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 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. 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. And 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. So 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 and 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. 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 would 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 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’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 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? 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 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. You’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 gives 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 use 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. 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. 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. Instead of just showing that data side by side, right clicking and comparing this date range will present a new review 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. What this is simply doing is creating a calculated metric really of your orders this year as compared to your orders last year. 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 is 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. 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. 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. What this is is simply pulling in a function. 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. I pull in cumulative orders. This is great. I’m going to right click here and add a time period column. I want to look at this month last year to this date range.

Suddenly I have this very nice looking trend line to see how are we comparing to our previous year and 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. 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. Oftentimes there are many ways to interpret numbers.

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.

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 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 for 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. And the mobile apps 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. 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 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.

Welcome, Christos, to our live Q&A, and thank you for sharing your experience and insights. Personally, I really like the cumulative view. I can definitely see this view being useful for my customers from a historical look back point of view in relation specifically to obviously COVID and changing behaviors with customers. I definitely know this capability has a special place in your heart. Do you have any further little insights and tips that you’d like to offer from that cumulative view? Yeah, absolutely. Thank you.

The cumulative view, I think it transforms the way we think about comparative views. I think commonly amongst a lot of customers, we see the comparative cumulative view from a week-over-week standpoint. I know in my personal experience, we had a trended view down to the hour of how are we pacing today versus this same exact hour last week and being able to see how that looks from a week-over-week standpoint.

To your point around COVID and how much that has impacted many industries, particularly retail and travel hospitality and whatnot, with that cumulative view, we can take a look at, okay, how are we pacing month to date for this particular metric in 2021? We look back and see how that even compares to previous years in one consolidated view.

Being able to pinpoint if there are drops in week-over-week, month-over-month, year-over-year, and being able to do further breakdowns really allows you to isolate and understand how you’re tracking towards your goals. I know a lot of organizations work towards a budgeted number of conversions per month and per year.

That ability within Adobe Analytics to then dive a little bit deeper and peel back the onion a bit and see what could be contributing to various downturns, but also upturns in your overall performance. I really like that feature. If you’re not using cumulative, I definitely recommend you bring that into the fold.

Yeah, it’s definitely a good tip for obviously every single industry as all customer behaviors at the moment have been extremely different since 2019. It’s good to have that look back, but also see what changes have actually kept strong post-pandemic from a positive view. It’s a great tip that everyone can obviously start to implement and run. Seeing you do that amazing demo firsthand has been great for me. I’ll definitely be using that one as well. But we have some great questions coming through everyone, so please keep them coming. I will kick off with a first one from Lauren. Lauren, thank you for sending this through. Lauren’s asked you, Christos, can you actually add imagery within the text box? Yes, yes. So the answer is yes with a caveat.

So in order to add an image to that text box, it needs to be an image URL that is publicly accessible. So the format of that image can be PNG, can be a JPEG or a GIF, but it needs to be publicly accessible. Unfortunately, Adobe Analytics does not yet support the drop-in of locally hosted photos and images within that text box view. So right now, it requires that public image URL to be able to be surfaced. But I think that’s kind of the first step, and hopefully in the near future, we’ll have something to support an image that is as local. So thank you. Thank you, Lauren, for sending that one through. As you know, we always like to have feedback. So any questions like that of something that you’d like to see in the product, these are the places to be doing it. So always make sure that you’re sending us through recommendations of how we can improve the product in the way that you would like to be using it day to day. So we do have them coming in hot and fast. So the next one is from Sanitha. Sanitha has asked, can I report the average page depth overall? So for example, equivalent page depth metric monthly. Yes, yes. That’s a great call out. So and a great question. The answer is yes, you can. You can look at… There should be actually be an out of the box metric, if not a pretty standard calculated metric for average page depth, which is basically taking the average number of page views per visit, if I’m understanding the question correctly, that over a period of time. And this can be particularly interesting if you’re layering on segmentation. So if there’s a particular user type that is averaging a longer or a further page depth, maybe page depth is a good indicator if you’re a media site, but perhaps if there is a landing page and there’s a lot of navigation and hopping around to different pages, perhaps that page depth metric showing you the fact that you’ve got individuals kind of navigating all over the site. So, but yes, to answer that question quickly, we can look at a paycheck metric in Adobe Analytics.

Perfect. Thank you for clarifying. And Andy has come through and asked a question outside of the cohort table, how else do you visualize cohorts over time? Great question. So, yeah, we looked at that cohort table earlier and saw that the primary view is to look at the cohort table and have either your dimensions over top across the table itself or doing that over time. You can, one thing that I think a lot of customers don’t recognize or realize is that that cohort table functions very much like any other table within Adobe Analytics. So you can actually layer on a, if you’re looking at a cohort table over time, say we’re looking at a browser by month and we can see our cohorts, each of our browsers being a cohort, trended over time, we can layer on a line chart and see that much like you would trend out any other dimension and metric view within Analysis Workspace, which I think is a cool way to compare your cohorts one against another, you know, that the table does a really good job at highlighting the cells that you have a high retention rate for. Sometimes being able to see that in a line chart view is particularly helpful.

