, so Christos, the very first question comes from Carla Mccozy and first of all, I want to apologize if I totally bungle anybody’s first or last names, but I’m going to do my best and my last name looks difficult but it is nice and easy to pronounce. It’s just Matt is off so I’ll do my best for yours. I’m going to try and do what I can. All right, so Carla asks, can you break down further the difference between visit versus visitor within a sequential segment? So Christos, take it away, man. Absolutely, yeah. So this isn’t really much different than your visit and visitor containers in any other segment really. So just a quick review here, your unique visitors is your broadest lens. So you’ll want to use this when you want to see a user’s activity across multiple visits. So in answering your business questions, you know, like how many times does this visitor come to my site in the month of September, for instance, or how many people are logging into your mobile app during that same time period. This lens for sequential segmentation will encompass the visitor’s cookie length and will give you that broader lens. Now on the visit level here, if you were to select that as your sequential segment include container, you’ll want to look at the number of sessions within a period of time. So that’s kind of the next level of granularity. You’re looking on the session, that particular session. So I think the example that I provided was, you know, depending on that the business question that you’re trying to answer, you’ll want to create the segment either on that visitor level or the visit level. And so if you’re selling a product that typically is taking place over multiple visits, maybe you want to look at it over a visitor container versus if you’re really focused in on trying to sell and analyze a user’s behavior on a specific session, use that visit container. Yeah, we’ve seen some really interesting use cases around when you want to use sequential for visit versus sequential within an entire visitor lifetime. I think there’s some great opportunity. You always want to think about, you know, not just the data side of things, but also the marketing and the advertising side of things. Do you have, for example, a direct response campaign currently going live that you want to analyze a little bit further that says, you know, within your, within X number of days or within, you know, just today or within your current visit, you know, complete this action and you’ll get X percent off in terms of purchase or your conversion or what have you. I think those are the different ways that, you know, from a use case perspective, there’s a great opportunity to be thinking about, okay, well, you know, what am I trying to analyze? Is my campaign session-based or is it, or is it a longer term visitor-based focus like Christos was talking about? And I think those are the types of, those are the types of questions you want to ask yourself before you actually jump in and just say, because then I know it’ll include everything. Great, Carla. Awesome. All right. Thank you for the question, Carla. Our next question that we have comes from Dylan Gump and Dylan asks, I’m curious if there are any resources for recreating some of those segments that Christos was walking through earlier within the raw data. And raw data to me, Christos, is like kind of a, not a double-edged sword. It’s more of like, there’s just so many ways that you can access raw data. And so for Dylan’s question, let’s assume he’s talking either data warehouse data or data feeds. And so he’s asking, how can you re- how can you recreate some of those super powered sequential segments that you were showing earlier within those raw data feeds and data warehouse? Okay. Yeah, no, that’s a great question. And one that comes up, I feel pretty often, unfortunately, sequential segments are really only meant to be used in the UI and with data feeds and data warehouse, sequential segments won’t be compatible with that. And you’ll see when you’re creating a segment, the compatible export abilities of that segments when you’re actually in the segment UI and see the compatible ways that you can use that segment. So in terms of the actual answer here and how we can actually get to leverage these sequential segments, this is really where you’ve got to step outside of the UI. And if you’re using R or Python, you can get these advanced sequential segments up and running and to be leveraged in data feeds as well as data warehouse. But if you got it in the UI, I would say use it there and try to get the most out of it in the UI before kind of going into the full data export capabilities, which are a little bit more advanced. And then one just general kind of takeaway here, I think there’s a lot of really great conversations being had in Experience League and resources. I know you asked specifically for resources here. Experience League is a fantastic place to take a look and see how other customers are leveraging these capabilities and answering these same business questions.

Yeah, yeah, totally agreed, Christos. I think we’re a little bit limited in terms of where we can apply those sequential segmentation capabilities. And that’s just simply because of the way that we’re processing those segments quite literally on the fly. However, take advantage of the resources that you do have within Experience League and those R and Python packages that are out there to try and make life a little bit easier for you if you’re messing around with data feeds. Yeah, great question, Dylan. Thank you. All right, so our next question is often, and I love answering it because it’s one of those questions that shows that customers really care about every little detail within our products. And we want to make sure that we’re providing the best to you. So this question comes from Abe Duavetti. And the question is, does other device type only imply desktop? And so if you’re dragging in, for example, the device type, mobile device type dimension into Analysis Workspace, does that imply desktop? And if yes, then why hasn’t Adobe just simply named it desktop? So it’s a good question, Christos. I don’t know if you want to take it first, and then I can add in my two cents from the product side of things. You bet. You bet. Yeah, no, this was a question that I had, I feel like, when I first started getting familiar with the dimensions that I would use and dimensions not to use, and I always used a mobile device type. And I think, correct me if I’m wrong, Eric, but the original dimension in Omniture was called mobile device type. And then it was called that back in the Omniture days. And it’s kind of just continued to be that way. So it’s just one of those things that you just got to have gotten used to and seeing other, and that meaning both desktop and desktop device, so any browser that is not a mobile phone. So yeah, I don’t know if you want to add to that. You bring up a really good point. You bring up a good point in that if we just named it desktop, then would that exclude laptops, for example? And then what about those crazy hybrid tablet plus things that Microsoft is coming out with? What do we call those? And so part of the reason, as you mentioned, is that originally the dimension was called mobile device type and sort of in the same vein as unspecified, we have, it was named other at that time. However, we didn’t want to just go into the data and change it from other to desktop or computer or laptop or something like that, because doing so could potentially actually break some of the visualizations that you have or some of the downstream data feed empowered capabilities or your segments or really all sorts of things. So we try to be a little bit hands-off whenever we can in terms of actually changing the data that you’re using all day, every day. Some things that we’ve seen customers do though is actually just create a segment that says device type equals other and name that segment as desktop or laptop or computer or something like that and then using that segment. And the super powered trick that I’ll add to that is you can also then create one for mobile, you can create one for tablet, you can create one for connected TV, for example, or whatever you want to do and add those segments as segment filter drop downs. So then, you know, whatever you’re doing your analysis, if I want to focus on desktop or I want to focus on tablet or I want to focus on mobile phone within two clicks, click click, I’ve got my drop down applied and I can go from there. Really good question. Thanks for asking, Abhay. All right, next question. I’m going to throw you a little bit of a curveball here, Christos, to make sure that all your synapses are firing. So we’ve got Kaushik Mehta who has asked a really good question. He asks, in the sequential segment that you were showing earlier, how do you ensure that a page is viewed after another within the same visit? So why don’t you take that one away, Christos? Sure, yeah. So one of the things we actually walked through was creating a within clause in between two criteria for our sequential segment. And within that within clause, forgive me for saying within twice, but you’re able to look and select the qualification for the subsequent requirement in that sequence. So if we were to create a within clause, you can actually select a page, the next page as your criteria. So you could ensure that the next page that is firing is what the segment is limiting in terms of what is being populated. So there’s a few other examples we walk through too, but some more ways to leverage that more fine-tuned, high fidelity approach to sequential segmentation based off of time, based off of other dimensions as well. Yeah, it’s great. I’m a big fan of those two sequential segment options, within as well as after, and being able to then say within a hit or within a page view or within a visit or within time. Are all great. And then as Christos mentioned, you’ve got this other section all the way at the bottom as you’re clicking around in there that says other dimensions where you can literally scroll through all the different dimensions that you have within your wheelhouse, within Workspace, to say within a page change or within a page type change or site section change or all those different things. Yeah, great question. All right. Looks like we have another question from Kaushik, which is great. Thank you, Kaushik. I really appreciate the engagement here. The question, and Christos, I’m looking forward to your answer here. How do we link images in the rich text box? Does the image always have to be online or can I just upload an image? Like what are my options here? Can you give Kaushik a quick overview of his image options, please? Yeah, for sure.