I think also another thing to mention, and we talked about it a bit, but being able to right click on a cell in a cohort table, it will open into that segment builder. And within that, you can save your segment and then leverage that. So say, you know, you’ve got a high value cohort segment, you can then use that and save that and use it for further analysis. You can use it for even for personalization, if that’s something that you’re looking to accomplish and really drive home for that particular cohort. So a couple ideas of how you can expand upon the standard visualization that comes with the cohort panel.

Well, as a customer success manager, I know that many of my customers have let me know that they find cohorts a little bit overwhelming. So thank you for sharing that additional insight.

We will be meeting one of my customers, the lovely Anil from Accent Group, a little bit later in the program. So Anil, I’m looking forward to working with you a little bit more and diving into cohorts a little bit more with you. But for all the other customers on the call, your customer success manager is there to help you and unlock the value that you have within your tool. So please reach out to them. And always we know that Experience League is always on. I like to say that they’re the digital version of your customer success manager. So the more time you spend in there, the more things that you’ll be able to uncover as well.

But Adam has asked a really, really good question, which I would actually like to know. He said, what analytics feature have you found the most praised any customers you have worked with over your experience, no matter how small? Oh, wow, that’s a.

That is a fantastic question, bringing back some nostalgia, I think. If it counts, Analysis Workspace is has has grown to be the place to do any analysis for the maybe the old school Adobe Analytics site catalyst users out there. The transition from ad hoc analysis, formerly known as Discover, into Workspace. You recognize that what Workspace allows you to do is is infinitely stronger and faster and more agile than what was previously available. So Analysis Workspace, I feel like that’s a little bit of a cop out answer because it is kind of the foundation of all analysis.

Honestly, I think I think the Adobe Analytics mobile app has gotten a lot of really positive praise. I you know. I think I mentioned it, you know, we we have as as an analyst, as a former analyst, that’s something that I wish I had. I wish I wish I was able to just, you know, pull up a report, do a breakdown, a visualization right on, you know, an iOS app that has, you know, I think in more recent years has been the far and above the the most praised feature and highly recommend exploring that and building out some views, even for your own consumption, just to get a good feel for for the the mobile app.

That’s great. A great question. Love that one. And another good one is from Daniel saying, Hi, Christos, which metrics do you generally recommend to measure the performance or effectiveness of content and landing pages for a template? Great. Hi, Daniel. Right back at you.

So I think a starting place, you know, in most landing page templates being able to understand first and foremost, where are customers coming from? Where are the potential customers coming from or just visitors coming from? So having having some sort of view into their referrals or the traffic channels that are driving them to that landing page. And ultimately, it’s like take a step back and think about what is that page looking to accomplish? What is it there? What what transaction and what does success look like for a visitor arriving on that page? And oftentimes it’s you know, there is a direct call to action or a lead form or something that that could be grabbing attention of of new visitors. So ensuring that you have tracking around that main.

KPI and and it really like what that what that landing page exists to do is, I think, mission critical and then having your various breakdowns, have those be be pre-populated. So if you have very common breakdowns, I know, for instance, if you’re a telco provider, you’re most commonly you’ll have a segment already in there for your subscribers or non-subscribers. Have that fully, fully created in your template and have that in your dropdowns so that you don’t have to recreate that every time mobile versus desktop. Other kind of your your your very broad segments that you’re incorporating to every analysis. So I encourage you to to have your really the building blocks of if you’re the analyst, what are the first things that you’re looking to break down any anomaly or any interesting piece of data with? So I’d start with those those three areas, but ultimately make sure that you’ve got that the main KPI, the main driver of success for that landing page available in your in your in your template.

And as I always say, it’s always making sure you’re across the different teams as well.

I’m obviously going into marketing, into data and insights. That’s the customer success. I’m always trying to connect the dots because sometimes organizations are so large. So going back to obviously the KPI, but always making sure you’ve got that holistic view and putting yourself in the customer’s shoes, as I do every day to you guys.

And last last one, because obviously we wish we could have you all day and all night. But we know that you’ve got other other things to be breaking down and making sure you’re giving us amazing presentations like today. But can you break down the difference between a visit and a visitor in a sequential segment? Yeah. So much like any other segment, you know, your sequential segment is just it’s just looking at a visitor’s behavior over a particular time period. A visitor, of course, just to kind of go back to one on one level of of analytics, is looking at your user behavior over multiple visits and a visit level is looking at that particular session. So a sequential segment can be applicable to both of those. You can look at a sequential segment over multiple visits with a unique visitor level and then a sequential segment can also be applied at the visit level, which is a helpful thing to know as you’re as you’re exploring that feature.

Awesome. Thank you. Well, thank you so much for your time, Christos, and we really appreciate your tips, your tricks, your insights and your amazing demos. So please keep them coming.