This is definitely, once again, not an uncommon question I think that’s brought up.

The ability to add the images was a major win. Also, nice cup, Eric. I like the cup. Go Eagles. The ability to add an image was a big win, I think, for our end users. But all images that are being brought into the rich text editor need to be a public image URL. But the image URL, it can be a PNG. You can pull in JPEGs. You can even pull in GIFs if you’re interested in doing that too. I think static and animated images are supported. If you really want to make things pizazzed up, you can pull things in that way. To answer the question, we do not have the ability to just host the images within an analysis workspace. I’ll leave it at that. Eric, I don’t know if you want to add anything else on that topic. Cool. The opportunity to upload images is something we continue to research from the product side of things. Some really unique ways that I’ve seen customers handle that is they actually upload the image into Adobe Target and then reference the image over in analysis workspace, which to be perfectly frank is perhaps maybe not the most seamless workflow that is created out there. However, it is a solution to the problem. Ideally, it’s a public image that you can add pretty simplistically. But if not, you’ve always got that as an option if you’re an Adobe Target subscriber is that opportunity to basically say, use the scene seven technology to drag it in and pop it in there. All right. We have a good question here from Dominique. Dominique asks, is there a way to share projects to users who do not already have Adobe Analytics accounts other than, for example, through email to like PDF or CSV or something like that? Christos, I’ll give you like 10 seconds yes or no answer here and then I can take it from there. The answer is no. That’s the quick answer. And Eric, I’ll pass it back to you as to why. Yeah. Unfortunately, today we don’t have the opportunity to share projects outside of users that already have access to Adobe Analytics. And really this is for a data privacy and control perspective. We’ve definitely had this question before and it’s something that from a product side we’ve researched a ton is trying to figure out what like the right levers are and the right ways to allow customers to share data from Adobe Analytics without actually accidentally just making this URL public, tweeting it out and everyone’s got access to, you know, an entire report suite’s worth of data. And so it’s something that we’re continuing to research, but unfortunately today, if someone needs access to Adobe Analytics, we’re going to have to give them access to Adobe Analytics. All right. Let’s see. So I’m actually going to jump over to Bruna Sejicza’s question, which I’m going to assume I just really bungled that name quite a bit. And so she asks a really great question. She wants to know how can I create these mobile app dashboards scorecards that you were showing a little earlier, Christo. So why don’t you walk Bruna through those steps? Yeah. Yeah. So right when you log in to Analysis Workspace, you’ll see a blue create project button. And within that button, you’re actually able to select whether or not you want to reference a custom template similar to what we’ve talked about. But over in the top, you’ll see a blank project or a blank mobile scorecard as your, the two kind of highlighted default blank projects that you can create. One of them being mobile scorecards. So if you open up a blank mobile scorecard, you just select it, hit create, and you’ll be presented with a blank slate. And this is where you can begin bringing in those KPIs, your predetermined segments, your filters, your date ranges that you want to have pre-baked into your particular mobile app scorecard, depending again on your business needs here. So I think we’ve covered some really good use cases in the talk earlier. And I think, again, these are all going to really depend on the needs of your business. But again, it should be pretty easily accessible. Assuming you have the correct level of access to create projects, you should be able to access that and set up a new scorecard. And then on the actual phone itself, you can log in using your credentials and your Adobe Experience Cloud credentials, and you can actually set up your access to use face ID, I believe as well. So that’s a fun, quick, easy way to get into the tool once the actual scorecard is curated. Yep. Yeah. Yeah. I’m a big fan of, you know, making it easier for folks to access data and the mobile app is a great way to do so. In fact, it was nominated as a potential winner for the DAA Qantas awards, which are taking place next month. So hopefully we’ll take home the gold there.

And, you know, a follow up question, two follow up questions, one from Natalie Pham and one from Abe Duavetti. And Natalie asks whether mobile app scorecards can include anomaly detection for error tracking. And then Abe asks whether the mobile app will work for federated users as well. And so I’ll kick over the first question about anomaly detection to you, Christos. What do you know about anomaly detection within mobile app dashboards? Yeah. So as of now, the ability to have the robust anomaly detection that you have in the desktop version of Analysis Workspace is not in the mobile app, but I do know that there are some fun releases coming in the future for the mobile app. And that you still do have the ability to look at things like a historical trend. Ultimately, that’s really what the app is there for, to have a snapshot week over week types of comparisons, and then to do your really deeper analysis in the desktop version. Yeah. Today we are not yet supporting anomaly detection within the dashboards app. However, we’ve got all sorts of new visualizations coming soon to an Adobe Analytics dashboards mobile app near you. So keep on refreshing those updates. I think they are expected to go live sometime next month. So get excited for those. The other question that came from Abe was about whether federated users have access to the dashboards mobile app. And the answer is yes. When the dashboards mobile app first came out, we were limited to just the Adobe ID login, but now everyone that can access Adobe Analytics can be provisioned to access the dashboards mobile app as well.

All right. So we’ve got time for like one, maybe two more questions before we get started with our next session. How’s that sound, Christos? Does that work for you, buddy? Yeah. It’s fun. All right. So one of the questions that came through came from Vidia, actually. So Vidia is asking whether you can export sequential segments in Adobe target for targeting, you know, in different target activities. So basically, are there any limits there in terms of sequential segments and being able to share them to the experience cloud for targeting within Adobe target or over an audience manager campaign, et cetera? So I think that might be the toughest question we have. So Christos, I’m excited to see you sweat a little bit over there, buddy. Yeah, for sure. So you should be able to push these segments into target for experience cloud purposes and to leverage the capabilities of those robust segments that you’ve created in analytics to do experience targeting to your A-B tests. And that’s kind of one of the very powerful capabilities of that and having that ability to action upon those segments. And you can kind of use it to better understand your customers, which is kind of what we focused on a lot in the conversation, but then also to take action in the form of personalization. Yeah. Super. Awesome. Yeah. Thanks, Christos. All right. Our last question that we have time for today. And if you asked a question today that we didn’t have time to get to, don’t worry. Christos and I are both within Experience League all the time. And so don’t hesitate to ask your questions there and we’ll be happy to answer them. All right. So very last question for Christos, who I think you’ll know the answer to this one, and I can’t wait to see if you do, besides who the best NFL team is right now, the number one team in the NFC East. Fly Eagles, fly. That’s what I’m talking about.

All right. So this question comes from Helena and she asks, how do you report on broken links? So what’s your preferred methodology from an implementation standpoint in terms of reporting on broken links? Christos, go. That’s a great question. I think there’s certainly the ability to do some form of error, customized error tracking on a broken link that whether it’s a 404 error, you’re able to capture the error type to do a trended view of errors. And potentially if a page is not being rendered, you can trigger that 404 error and to be capturing that into a specific error type. Curious, Eric, if you’ve got another kind of methodology there that would make sense. Yeah. I think your number one solution is going to be that error page, 404 error style solution. I’ve also seen some customers that on their site, they have link tracking as well. So on the click, they’re able to see exactly what link was clicked so that you can see the referring information as well. But really your pages not found dimension, which uses that S.error page. If you’re ancient like me and remember implementing using JavaScript, then that would be your number one thing to do. The most important thing to keep an eye on is the values that you’re passing into. I think it’s page name when error page is set is what will populate within that dimension. So in case I’m misremembering that, I do recommend checking out Experience League to check on the documentation for the pages not found dimension just to make sure that you’ve got it set correctly. However, that’s definitely the number one way to go is with those 404 errors. Awesome. All right. Well, thank you so much, Christos. Really appreciate you taking some time with me today in front of the Skill Exchange fireplace that I’ve got here. Go birds, man. Go birds. Thank you very much, Eric.